Determinants of capital structure: further evidence from China.
The Trade-Off Theory and the Pecking Order Theory are two main competing theories that are used by previous empirical studies on corporate, capital structure. However, those studies were not able to provide a better understanding of capital structure (Fama & French, 2005). Especially, the empirical works which sought the explanatory factors of a firm's debt were unable to provide consistent evidence. In this study, while expanding the current works on the determinants of capital structure by presenting further evidence from China, we provide a new interpretation of the relationship between agency cost and capital structure.
Instead of the profitability measures used in previous studies, we used the technical efficiency1 of a firm to represent agency cost. The link between productive efficiency and leverage has been first identified by Leibenstein (1966). As explained by Leibenstein, a firm's relative inefficiency can be used as a summary measure of an incomplete contract, different principle agent objectives and inadequate motivation. More recent works of Berger and Bonaccorsi di Patti and (2006) Margaritis and Psillaki (2007a) have been linked to this theoretical interpretation with the empirical work in capital structure. We used the empirical framework they developed in this study. Furthermore, a firm's relative market share measured on sales revenue is used as a proxy for the political power of the firm. The aim of this paper is to investigate the role of production and cost decisions in determining financial leverage. It uses three main hypotheses: efficiency risk, franchise value (Berger & Bonaccorsi di Patti, 2006; Margaritis & Psillaki, 2007b) and political power to test the impact of such decisions on corporate capital structure.
The rest of this paper is organised as follows. The next section briefly discusses the relevant literature of previous studies on corporate capital structure. Section three outlines the methodology used in the study. Section five presents the empirical results followed by the conclusion of the study.
Since the seminal work of Modigliani and Millar in 1956, a large number of researchers have attempted to understand the determinants of a firm's capital structure (Bradley, Jarrell, & Kim, 1984; Chen & Strange, 2005; Chen, 2004; Ozkan, 2001; Titman & Wessels, 1988). Those studies have mainly been based on the Pecking Order Theory (Jensen & Meckling, 1976) and the Trade-Off Theory. As pointed out by Margaritis and Psillaki (2007b), even though there has been remarkable progress on the theoretical work of capital structure, the practical application of those theories are far from satisfying. Recorded evidence in previous empirical works indicate that there is no a comprehensive theory that can explain the relationship between capital structure choice and a firm's value.
The Trade-Off Theory identifies optimal leverage by weighing the cost and benefits of using more debt in the capital structure (Fama & French, 2002). It considers factors such as taxes, agency cost, financial risk and political cost associated with the debt financing. Though many previous studies used this theory (Ozkan, 2001), the major problem with this approach is obtaining a reliable measure for representing agency cost, financial risk and political cost (Berger & Bonaccorsi di Patti, 2006).
The Pecking Order Theory is based on the information asymmetry argument which states that insiders possess more private information about the firm's expected return and investment opportunities than outsiders. As explained by the theory, this information asymmetry may under price the firm's securities in the market (Myers & Majluf, 1984). This theoretical argument paves the way for Myers' Pecking Order Theory of capital structure (Myers, 1984). According to Myers, firms prefer to finance new projects by first using internally generated capital, then low risk debt and finally using equity. One of the main implications of the Pecking Order Hypothesis is that the firm's ability to collect funds internally will affect the extent of funds sourced on debt. Therefore, the availability of internally generated funds may diminish a firm's need for generating funds using external sources.
