Economic Change and Restructring (2005) 38:11–35 DOI 10.1007/s10644-005-4521-7
Springer 2006
The Determinants of Capital Structure: Evidence from Chinese Listed Companies JIAN CHEN1,* and ROGER STRANGE2 1
Business School, the University of Greenwich, SE1 9NH, London, Greenwich, UK; 2Department of Management, King’s College London, 150 Stamford Street, SE1 9NN, London, (*Corresponding Author: E-mail:
[email protected]) Abstract. This paper attempts to investigate the determinants of the capital structure of a sample of 972 listed companies on the Shanghai Stock Exchange and Shenzhen Stock Exchange in China in 2003. Various theories, namely, the trade-off, pecking order and agency theories, are deployed to explain and predict the signs and significance of each factor identified by Ragan and Zingales (1995) and Booth et al. (2001). Furthermore, we include institutional shareholdings, including state agency shareholdings, state-owned shareholdings and privately owned shareholdings, as corporate governance variables to examine the effects of corporate structure on the debt financing behaviours. As well documented, we find that profitability is negatively related to capital structure at a highly significant level. The size and risk of the firms are positively related to the debt ratio – but only in term of market value measures of capital structure. The years of the companies being listed on stock markets are positively related to capital structure, indicating the access of the firms to debt finance is more easily judged by book value. Tax is not a factor in influencing debt ratio. Ownership structure has a negative effect on the capital structure. The firms with higher institutional shareholdings tend to avoid using debt financing, a behaviour that can be explained by entrenchment effects. A further classification of the institutional shareholders reveals that, among the three groups of institutional shareholding, the state institutions, including state agency and state-owned institutions, were more averse to debt financing, particularly for state-owned institutions. There is no strong evidence indicating debt-averse behaviour by domestic institutional shareholders. JEL Classification Numbers: G31, G32 Key words: capital structure, ownership structure
1. Introduction A good understanding of the determinants of firms’ capital structures is still elusive (Barclay and Smith, 2005) notwithstanding the volume of research since the seminal 1958 paper of Modigliani and Miller (MM). Much of this research has focused on the relaxation of the assumptions made in the MM paper, and these extensions include variables such as taxes, bankruptcy costs, industrial characteristics, ownership structure and agency costs (Harris and
12 Raviv, 1990). This research has also covered different economies with different institutional backgrounds. Rajan and Zingales (1995) found that the factors influencing the firms’ capital structures in the United States and other industrialised countries were similar, although they failed to provide an underpinning theory. Booth et al. (2001) investigated firms’ capital structures in developing countries, to see whether there were similar determinants as in developed economies. Their major finding was that a similar group of factors could explain capital structures, but that the persistent differences between the countries could only be understood with reference to the unique institutional structures of each country. With the increasing interest in corporate governance issues, the link between corporate governance and capital structure has also been attracting considerable theoretical attention (Jensen, 1986; Berger et al., 1997; Harvey et al., 2004). The objective of this paper is to investigate the determinants of capital structure in Chinese firms. There have been only two previous studies of Chinese firms. Chen (2004) uses data for 1997, and does not allow for differences between industries. Huang and Song (2005) only consider a limited range of ownership structures, although they do consider industry effects. In this paper, we not only use a recent (2003) dataset of 972 firms and control for differences across nineteen different industrial sectors but also consider the impact of several different types of institutional shareholders. More specifically, we classify the institutional shareholders according to whether they are (a) state agencies, which are government organisations exercising the functions of shareholders on behalf of the state; (b) state-owned institutions, which are entities controlled by governments at various hierarchic levels; and (c) domestic institutions, which are standalone entities set up by mixed groups of shareholders. This classification should shed light on the financing behaviour of listed companies with different types of large shareholders. Even more importantly, we combine insights from various strands of finance theory (notably agency theory, signalling theory and the theory of corporate control, as well as the timing of equity issues/purchases) to establish a more comprehensive model of the determinants of capital structure. The structure of the paper is as follows. In section 2, we outline the theory of capital structure, beginning with the classic MM paper and then discussing various subsequent refinements and extensions. An econometric model to explain the debt ratio is developed in the following section, and we suggest the expected impacts of the various explanatory variables. The dataset is then identified, and the regression methods are described in section 4. Section 5 presents and discusses the empirical results from the regression analysis. Section 6 summarises the main findings.
13 2. Theory and Prior Empirical Evidence1 The modern theory of capital structure originated with the paper by Modigliani and Miller (1958) who demonstrated that, if investors can borrow and save on the same terms as firms, and if firms’ financing decisions do not affect their total cash flows, then the firms’ choice between debt and equity has no effect on their total market value. In other words, capital structure cannot create value unless it affects the total returns. This conclusion was based upon the rather restrictive assumptions that the capital market was perfect, and that there was no taxation. Furthermore, it is clear that in reality firms have very different capital structures. The MM model should thus be seen as a starting point for modelling more realistic scenarios which explain why debt might be used in preference to equity. One early extension was to allow for the incidence of taxation and financial distress. Since the late 1970s, there have been two new strands of research which originate more from the theory of the firm: the ‘pecking order’ theory and the ‘trade-off’ theory. The pecking order theory argues that firms have a preference of issuing financing instruments due to adverse selection problems (Myers and Majluf, 1984). The theory suggests that the financial manager tends to use internal capital as the first choice, then issue debt, and equity will only be considered as the last resort as issuance of equity can be perceived by the market as a signal of a poor future for the investment. In contrast, the trade-off theory emphasises that an optimal capital structure can be achieved by the trade-off of the various benefits of debt and equity. 2.1.
THE PECKING ORDER THEORY
The pecking order theory is based on the information asymmetries between the firm’s managers and the outside investors. Ross (1977) was the first to address the function of debt as a signalling mechanism when there are information asymmetries between the firm’s management and its investors. He argued that management has better knowledge of the firm than the investors, and that management will try to avoid debt when the firm is performing poorly for fear that any debt default due to poor cash flow will result in their job loss. The information asymmetry may also explain why existing investors may not favour new equity financing, as new investors may require higher returns to compensate for the risks of their investment thus diluting the returns to existing investors. Myers (1984) and Myers and Majluf (1984) later developed their so-called pecking order theory of financing: i.e. that capital structure will be driven by firms’ desire to finance new investments preferably through the use of internal funds, then with low-risk debt, and with new equity only as a last resort. In their theory, there is no optimal capital structure that maximises the firm value. The financial managers issue
14 debt or equity purely according to the costs of capital. Subsequent empirical studies provide mixed evidence. Helwege and Liang (1996) found no empirical evidence for such a pecking order. Booth et al. (2001) found evidence supporting the theory in their 10-country empirical study. Frank and Goyal (2003) tested the pecking order theory on a broad cross-section of publicly traded American firms for 1971 to 1998, and concluded that the theory was not supported by the evidence. Whilst large firms exhibited some aspects of pecking order behaviour, the evidence was not robust to the inclusion of conventional leverage factors, nor to the analysis of evidence from the 1990s. 2.2.
THE TRADE-OFF THEORY
The trade-off theory argues that there is an optimal capital structure that maximises the firm value, but the trade-off comes in various forms. 2.2.1. Tax-Shield Benefits and the Financial Distress Cost of Debt One of the crucial assumptions of the MM (1958) model was that there is no taxation. Later work by Modigliani and Miller (1963), and Miller (1977) add tax effects into the original framework. An implication of this newer work was that firms should finance their projects completely through debt in order to maximise corporate value. Clearly this contradicts reality in that debt constitutes only a fraction of firms’ total capital. Subsequent theoretical work seeks an optimal capital structure which results from a trade-off between the benefits of tax shield of debt and the costs of financial distress of debt. According to this line of theory, the benefits of debt arise from its tax exemption, which implies that a higher debt ratio will increase the firm’s value. But the benefits can be offset by costs of financial distress, which may destroy the value of the firm. Thus the optimal capital structure is determined by the trade-off between the tax-free benefits of debt and the distress costs of debt - see Figure 1. DeAngelo and Masulis (1980), Ross (1985) and Leland (1994) have shown that, in the presence of taxation, it is advantageous for a firm with safe, tangible assets and plenty of taxable income to take a high debt-equity ratio to avoid high tax payments. For a firm with poorer performance and more intangible assets, it is better to rely on equity financing. One problem with the theories based on consideration of the tax-shield benefits is that they cannot explain why capital structures vary across firms that are subject to the same taxation rates. Empirical evidence from the United States (Copeland and Weston, 1992) shows that the capital structure of corporations did not change much after corporate income tax came into existence. In Australia, where there is no dual income taxation at all, capital structure is roughly the same as in other economies (Rajan and Zingales,
15 Market value PV (costs of financial distress)
PV (tax shield) Value if all-equity financed
Optimal debt ratio
Figure 1. The Optimal Capital Structure when Debt is associated with Tax-shield Benefit and Financial Distress Costs.
1995). Booth et al. (2001) found that the tax benefits vary in developing countries and play no role in the determination of capital structure choice. 2.2.2. Agency Theory and Capital Structure Even if markets are perfect and there is no tax impact, agency theory suggests that the appropriate mix of debt and equity is still an important matter for corporate governance. In general, debt claims provide the holders with a fixed repayment schedule but little in the way of rights to control the company, as long as the repayment schedule (and sometimes certain other terms) is met. However, creditors can have a strong influence over a company if it gets in financial distress but, even if a company is financially sound, creditors can influence whether it can obtain additional funding for proposed new projects. For example, a bank that has loaned a company the money for factory expansion can make it easy or hard for the company to borrow more money for a new office building. Conversely, equity claims – in particular, common stock – give shareholders the right to vote for Boards of Directors and on other important corporate issues such as major mergers or plans that would dispose of substantial portions of the company’s assets. Shareholders are also entitled to receive dividends or other distributions whenever the company pays them or, if the company is liquidated, to receive the net assets of the company after paying all debts and any securities, such as preferred stock, that rank ahead of common shares. These two features, the right to vote and the right to receive dividends and other distributions, are the defining characteristics of common shares. Jensen and Meckling (1976) identify two potential sources of conflict. On the one hand, conflicts between debt-holders and equity-holders arise because
16 the debt contract gives equity-holders an incentive to invest sub-optimally. More specifically the debt contract provides that, if an investment yields large returns well above the face value of the debt, equity-holders will capture most of the gain. If, however, the investment fails, debt-holders bear the consequences because of limited liability. As a result, equity-holders may benefit from ‘going for broke’; i.e. investing in very risky projects, even if they are value-decreasing. Such investments result in a decrease in the value of the debt. The loss in value of the equity from the poor investment can be more than offset by the gain in equity value captured at the expense of debt-holders. Equity-holders correctly anticipate equity-holders’ future behaviour. In this case, the debt-holders receive less for the debt than they otherwise would. Thus, the cost of the incentive to invest in value-decreasing projects created by debt is borne by the equity-holders who issue the debt. This effect, generally called the asset substitution effect, is the agency cost of debt financing. On the other hand, conflicts between shareholders and managers arise because managers hold less than 100% of the residual claim. Consequently, they do not bear the entire cost of these activities. Managers may thus invest less effort in managing the firm’s resources, and may be able to transfer firm resources to their own personal benefit, for example through ‘empire-building’ or by consuming ‘perquisites’ such as corporate jets, luxurious offices etc. The manager bears the entire cost of refraining from these activities, but captures only a fraction of the gain. As a result, managers overindulge in these pursuits relative to the level that would maximise firm value. This inefficiency is reduced the larger is the fraction of the firm’s equity owned by the manager. Holding constant the manager’s absolute investment in the firm, an increase in the debt ratio of the firm increases the manager’s share of the equity and mitigates the loss from the conflict between the manager and shareholders. Moreover, as pointed out by Jensen (1986), since debt commits the firm to pay out cash, it reduces the amount of ‘free’ cash available to managers to engage in the types of pursuits mentioned above. This mitigation of the conflicts between managers and equity-holders constitutes a benefit of debt financing. Jensen and Meckling (1976, p.117) argue that an optimal capital structure can be obtained by trading off the agency cost of debt against the benefit of debt as shown in Figure 2. The figure shows total agency costs, AT(E), as a function of the ratio of outside equity to total outside financing E = So/(B+So), for a given firm size, V*, and given total amounts of outside financing (B + So), where So stands for outside equity finance and B for debt finance. ASo(E) are the agency costs associated with outside equity, whilst AB(E) are the agency costs associated with debt. AT(E*) are the minimum total agency costs at optimal fraction of outside financing E*.