Free Cash-Flow Theory is another theory used in corporate capital structure studies (Jensen, 1986). This theory states that the main challenge faced by the corporation is how to motivate managers to avoid making underinvestment decisions that may create a return below the cost of capital. On the other hand, management's desire to avoid the cost of underinvestment to the firm's shareholders may force it to use debt capital to finance projects that have negative net present values, resulting in an increase in the financial distress to the firm (Myers, 1977). Such behaviour could lead to an increase in managers' risk-taking activity as they are acting on the shareholders' behalf on the cost of debt holders as part of risk shifting investment strategies (Jensen & Meckling, 1976). Consequently, the agency costs of outside equity may exhibit a positive relationship between a firm's performance and its leverage. On the other hand, the agency costs of outside debt results negatively on the firm's performance because highly leveraged firms, especially those are on high risk in default, are more likely to pass up profitable investment opportunities or shift to riskier operating strategies. Furthermore, the separation of ownership and management in modern-day corporations creates agency costs that result from the noncongruent objectives of mangers and owners (Jensen & Meckling, 1976).
This study examins the impact of the firm's productive efficiency and the impact of market structure on the firm's financing choices. We relied on both the Trade-Off Theory and the Pecking Order Theory in linking productive efficiency and market structure with corporate capital structure. In a recent stduy, Berger and Bonaccorsi di Patti (2006) used profit efficiency as an alternative measurement of a firm's performance. However, Stigler (1976) is the first economist to explain the conceptual link between productive efficiency and agency costs. As expained by Stigler, "increase in x-efficiency to 1) increase in motivational efficiency--workers are stimulated by incentive to pay, or management by competition or other adversities; and 2) improvements in the efficient market for knowledge" (Stigler, 1976 p. 213). This wording clearly indicates the relationship between agency theory and x-efficiency. Stigler also showed that profit efficiency evaluates how close a firm is to earning the profit that a best-practice firm would earn facing the same exogenous conditions. This has the benefit of controlling firm-specific factors outside the control of management that are not part of agency costs. However, most financial ratios do not account for important differences across firms within an industry--such as local market conditions. Berger and Bonaccorsi di Patti (2006) stress that the performance of a best-practice firm under the same conditions provides a reasonable benchmark for how the firm would be expected to perform if agency costs were minimized. Therefore, a firm's technical efficiency is a better measurement of performance.
Previous studies have identified a large number of factors as determinants of a firm's financing choices. Some of them are agency costs (Jensen and Meckling, 1976; Myers, 1977; and Harris and Raviv, 1990); corporate control issues (Harris and Raviv, 1988); asymmetric information (Myers and Majluf, 1984; and Myers, 1984) and taxation (DeAngelo and Masulis, 1980; and Bradley et al., 1984). This study examined the impact of a firm's relative efficiency and market structure on its financing choices.
As pointed out by Margaritis and Psillaki (2007b), efficient firms face a trade-off between the advantages of acquiring more debt and the costs associated with financial distress. A value-maximizing firm would operate at the point where these expected benefits and costs are equal (Margaritis & Psillaki, 2007b). This point will correspond to a higher debt-to-equity ratio for more efficient firms equipped with assets that would escape serious damage in the event of financial distress.
The purpose of this study is to examine the influence of relative efficiency and market structure on a firm's financial leverage. Thus, we used three main hypotheses in this study. The first two hypotheses are used to explain the relationship between a firm's capital structure and its efficiency. The first, efficiency risk hypothesis, is based on the Trade-Off Theory of capital structure. More efficient firms may tend to offer a higher return for a given capital structure (Margaritis & Psillaki, 2007b). The higher return may increase the firm's long-term debt capacity by putting those firms in a better position to substitute equity for debt in their capital structure. Therefore, the Efficiency-Risk Hypothesis predicts that more efficient firms choose relatively higher debt to total assets ratios, because higher efficiency is expected to lower the costs associated with bankruptcy and financial distress.
The prediction of the second hypothesis--franchise value--is more aligned to the theoretical explanation of the Pecking Order Theory. Firms which may expect to sustain high efficiency rates into the future will choose lower debt to total assets ratios as a precautionary action to reduce economic rents or franchise value generated by these efficiencies from the threat of liquidation (Berger & Bonaccorsi di Patti, 2006; Demsetz, Saidenberg, & Strahan, 1996). Accordingly, the franchise-value hypothesis (see Berger and Bonaccorsi di Patti, 2006) assumes that efficient firms tend to hold extra equity capital and therefore, all else being equal, choose lower debt to total assets ratio to protect their future income or franchise value.