17 Agency costs AT(E)
AT(E) = ASo(E) + AB(E) AT(E*)
ASo(E)
0
AB(E)
E* = So / (B+So) Fraction of outside equity
1.0
E
Figure 2. The Optimal Capital Structure according to Agency Theory.
A number of implications follow from this analysis. First, one would expect bond contracts to include features that attempt to prevent asset substitution, such as interest coverage requirements, prohibitions against investments in new unrelated lines of business, etc. Second, industries in which the opportunities for asset substitution are more limited will have higher debt levels ceteris paribus. Thus, for example, the theory predicts that regulated public utilities, banks, and firms in mature industries with few growth opportunities will be more highly leveraged. Third, it is optimal for firms with slow or even negative growth, and that have large free cash inflows from operations, to have more debt. Large free cash flows without good investment prospects create the resources to consume perquisites, build empires, overpay subordinates etc. Increasing debt reduces the amount of ‘free cash’ and increases the manager’s fractional ownership of the residual claim. According to Jensen (1986) industries with these characteristics include steel, chemicals, brewing, tobacco, television and radio broadcasting, and wood and paper products. The theory predicts that these industries should be characterised by high leverage ratios. 2.2.3. Corporate Control One limitation of agency theory is that it assumes the agency problem can be mitigated, or eliminated, by a comprehensive contract which postulates all the future contingencies and states the circumstances in which the manager should take what action, as criticised by Hart (1995a, 1995b). But such a comprehensive contract would be very costly to design and/or execute
18 (Williamson, 1988). It may well be optimal to leave the contract incomplete, and to assign the equity-holders the residual control rights beyond contractual control rights which are assigned to the debt-holders (Aghion and Bolton, 1992). This incomplete contract approach regards equity and debt as ‘contingent state’ securities. When the firm is financially healthy, it is the equity-holders who have control. But a default of debt repayment will trigger the transfer of control to the debt-holders. Liquidation of the firm and/or managerial sackings are then inevitable. Thus management is constrained by the requirement to ensure a smooth repayment of debt (see table I). Two related models share a common concern with conflicts between shareholders and managers, though they differ in the specific ways in which the conflicts arise and in the role of debt. Harris and Raviv (1990) postulate that managers always want to continue the firm’s current operations, even if liquidation of the firm would be the preferred option for investors. But debt can force managers to liquidate firms, because default may well trigger the managers’ job loss. The optimal capital structure is achieved by trading off improved liquidation decisions against higher investigation costs. A different model by Stulz (1990) is based on the assumption that managers always want to invest available funds so as to expand the size of the firm, even though investors might prefer higher dividend payouts. The optimal capital structure in Stulz’s model is achieved by trading off the benefits of debt in preventing investment in bad projects with the costs of debt in preventing investment in good projects. Therefore, unlike in the Modigliani-Miller world, changing the capital structure of the firm changes the allocation of power between the insiders and the outside investors, and thus almost surely changes the firm’s investment policy (La Porta et al., 2000). 2.2.4. Equity Market Timing and Capital Structure Recently a number of studies have focused on the impact of equity market timing on capital structure, arguing that firms adjust their capital structures to target structures in response to changes in the firms’ market values (Baker and Wurlger, 2002; Hennessy and Whited, 2005). Firms will be more likely to issue equity when the market values of their shares are high, and to Table I. The Key Differences in Governance Structure between Debt and Equity Financial instrument
Contractual constraints Securities Intrusion
Source: Williamson (1988).
Debt
Equity
Numerous Pre-emptive Nil
Nil Residual claimant Extensive
19 repurchase equity when the share prices are low. The implications of this theory of equity market timing are that there should be an inverse relationship between capital structure and market value, and that the adjustment of the capital structure to its target level is a long and slow process. However, considerable theoretical development and empirical testing are needed (Frank and Goyal, 2004) before this can stand as an independent theory.
3. The Model 3.1.
THE DEPENDENT VARIABLES
Two broad definitions of capital structure are commonly used – one based on book value, the other on market value – and each has its strengths and weaknesses. For instance, the agency problems associated with debt (Jensen and Meckling, 1976; Myers, 1977) largely relate to how the firm has been financed in the past, and thus on the relative claims on firm value held by equity and debt. Here, the relevant measure is probably the amount of total book debt to capital. Mackay and Philips (2005) also point out that the use of book value can be justified on the grounds that financial managers focus more on book value when designing financial structure.2 And Barclay et al. (2006) justify the use of book value as there might be spurious relation between market value measurement and Tobin’s q. On the other hand, Welch (2004) argues that the market value measurement of capital structure can significantly explain stock returns. We thus use two measures of capital structure in this study. The book value measure is defined as follows3: Leverage 1 ¼
Total liabilities Total book value ðdebt þ equityÞ
ð1Þ
For the market value measure of capital structure, we follow the studies of Bradley et al. (1984), Titman and Wessels (1988), Opler and Titman (1994), and Gilson (1997), Fama and French (2002), and Welch (2004): Leverage 2 ¼
3.2.
Total liabilities Total liabilities þ market value of common stock
ð2Þ
THE EXPLANATORY VARIABLES
There are three groups of explanatory variables. The first group consists of seven variables, all of which have been suggested in the literature. The second group consists of four ownership structure variables. And the third group includes eighteen industry dummy variables.
20 As regards the first group, both Jensen and Meckling (1976) and Myers (1977) predict a negative relationship between the debt level and profitability. Chang (1987) also argues for a negative relationship as more profitable firms can finance higher growth rates internally, and his theory is empirically supported by Kester (1994), Friend and Hasbrouck (1988), Titman and Wessel (1988), Rajan and Zingales (1995), Wald (1999), Booth et al. (2001), and Fama and French (2002). Furthermore, these findings are robust for both developed economies as well as developing economies. We therefore expect a negative relationship between profitability (PROF), as measured by the ratio of earnings before interest and tax to total assets, and the debt ratio. Nearly all studies agree that firm size is an important determinant of capital structure, and that large firms are more likely to be debt-financed than their smaller counterparts. There are a number of reasons. Large companies are more diversified and thus have more stable cash flows, which helps reduce the risk of the debt. Additionally, large firms may be able to exploit economies of scale in issuing securities. Smaller firms are likely to face higher costs for obtaining external funds, because of information asymmetries. Large firms may also be more mature and hence have a larger fraction of firm value accounted for by assets in place, as opposed to growth opportunities. Diamond (1991) shows that large firms are likely to have established reputations and more firm-specific information publicly available than do small firms, facilitating the issuance of public debt. We thus include firm size as a control variable in our analysis and predict impact upon the debt ratio. Firm size can be measured in many different ways (Booth et al., 2001; Gilson, 1997), but Anderson et al. (2003) show that alternative measures of size (based on annual sales or total asset values) do not materially affect the inferences. Following Anderson et al. (2003), we use the natural logarithm of total assets as the measure of the size of the firm (SIZE) and expect it to have a positive impact. Intangible assets mainly consist of patents, goodwill, and copyrights, and reflect the unique characteristics of the firm (Rajan and Zingales, 1995). According to the ‘pecking order theory’, firms with more intangible assets should issue debt to mitigate information asymmetries, thus leading to higher levels of leverage (Harris and Raviv, 1990). On the other hand, the extension of agency theory discussed above suggests that firms with more intangible assets should borrow less to avoid excessive monitoring costs. The net effect of the variable INTAN is thus uncertain a priori, and remains to be established empirically. Various studies have shown that a firm’s optimal debt level is a decreasing function of the volatility of its earnings (Demsetz and Lehn, 1985; Titman and Wessels, 1988; Booth et al., 2001). We use the standard deviation of the return
21 on equity over the three years (2001–2003) as a proxy for business risk (RISK), and expect this variable to have a negative impact upon the debt ratio. The costs associated with the agency relationship are likely to be higher for firms in growing industries, as they have more flexibility in their choice of future investments. More specifically, improvements in a firm’s growth opportunities lead to an increase in the agency costs of debt and to a reduction in the agency costs of managerial discretion (Booth et al., 2001). This argument is supported by the empirical findings by Smith and Watts (1992), Titman and Wessels (1988), and Goyal et al. (2002). On the other hand, the ‘‘pecking order theory’’ argues that the high growth firm should issue debt, as debt is a more convincing financing instrument than outside equity financing (Myers and Majluf, 1984). We therefore cannot expect a clear negative or positive sign on the growth of sales (GROW) related to the debt ratio. The agency costs associated with asset substitution will be lower when the firm has a reputation for the selection of safe projects. The longer the firm’s history of repaying debt, the easier it will be to raise debt finance at a lower cost of borrowing. Diamond (1989) has shown that younger firms typically have less debt than older ones, other things being equal. We hypothesise that the age of listed companies (AGE), as measured by the years of listing on the Stock Market, will be an important indicator of corporate credibility, and expect there to be a positive relationship between AGE and the debt ratio. The rate of corporate income tax has long been identified as a potential determinant of capital structure. Firms will prefer to have more debt than equity finance because of the tax-shield on interest when the corporate income tax rate is higher, holding the personal income tax rate and the rate on dividend income constant. In China, different corporate income tax rates apply to different types of firms, even though they are all listed companies.4 Listed companies are normally faced with a 15% tax rate if local tax bureaux permit, but a small number of corporations face a higher rate of 33%. Companies with foreign investment enjoy a tax holiday during their first two years of establishment, and then only pay 7.5% for the following three years. And companies in high-technology industries also need to pay tax at a rate of only 7.5%. Following Miller (1977), we expect a positive relationship between the corporate income tax rate (TAX) and the debt ratio, in that the higher tax rate will reduce the cost of debt capital other things being equal. However, Booth et al. (2001) found a negative relationship between the corporate tax rate and the level of leverage. The second group of explanatory variables relate to the ownership structure of the firms. The impact of ownership structure on firms’ capital structures was first identified by Jensen and Meckling (1976) who pointed out the different financial motivations of different groups of shareholders. On the one hand, the existence of large shareholders may strengthen the monitoring
22 of management but, on the other hand, they may expropriate the minority shareholders by using their dominant powers and exploiting the private benefits of control (Pound, 1988). Filatotchev and Mickiewicz (2001) have indicated that concentrated shareholdings may also create entrenchment effects in addition to incentive effects (McConnell and Servaes, 1990) and, instead of imposing efficient monitoring and control on managerial discretion, the large-block shareholders may produce their own set of agency costs (Roe, 1990). Empirically, Amihud et al. (1990) and Zeckhauser and Pound (1990) found a negative relationship between the presence of large shareholders and firm leverage. In the Chinese ownership system, there are many block shareholders. All listed companies must be set up by promoters, who are institutions holding significant proportion of shares from the start and the shares that they hold cannot be traded openly on the official stock markets, thus causing effective lockouts. The shareholdings by the State thus dominate, accounting for around 70 percent of the total outstanding shares (Green, 2003). The State shareholdings are typically delegated to two institutions: governmental agencies which only represent the owners, and state-owned institutions who actively intervene in the business operations of the listed companies, normally through the presence of board members and sometimes even the Chairman. There are also other domestic institutional shareholders, which are normally privately capitalised companies. We introduce four ownership variables related to the degree of institutional shareholding: (a) the percentage of shares held by State asset management agencies (STATE); (b) the percentage of shares held by State-controlled institutions (SINST); (c) the percentage of shares held by domestic institutions (DINST); and (d) the total percentage institutional shareholding (INST = STATE + SINST + DINST). We predict negative signs for the coefficients of all four variables, because of the effects of the private benefits of control and of entrenchment. The third group of explanatory variables consists of nineteen industry dummy variables to control for systematic differences in leverage across industries (Harris and Raviv, 1990: 334) as a result of differences in systematic risk and of the unequal possibilities to engage in asset substitution. The China Securities Regulatory Commission (CSRC) categorises firms to one of twenty two industry sectors – see Appendix I - but firms in three industrial sectors (viz: financial and insurance institutions, utilities, manufacture of furniture and wood) were omitted from the study. The dummy variable for the media and culture industry (D1) is not included in the regression analysis so as to avoid the dummy variable trap. Table II summarises the explanatory variables included in the model and their expected impacts upon the debt ratio.