The third hypothesis--political power--is based on the Structure Conduct Performance Theory which stresses the significance of influence of market power in firms' decision making process. The extent of using debt financing might be influenced by the firm's behaviour in the product and service market. Therefore, the third hypothesis 'political power' predicts that a firm with higher political strength in its respective market segment is in a position to raise debt capital than the other firms in the same market segment. The political strength of a given firm in a market can be measured based on the firm's size and its market share. The size of a firm is itself an absolute measure. It is not reflecting the real strength of a firm in the market place. Thus, we use the relative market (revenue) share of individual firms to represent firms' political strength together with the firm's size. Furthermore, market concentration may play a vital role in variation of leverage ratio in different industries. Highly concentrated industries may have a small number of dominating firms. Those firms may have relatively higher debt capacity than the other firms. Thus, we predict a positive relationship between a firm's leverage and a firm's degree of market concentration.
We extend the model used by Bradley, Jarrell et al., (1984), Titman and Wessels (1988) and others by incorporating efficiency and market structure variables. The estimated parameters for the following model are used to test the efficiency-risk, franchise-value and political power hypotheses:
[L.sub.it] = [[alpha].sub.0] + [[beta]
where [L.sub.it] is the debt (long-term, short-term and total debt) to total assets ratio of the 'i'th firm in the year 't'. Theta ([theta]), M and C are the explanatory variables used to proxy efficiency, the market share and the market concentration of the i'th firm in year 't' respectively. The variable '[Z.sib.it]' is a vector of control variables that is used for representing other significant determinant variables. The vector of control variables contains factors other than technical efficiency market concentration (MCON) and market share (MS) that are likely to influence the debt capital ratio, including firm size, operating cycle, profitability and other relevant variables. ID (industry dummies) and TD (time dummies) are two vectors of dummy variables used to capture the unobservable industry variant and time variant impact on the firms' leverage. The variable '[??]' represents the error component of the regression estimation.
Data and variables
We used an unbalanced panel dataset that is consistent with data from the firms listed on the Shanghai and Shenzhen Stock Exchanges in China. All data for the study has been extracted from the Taiwan Economic Journal (TEJ) database. The database presents corporate, financial statement data from 31 industries. We used the same industry classification that is used in the TEJ for estimating the efficiency, market concentration and relative market share of each firm. Seven industries were excluded from the study sample since those industries contain a small number of firms that is not sufficient to estimate the relative efficiency of those industries. Our initial sample contained 7,820 observations from 24 industries. We used a full data sample to construct separate production frontiers for each period. This was done to estimate relative efficiency and to calculate relative market share and market concentration. We combined data from annual reports with the market data extracted from the same database and obtained 5263 observations.
We used three leverage measures (short-term debt to total assets, long-term debt to total assets and total debt to total assets) as dependent variables. Variables such as ROA, Input-oriented data envelopment analysis (DEA) estimated technical efficiency and scale efficiency scores, leverage, market share of the firm (sales) and the industry market concentration (sales) have been used as main explanatory variables. To proxy other control variables, financial ratios such as return on assets (ROA) (profitability), market to book value (growth potential), non debt tax shield, size (LN(TA)), rate of outstanding shares (use of capital market), total assets growth (growth) and long term asset to total assets (Tangibility of assets) are also used.
Input-oriented data envelopment analysis (DEA) estimates the efficiency of Chinese firms. The DEA, which is a non-parametric approach widely used for estimating relative productivity and efficiency, has the capacity to incorporate multiple inputs and outputs in the efficiency assessment process. It also allows for the progressive assembling of production frontiers without a pre-specified functional form. This study applied the constant return to scale DEA model which is called CCR2 (Charnes, Cooper, & Rhodes, 1978) to estimate the technical and pure-technical efficiency.