23 4. The Data The original dataset contained 1053 firms that were publicly traded on either the Shanghai Stock Exchange (SHSE) or the Shenzhen Stock Exchange (SZSE) in 2003. However, ten financial institutions were omitted because they had special capital structures, 51 utility companies were omitted because they operated in heavily regulated business environments, and two furniture manufacturing and wood processing firms were omitted because of their small number. The analysis in this paper is thus based on the remaining 972 firms spread across the remaining nineteen CSRC industrial categories – see Appendix I. Table III provides descriptive statistics for both the dependent variable(s) and the explanatory variables. The average debt ratios for the sample (53.07% book leverage; 30.38% market leverage) are modest compared with developed economies. Rajan and Zingales (1995) report the corresponding figures for the United States (52%, 44%), Japan (69%, 49%), Germany (73%, 55%), and the United Kingdom (54%, 40%). The debt levels are also modest when compared with developing economies (Booth et al., 2001): Brazil (30.3%, book measure only) and South Korea (73.4%, 64.3%). The firms in the sample exhibit considerable variability in the values of the explanatory variables. Firm size ranges from a minimum of 60 million yuan to 60 billion yuan. The average number of years since listing is 6.6 years, with a range from three to thirteen years. The performance ratios are striking: average profitability is only 2.38%, but the average annual sales growth rate is over 40% reflecting the fast development of the whole economy. The total
Table II. The Explanatory Variables and their Expected Impacts Variable
Description
Predicted sign
PROF
Earnings before interest and tax as percentage of
)
SIZE INTAN RISK GROW AGE TAX INST
total assets Natural logarithm of total assets The ratio of intangible assets to total assets Standard deviation of return on equity (2001–3) Average percentage growth rate of sales (2001–3) Years of listing on the Stock Market Corporate income tax (%) The percentage of shares held by all institutional
+ +/) – +/– + + –
STATE
shareholders The percentage of shares held by state asset management
–
SINST DINST D1 – D19
agencies The percentage of shares held by state-controlled institutions The percentage of shares held by domestic institutions Dummy variables for 19 industries
– –
See Appendix I
24 Table III. Descriptive Statistics for the Variables in the Model Variable
Mean
Standard Deviation
Minimum
Maximum
Leverage 1 (%) Leverage 2 (%) PROF (%) SIZE (million yuan) INTAN (%) RISK (%) GROW (%) AGE (years) TAX (%) INST (%) STATE (%) SINST (%) DINST (%)
53.07 30.38 2.38 2381.41 0.0493 31.1708 40.62 6.60 19.27 57.84 13.86 22.73 21.25
36.60 15.56 14.86 3486.20 0.0697 318.4169 224.5 2.52 32.79 13.11 21.72 27.00 22.75
2.73 1.42 -251.08 60.46 -0.0001 0.02 -75.62 3.00 -306.47 0.00 0.00 0.00 0.00
488.27 85.14 30.97 60917.58 0.7452 8795.49 4779.19 13.00 367.24 90.15 85.00 84.98 80.00
institutional shareholding accounts for 57.8% of the total outstanding shares, with the State controlling just over 36%. A number of observations may be made with regard to the other explanatory variables. First, the average corporate income tax burden is 19.27%, with a minimum rate of –306.47% (i.e. a tax refund) and a maximum rate of 367.27%. Naturally, the high growth rates are accompanied by high business risk (RISK): the average figure was 31% with a very high standard deviation.