The ultimate objective of the firm is to maximise shareholder wealth. To achieve this objective, management should efficiently apply the firm's resources in its operational activities. Since management uses income statements to produce the information about their operation, we thus used income statement data to identify an appropriate input and output combinations that satisfies the production environment of all industries. The efficiency estimation model includes cost of sales, other operating cost and non-operating expenses as inputs and net sales income and other income as outputs. Production frontiers for estimating the relative efficiency of each decision-making unit are separately constructed for each industry.
The sales and the total assets of firms can represent the combined outcome of entire operational activities. Thus, we used net sales to represent the market size of each industry, and we used total assets to represent the firm's size. Previous studies have used Herfindahl-Hirshman index (HHI)3 (Goldberg & Rai, 1996; Molyneux, 1999; Yu & Neus, 2005) and the 'k' firms concentration ratio (CRk) (Goldberg & Rai, 1996) to proxy the collusive power of dominating firms in an industry. Since HHI takes in into account the market share of every firm in the industry, this study uses it to proxy the collusive power of a given industry. The respective market concentration indexes and relative market share of each firm have been estimated separately for all 24 industries.
Previous studies have been applied a fairly large number of control variables as the determinants of corporate capital structure. To isolate the impact of those other control variables; we applied a number of variables to our regression model. Those variables have been used to proxy firm size (Natural logarithms of the total assets), assets tangibility (fixed assets to total assets ratio), growth (assets growth ratio), use of capital market (percentage of trading shares in the stock market) growth potential (market to book value ratio), and non debt tax-shield (depreciation tax shield).
Results and Discussion
Table 1 provides descriptive statistics of the sample data. The reported data indicate that Chinese firms have used a relatively lower level of debt for financing their capital requirements. The estimated average, short-term debt ratio reveals that Chinese firms extensively use short-term debt. Annual average debt ratios indicate that there is a slight increase in the use of debt capital during the later part of the sample period. The other notable point is Chinese firm's lack of reliance on the stock market for capital accumulation. The analysis of descriptive statistics reveals that Chinese firms have floated only a minor portion of their issued capital share. It is evident that the Chinese rely very little on direct financing sources. The number of floated shares may have affected the market-to-book value ratio.
We used market share, market concentration and efficiency as the main test variables in this study. Most of the industries are dominated by a fairly large number of big firms.
Consequently, the total market has been shared equally among these big firms. Thus, the average market share records relatively lower percentage. This is also reflected in the recorded average market concentration ratio. A relatively higher level of average scale efficiency scores with a small standard deviation has been recorded by the Chinese firms. On the other hand technical efficiency has indicated a relatively high level of dispersion. As explained by Huang and Song (2006), not like in other countries, the corporate income tax rates depend on the type of ownership and the location of the firm. Thus, we estimated the depreciation tax shield for each company based on the effective tax rate. The effective tax rate was estimated based on income before tax and income tax for the period. We used a logarithm value of the depreciation tax shield to proxy the non-debt tax shield. The Table 2 reports the correlation coefficient among the test variables. Those statistics evidently show that there is no reason to believe that multicollinearity problem exists among the test variables.
The estimated coefficients for the study model are presented in the Table 3. The coefficients have been estimated using two model specifications and three dependent variables (long-term debt, short-term debt and total debt). We used estimated coefficients to test the efficiency-risk, franchise-value and political power hypotheses. The results presented in the Table 4 identify both industry variant and time variant unobservable impacts on the firm's leverage.
Estimated coefficients that use two model specifications show a statistically significant negative relationship between corporate debt and technical efficiency. This result rejects the efficiency-risk hypothesis that predicts a statistically significant positive relationship. Consequently, the result supports the franchise-value hypothesis indicating that more efficient firms tend to choose relatively low debt ratio to protect future income derived from higher efficiency. On the other hand, high income generated on higher technical efficiency may enhance the internally generated capital surplus of the firm. Thus, the recorded negative relationship supports the Pecking Order Theory (Jensen & Meckling, 1976). Further evidence to support the Pecking Order Theory is provided by the estimated coefficients for profitability. The firm's profitability records a statistically significant negative relationship with all leverage ratios.
We used market share to proxy the political power of individual firms. The estimated coefficients for the market share records statistically significant negative coefficients for all regression specifications. According to the result, firms with relatively higher market share tend to use a lower level of debt (either short term or long term). This is due to two reasons. The first, firms with high market share may use its market power to set the prices thereby earning an abnormal profit than other firms. This abnormal profit may lead to internally generated funds. The second reason is China's specific financial environment.
We used market concentration to find the impact of industry concentration on a firm's debt level. The result indicates firms in highly concentrated industries have paid more emphasis on long-term debt and lesser significance to short-term debt. Overall, this result rejects the predicted relationship in the political cost hypothesis. Further, the finding emphases the relevancy of the Industrial Organization Theory (Plott, 1982) to explain the firm's capital structure decisions.
Along with the main test variables, the model also included a vector of control variables. Among them, size and asset tangibility show a statistically significant positive relationship with long-term and total-debt ratio. The other control variables show negative relationships. Only the assets' tangibility has shown a different relationship with the short-term debt ratio.
A firm's size indicates its ability to provide collateral for securing debt finance. Further, a larger firm may be able to provide more firm-related information to the market, creating information asymmetry. Relatively large firms may have a better reputation in the market. Titman and Wessels (1988) explained that the bankruptcy cost and the size of a firm have a negative correlation and thereby have a positive relationship between leverage and firm size. Therefore, previous researchers predicted a positive relationship between market value of the firm and leverage would occur (Chen and Strange, 2005). Especially in China, large firms may have easier access to debt financing than smaller firms. In our analysis, we found a statistically significant positive relationship between the firm's size and its leverage. Our result is consistent with the findings of Li, Yue and Zhao (2009) and Huang and Song (2006).
We used the fixed assets-to-total assets ratio to measure the tangibility of a firm's assets. Firms can use their fixed assets as collateral to secure debt financing. Myers (1977) stressed the significance of the firm's asset structure in raising debt capital. Firms can use their tangible assets as securities when raising low-cost, secured debt (Titman & Wessels, 1988). According to Harris and Raviv (1990), firms that have a higher liquidation value of tangible assets have more debt than other firms. Our study confirmed both theoretical predictions by recording statistically significant positive coefficients for variables used to represent asset tangibility with long-term debt and total-debt ratios. Furthermore, the regression analysis, which used short-term leverage ratio as the dependent variable, records a statistically significant, negative coefficient for the assets' tangibility. Overall, these results indicate that firms with a higher level of tangible assets tend to have more long-term debt and less short-term debt in their capital structure. The result reveals that firms with a higher level of tangible assets are able to substitute high cost, short-term debt with the low cost long-term debt by using their capacity to use such assets as collateral when raising long-term debt. This finding also suggests that firms tend to match the maturities of their funding arrangement with their asset structures as indicated by Mayers (1977).
As explained by Mayers' (1997) Pecking Order Theory, debt capital is less attractive than retained earnings but more attractive than equity financing. Because firms that experience growth are able to accumulate internally generated funds they may tend to use a lower level of debt financing. On the other hand, growth firms may be subjected to the information asymmetry (Myers & Majluf, 1984). Thus, they may have very little incentive to use new equity financing to fund new projects. This study used the total asset growth rate to proxy a firm's growth. We found a statistically, significant negative relationship between a firm's corporate debt ratio and the growth of its assets.