5. The Empirical Results The model has been estimated using a robust (White) regression technique to take account of any potential heteroskedasticity, a common problem in cross-section models. Ordinary Least Squares (OLS) is an inappropriate estimation technique in the presence of heteroskedasticity, as the coefficient estimates are inefficient and the estimates of the standard errors are biased. The formula below is applied to obtain the robust estimates of the coefficients: N
^ ^ R u0 uj ÞV ^m ¼ Vð j j¼1
ð3Þ
^ ¼ ð@ 2 ln L=@b2 Þ1 is the conventional OLS estimator of variance, where V and uj (a row vector) is the contribution from the jth observation to the scores ¶lnL/¶b. Two versions of the model were estimated using the two alternative measures (i.e. leverage1 and leverage2) for the debt ratio:
25 Debt Ratio ¼ a þ b1 PROF þ b2 SIZEb3 INTAN þ b4 RISK þ b5 GROW þ b6 AGE þ b7 TAX þ b811 Ownership þ b1229 Dummies þ li
ð4Þ
where Debt ratio=leverage 1 or leverage 2 a = intercept b1 ... b7 = the coefficients of the first group of explanatory variables b8 ... b11 = the coefficients of the four ownership structure variables b12 ... b29 = the coefficients of the eighteen industry dummy variables li = the (heteroskedastic) error term The regression results are tabulated in Table IV (with leverage1, the book value of capital structure, as the dependent variable) and in Table V (with leverage2, the market value of capital structure, as the dependent variable) respectively. In each table, we first include the total institutional shareholding (INST) as an explanatory variable to assess the impact on the capital structure decision (column 1). The total institutional shareholding is then divided into its three component categories (i.e. STATE, SINST and DINST), and these three variables are separately included as explanatory variables (column 2). Table IV. The Regression Results using the Book Value of the Debt Ratio as the Dependent Variable Explanatory variables
PROF SIZE INTAN RISK GROW AGE TAX INST STATE SINST DINST Industry dummies Constant Number of observations R2 F-statistic
Including INST as an explanatory variable (1) )0.8617*** (9.07) )0.0214 (0.89) 0.0949 (0.33) 3.7610)5 (0.91) 0.0001* (1.74) 0.0192*** (3.36) )0.0117 (0.38) 0.0064 (0.05)
Yes 0.4837* (1.80) 972 0.2124 12.61***
Including STATE, SINST and DINST as explanatory variables (2) )0.8590*** (8.91) )0.0169 (0.67) 0.0668 (0.23) 3.5310)5 (0.84) 0.0001 (1.59) 0.0163*** (3.02) )0.0166 (0.54) 0.0842 (0.53) )0.0702 (0.55) 0.0660 (0.49) Yes 0.4647 (1.46) 972 0.2214 13.13***
Notes: (1) The absolute values of the t-statistics are in brackets. (2) The symbol * signifies that the variable is significant at the 10%level; ** that it is significant at the 5% level; and *** that it is significant at the 1% level.
26 Table V. The Regression Results using the Market Value of the Debt Ratio as the Dependent Variable Explanatory variables
Including INST as an explanatory variable (1)
Including STATE, SINST and DINST as explanatory variables (2)
PROF SIZE INTAN RISK GROW AGE TAX INST STATE SINST DINST Industry dummies Constant Number of observations R2 F-statistic
)0.2882*** (5.84) 0.0671*** (11.27) 0.0980 (1.58) 2.67 10 )5*** (2.64) 7.99 10)6 (0.27) )0.0016 (0.86) 0.0160 (1.01) )0.1017*** (2.63)
)0.2899*** (5.80) 0.0725*** (11.73) 0.0790 (1.28) 2.7 10)5*** (2.59) 3.53 10)7 (0.01) )0.0028 (1.49) 0.028 (0.79)
Yes )0.6792*** (8.74) 972 0.2912 16.68***
)0.0907** (2.15) )0.1351*** (3.49) )0.0272 (0.68) Yes )0.6106*** (6.83) 972 0.3128 16.96***
Notes: (1) The absolute values of the t-statistics are in brackets. (2) The symbol * signifies that the variable is significant at the 10%level; ** that it is significant at the 5% level; and *** that it is significant at the 1% level.
Two general observations may be made. On the one hand, the signs and magnitudes of the regression coefficients are very stable within each pair of regressions, particularly those with the market value measure of capital structure. But, on the other hand, there are marked differences in the magnitude and significance of the impact on the book value and the market value of several variables. Perhaps this reflects the ‘one reality-two stories’ phenomenon in corporate finance research (Barclay and Smith, 2005). As regards the first group of seven explanatory variables, the coefficients of the profitability variable (PROF) are negative, as expected, in all four regressions confirming the findings of Chen (2004). The negative signs indicate that firms with more profitable projects are inclined to use internallygenerated funds rather than debt, and the significance of the coefficients is very striking. Every 1% increase in the return on assets will lead on average to a 0.86% reduction in the book debt ratio, and to an average 0.29% fall in the market value measure, other things being equal. This result might reflect, following the agency theory arguments of Jensen (1986), that entrenched managers in more profitable firms avoid using debt because it provides a hard constraint. But the result might also be explained by the ‘pecking order’ theory as management preferring to use internal funds (less costs incurred by information asymmetry problem) than debt.
27 There is contradictory evidence with regard to the effects of firm size (SIZE) on capital structure. Firm size has a weak, barely significant, negative effect on the book value of the debt ratio, but a strong, highly significant, positive effect upon the market value measure. One interpretation of the positive relationship, following Sapienza (2004), is that State-owned banks, which still account for a large proportion of bank lending in China, tend to favour large firms with credits for political reasons: large firms employ more labour. Unlike private banks whose objective is pure profit-seeking, the role of State-owned banks is to facilitate wider social welfare. Firms with high proportions of intangible assets (INTAN) are more likely to have higher market values of the debt ratio, though the coefficients are not quite statistically significant. This is evidence in favour of the pecking order theory, according to which firms with more intangible assets face more serious information asymmetry problems. Debt financing helps mitigate these problems, as issuing debt is a stronger signal than issuing equity (Myers and Majluf, 1984). The business risk variable (RISK) has positive coefficients in both sets of regressions, and is highly significant with regard to the market value measure. This does not conform to our prediction, but is consistent with the results of Booth et al. (2001) who reported different signs for different countries and suggested that this might reflect the unique institutional structures (including taxation rules and bankruptcy law) within which the firms were operating. Bankruptcy is rare in China, particularly in listed firms, as the Government provides support when necessary. There are several instances in the sample of 972 firms in this study where the firms’ assets fall seriously short of its liabilities, but these firms continue to exist as the banks are not allowed to force the firms to close down. The sales growth (GROW) variable has a positive coefficient in all four regressions, contradicting the pecking order theory, but is not significant. There is contradictory evidence with regard to the effects of the age of the firm (AGE) on capital structure. AGE has a weak, insignificant, negative effect on the market value of the debt ratio, but a strong, highly significant, positive effect upon the book value measure. These results suggest that access to debt finance is assessed by reference to the book value of the debt ratio, and that it is easier for firms with longer histories on the stock markets. These findings suggest that corporate credibility may well reduce the cost of borrowing from the banks, and enable firms to establish better lines of credit. An alternative and more fundamental explanation might be that the longer histories on the stock market imply greater monitoring from the banks, and thus a reduction of the agency costs of providing debt finance. There was also contradictory evidence with regard to the effects of the corporate tax burden (TAX), but none of the coefficients were