The concept of a stock market is new to the Chinese corporate world. As shown in the Table 2, we use the percentage of outstanding shares (the percentage of shares trading in the secondary equity market) to identify the extent of using capital markets to finance a firm's capital requirements. According to Chen (2004), the substantial capital gains acquired on secondary market force listed companies to use the capital market for equity financing. Descriptive statistics presented in the Table 1 show that, out of all of the issued shares, only 13.5% of those shares are traded in the market. The study found a statistically significant negative coefficient for the variable used for representing the use of capital market. This result suggests that an increase in the use of the capital market for equity financing reduces the firm's use of debt in finance structure
Growth opportunities can be considered as future capital assets that cannot be used as collateral for lending agreements (Titman and Wessels, 1988). Firms with growth opportunities may have incentives to invest in a suboptimal manner. There are two competing argument on the impact of growth potential on a firm's financing decisions. As explained by Myers (1977), since risky debt may capture enough of the benefit from the exercise of growth potential, managers act to maximize equity value rather than total firm value. Consequently, managers have an incentive to underinvest in future growth opportunities. Therefore, a firm's leverage and growth potential may have a negative relationship. On the other hand, growth potential may offer incentive to managers to over-invest in future growth options (Jensen and Meckling, 1976), resulting in more demand for the debt capital. We used market-to-book value to proxy the firm's growth potential (Mayers 1977) and found a statistically significant positive relationship with the short-term debt ratio and with the total debt ratio. However, the study does not provide any evidence on the relationship between long-term debt and growth potential. Nevertheless, this result can be interpreted as supportive evidence of the first argument. When firms have growth opportunity, they may tend to use short-term sources of debt to avoid possible under investment problems on the expectation of raising long-term capital in the future.
The non-debt tax shield is the present value of the tax benefits on depreciation and investment tax credit. The non-debt tax shield can be regarded as a substitute for the tax benefit on debt interest payments. The significance of the interest tax shield entirely depends on the availability of the non-debt tax shield (DeAngelo & Masulis, 1980). Thus, the advantage of debt financing may be reduced when the other form of non-debt tax shield exists. The estimated coefficients for the non-debt tax shield are negative and statistically significant for all leverage ratios. This evidence confirmed DeAngelo and Masulis' explanation of the impact of the no-debt tax shield.
The results presented in the Table 4 considered the time variant and industry variant unobservable impact of debt financing. The sign of the estimated coefficients for the main test variables and the other control variables are not significantly different to the results presented in the table 3. We used six dummy variables to investigate the impact of time invariant unobservable factors on firm leverage and 21 dummy variables to represent industry influence. We found very little evidence to support the time variant impact on long term and short term debt. However, the study produces some evidence to support the existence of differences in total debt ratio.
Results presented in the Table 4 show the estimated coefficient for equation two with the industry and time dummies. The results indicate that firms in industries such as food production, forestry and paper, leisure goods, software and computer services and support services are using a relatively higher level of debt in their capital structure. On the other hand, industries such as general retailers, industrial engineering and industrial transport are using a relatively lower level of debt in their capital structure. The results reveal that the firms' choices of long-term and short-term debt are not similar. Estimated coefficients for time dummies record significant differences in corporate debt levels during the latter part of the sample period, indicating an increase in use of short-term debt financing. This result indicated that the expansion of Chinese industry has forced Chinese firms to rely on short-term, debt financing sources.
This study aimed to provide further evidence on the determinants of capital structure of listed companies in China. The study model included three set of variables. The first set, main test variables, was used to identify the influence of a firm's relative efficiency and market structure on its financial leverage. Our study found evidence to support the Franchise Value Hypothesis and concluded that efficient firms tend to have a lower level of corporate leverage. Furthermore, the variable used for representing a firm's relative market power has recorded a statistically significant negative relationship. These results are consistent with the Pecking Order Theory. On one side, efficiency may improve the firm's ability to generate internal capital. On the other hand, a firm can use its power in the market to set prices. Both actions will increase the firm's ability to generate internal capital and reduce the reliance on external debt. We also use another two sets of variables to represent the other control variables and the industry and time variant factors. Those results are more consistent with the findings of previous studies.