28 statistically significant. This suggests that the tax advantages of debt capital are not attractive to firms in China – a result not uncommon in developing countries (see Booth et al., 2001). We now turn to the second group of potential determinants of capital structure, namely the variables related to the institutional shareholdings. As Harris and Raviv (1990) have pointed out, the relationship between large shareholdings, particularly institutional shareholdings, and capital structure is still not obvious. The existence of both concentrated shareholdings and debt may exercise the function of disciplining managers or, as has been the case in many developing countries, the institutional shareholders can collude with managers to take advantage of small investors. Furthermore, the institutional shareholders themselves are agents of their clients, leading to an ‘agency of agency’ problem. In short, concentrated shareholdings may perform an alignment function or provide an entrenchment hazard. The various measures of institutional shareholding appear to have no effect on the book value of the debt ratio – see Table IV. But institutional shareholdings do have very significant effects upon the market value of the debt ratio – see Table V. The sign of the coefficient for the total institutional shareholding is negative, as expected, and is significant at the 1%. This suggests that the institutional shareholders tend to avoid debt financing – a signal for entrenchment. Most institutions send representatives to sit as Board members, which effectively makes them insiders so entrenchment is appropriate to describe managerial behaviour. An alternative explanation would be that the large shareholders prefer lower levels of debt so as to avoid financial distress, regardless of the value of the companies. It is more interesting to look at the results for the various categories of institutional shareholders. The coefficients for both the State asset management agency shareholdings and the State-owned institutional shareholdings were negative, and very significant. The sign of the coefficient for the domestic institutional shareholdings was also negative, though not statistically significant. Our interpretation is that the presence of state shareholdings may result in bigger entrenchment issues, and that they tend to avoid debt financing in order to reduce the risk of financial leverage. It is important to consider the direction of causality between the capital structure and the ownership structure. Friend and Hasbrouck (1988) suggest the possibility of reverse causality: a high level of debt may increase the risk attached to the firm stock, and drive out outside shareholders. However, it should be noted that the ownership structure of many Chinese firms is rather inflexible, compared to other economies, in that large shareholders do not have the freedom to alter their shareholdings according to the financial position of the firm. Their scope for financial decision-making is limited to the choice of debt level, and not to adjusting the ownership structure.
29 Finally, we consider the third group of explanatory variables: the eighteen industry dummy variables. These variables were included in the various regressions and the results are reported in Appendix II and Appendix III, but the detailed results are not shown in Tables IV and V to conserve space. It should be noted that no dummy variable was included for the media and culture industry. As Jensen (1986) predicts, the steel (D14), chemicals (D16), brewing (D17), and paper product industries (D19) should be characterised by high leverage ratios. Our results broadly confirm these predictions. The coefficients for the steel (D14), chemicals (D16) and paper product (D19) industries are always positive and statistically significant; whilst those for the brewing (D17) industry are positive but not statistically significant. Thus the predictions based on agency theory are generally right. 6. Concluding Remarks In this study, we have examined the determinants of capital structure in a sample of 972 Chinese listed companies, and focused particularly on the role of institutional shareholdings. As predicted by various studies around the world, profitability is the strongest and most highly significant predictor of financing behaviour. More profitable firms will use less debt. Also firms with higher proportions of intangible assets tend to hold more debt, though the effects were not statistically significant. There were contradictory results with regard to firm size and age. Firm size had a significant positive effect on the market value of the debt ratio but not on the book value, whilst firm age had a significant positive effect upon the book value of the debt ratio but not the market value. Business risk had a significant positive effect upon the market value of the debt ratio, contrary to the predictions of both the trade-off and the pecking order theories. This may be explained by reference to the Chinese institutional structure in which debt is not as hard a constraint as in many economies with well-functioning legal systems. Firms with longer histories of being listed on the Stock Exchanges will have more access to debt finance due to the reduced information asymmetries. The rate of growth seems to play no role in determining the capital structure: a finding consistent with Chen (2004)5. And taxation does not seem to be a consideration for managers when considering financing. Overall, the results for the first group of explanatory variables are broadly consistent with the evidence from other economies, but there are clearly some special Chinese features. Furthermore, the better performance of the model with the market value measure of capital structure indicates that the timing of equity issues is an important consideration – that firms will issue more equity when stock markets are overvalued (Baker and Wurgler, 2002). As the denominator of the market value measure of capital structure includes the market value of the equity component, higher-valued equity leads to lower
30 market value of the debt ratio (Fama and French, 2002). This explanation should be tested further when suitable data become available. The results for the second group of explanatory variables also tend to confirm the a priori predictions that corporate governance has an impact. Firms with large institutional shareholdings have smaller debt ratios, indicating the reluctance or inability of these major shareholders to take on debt: typical entrenchment effects. This effect is particularly apparent with regard to state shareholdings, both those held by state agencies and state-controlled institutions. A question for future research is whether institutional shareholders are better than individual investors in reducing agency problems? The available evidence suggests the answer is yes in most developed economies, but whether the ‘agency of agency’ aspect can mitigate agency problems is questionable. Finally, our results appear to confirm the findings of Jensen (1986) and others that firms in certain industrial sectors will have higher debt ratios. Two final points should be emphasised with regard to the determinants of capital structure. First, as pointed out by Chen (2004) for China and by Booth et al. (2001) for other countries, it is vital to be aware of the legal, cultural and institutional differences between developing economies (including China) and the Western economies. Corporate governance is ‘path dependent’ (Bebchuk and Roe, 1999). Second, many theories offer similar predictions as to the capital structures of firms. Often the only differences between the theories lies in the strength of the predictions, and the evidence if typically far from clear-cut (Fama and French, 2002). It should be born in mind that the objective is to identify the determinants of capital structure, rather than to test the relative potency of individual theories. Appendix Appendix I. The Composition of the Sample according to the CSRC Industrial Classification System Dummy
Description of Industry
Number of firms in sample
D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11 D12 D13
Media, culture Conglomerate Real Estate Construction Transport, storage Mining Agriculture, forestry, fishing, Wholesale, retailing, trading Information technology Social Service Manufacturing – Machinery, equipment, instrument Manufacturing – Electronics Manufacturing – textile, clothes
10 78 42 15 37 11 25 83 64 35 156 26 44
31 Appendix I. Continued Dummy
Description of Industry
Number of firms in sample
D14 D15 D16 D17 D18 D19
Manufacturing – metal, non-metal Manufacturing – others Manufacturing – Petroleum, chemistry, plastics Manufacturing – food, beverage Manufacturing – pharmaceutics and biological products Manufacturing – papermaking and press Manufacturing – furniture, wood process Financial and insurance institutions Gas, water and electricity
93 14 116 45 59 19 21 102 513 1053
Total
Notes: (1) The two food manufacturing and wood processing firms were omitted from the analysis because of their small number. (2) The ten financial and insurance institutions were omitted from the analysis because of their special capital structures. (3) The fifty one utility firms were omitted from the analysis because of the heavily regulated business environments. Appendix II. The Estimated Coefficients for the Industrial Dummy Variables when the Book Value of the Debt Ratio is the Dependent Variable (Table IV) Dummy Variables
Including INST as an explanatory variable (1)
Including STATE, SINST and DINST as explanatory variables (2)
D2 D3 D4 D5 D6 D7 D8 D9 D10 D11 D12 D13 D14 D15 D16 D17 D18 D19
0.2320*** (3.57) 0.2670*** (3.22) 0.3266*** (4.52) 0.1038 (1.40) 0.0722 (0.93) 0.1897** (2.53) 0.2244*** (3.55) 0.1615** (2.57) 0.1075 (1.46) 0.1992*** (2.75) 0.1440** (2.02) 0.1351** (1.98) 0.2787*** (3.10) 0.4604 (1.44) 0.1735*** (2.74) 0.1119 (1.63) 0.2216*** (3.10) 0.5116** (2.50)
0.2168*** (3.38) 0.2527*** (3.29) 0.3178*** (4.53) 0.0933 (1.32) 0.0986 (1.28) 0.1875*** (2.66) 0.2000*** (3.13) 0.1552** (2.54) 0.1114 (1.57) 0.1930*** (2.84) 0.1298* (1.89) 0.1211* (1.82) 0.2799*** (3.16) 0.4461 (1.42) 0.1739*** (2.83) 0.0938 (1.44) 0.2105*** (3.02) 0.4954** (2.50)
Notes: (1) D1 (for the media and culture industries) was not included in the regressions, so as to avoid the ‘dummy variable trap’. (2) The absolute values of the t-statistics are in brackets. (3) The symbol * signifies that the variable is significant at the 10% level; ** that it is significant at the 5% level; and *** that it is significant at the 1% level.
32 Appendix III. The Estimated Coefficients for the Industrial Dummy Variables when the Market Value of the Debt Ratio is the Dependent Variable (Table V) Dummy Variables
Including INST as an explanatory variable (1)
Including STATE, SINST and DINST as explanatory variables (2)
D2 D3 D4 D5 D6 D7 D8 D9 D10 D11 D12 D13 D14 D15 D16 D17 D18 D19
0.1385*** (3.18) 0.1955*** (4.04) 0.2344*** (4.57) 0.0174 (0.37) )0.0398 (0.81) 0.1673*** (3.40) 0.2044*** (4.71) 0.1202*** (2.73) 0.0769 (1.61) 0.1100** (2.58) 0.0743 (1.58) 0.1134** (2.48) 0.1213*** (2.77) 0.1522** (2.58) 0.1311*** (3.04) 0.0700 (1.53) 0.1247*** (2.81) 0.2521*** (4.28)
0.1180*** (2.86) 0.1838*** (3.98) 0.2392*** (4.87) 0.0134 (0.31) )0.0331 (0.70) 0.1620*** (3.50) 0.1925*** (4.65) 0.1053** (2.54) 0.0759* (1.66) 0.1005** (2.50) 0.0652 (1.47) 0.0951** (2.15) 0.1193*** (2.89) 0.1391** (2.42) 0.1294*** (3.16) 0.0601 (1.39) 0.1126*** (2.67) 0.2391*** (4.11)
Notes: (1) D1 (for the media and culture industries) was not included in the regressions, so as to avoid the ‘dummy variable trap’. (2) The absolute values of the t-statistics are in brackets. (3) The symbol * signifies that the variable is significant at the 10% level; ** that it is significant at the 5% level; and *** that it is significant at the 1% level.
Notes 1. This section is the updated version of the discussion on the theory of capital structure in Chen (2004). 2. See the recent survey by Graham and Harvey (2002). 3. This measure, however, fails to take account of the fact there are some assets that are offset by specific non-debt liabilities. For example, an increase in the gross amount of trade credit is reflected in a reduction of this measure of leverage. Given that the levels of accounts payable and accounts receivable may jointly be influenced by industry considerations, it seems more appropriate to use a measure of leverage unaffected by the gross level of trade credit. A variant of the above definition is provided by the ratio of total debt to net assets, where net assets are total assets less accounts payable and other liabilities. Although this measure is not influenced by the amount of trade credit, it is affected by factors that may have nothing to do with financing. For example, assets held against pension liabilities may decrease this measure of leverage. 4. In some cases, listed companies can face tax rates that are different from their subsidiaries. Consolidated tax figures are recorded when companies exert a controlling interest in other firms. 5. Huang and Song (2005) use Tobin’s q as a measure of growth, and find a positive relationship between growth and capital structure. But Tobin’s q is a proxy for future growth potential, while the growth variable used in this study and by Chen (2004) is a measure of historical sales growth. Fama and French (2002) point out that there are problems with using Tobin’s q as a proxy for potential growth, as it may have a spurious correlation with the market value of the debt ratio.
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