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(1.) The input-oriented model identifies technical inefficiency as a proportional reduction in input usage for a given level of output (Coelli, Rao and Battese (1998).
(2.) Constant Return to Scale DEA model (Charnes, Cooper and Rhodes 1978)
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
Where, [y.sub.rj] is the amount of [r.sup.th] output produced by DMU 'j' using [x.sub.ij] amount of 'I the input. '[theta]' denote the CCR efficiency of DMU 'j". Both [y.sub.rj] and [x.sub.ij] are exogenous variables and '[[lambda].sub.j]' represents the intensity variables assigned to each DMU under observation.
(3.) C[R.sub.k] represents the total market shares of the kth largest or dominating firms in the market and it ignores relatively small firms in the market (Bikker and Haaf 2002; Worthington et al. 2001). HHI represents the sum of the squares of the market shares of all firms within a given industry. Since, HHI accounts all firms in the industry it avoids an arbitrary cut-off (Bikker and Haaf 2002).
HHI = [N.summation over (i=1)] = Number of firm, [v.sub.i] = market share of ith firm, V =Total market share
SENARATH LALITHANANDA SEELANATHA
Centre for Strategic Economic Studies-Victoria University
Table 1: Descriptive statistics 1999 2000 2001 2002 Long term debt to 0.050 0.053 0.063 0.055 total assets [0.078] [0.076] [0.249] [0.083] Short term debt to 0.161 0.159 0.166 0.173 total assets [0.141] [0.152] [0.127] [0.145] Total debt to 0.209 0.211 0.228 0.228 total assets [0.154] [0.165] [0.271] [0.159] Technical efficiency 0.786 0.827 0.826 0.842 [0.182] [0.163] [0.174] [0.157] Market share Sale 0.031 0.028 0.027 0.026 [0.051] [0.044] [0.044] [0.043] Concentration Sale 0.077 0.078 0.072 0.072 [0.065] [0.054] [0.050] [0.047] Size 13.898 14.032 14.130 14.201 (LN[Total assets]) [0.837] [0.823] [0.855] [0.871] Asset tangiblity 0.348 0.328 0.341 0.356 (Fixed assets to [0.180] [0.179] [0.189] [0.193] total assets) Profitability 5.729 4.962 3.611 2.741 [6.074] [5.478] [5.987] [8.702] Asset growth 15.458 16.604 21.622 23.197 [16.930] [21.287] [50.123] [62.545] Use of capital market 0.100 0.112 0.122 0.127 (Outstanding share) [0.092] [0.120] [0.125] [0.128] Growth potential 1.266 2.243 2.187 2.423 (Market value to [4.205] [10.136] [8.039] [7.962] book value) Tax-shield 7.764 7.232 8.063 8.512 [1.506] [1.912] [1.693] [1.688] 2003 2004 2005 Grand Long term debt to 0.061 0.064 0.065 0.059 total assets [0.088] [0.094] [0.094] [0.123] Short term debt to 0.179 0.184 0.191 0.174 total assets [0.134] [0.147] [0.147] [0.142] Total debt to 0.240 0.248 0.255 0.233 total assets [0.153] [0.165] [0.163] [0.181] Technical efficiency 0.836 0.792 0.772 0.813 [0.148] [0.165] [0.178] [0.168] Market share Sale 0.024 0.022 0.024 0.026 [0.047] [0.041] [0.045] [0.045] Concentration Sale 0.070 0.078 0.088 0.076 [0.045] [0.047] [0.050] [0.051] Size 14.270 14.341 14.488 14.210 (LN[Total assets]) [0.931] [0.969] [0.981] [0.918] Asset tangiblity 0.360 0.368 0.371 0.354 (Fixed assets to [0.201] [0.201] [0.203] [0.194] total assets) Profitability 3.347 3.215 8.456 4.343 [6.326] [10.124] [7.663] [7.731] Asset growth 18.972 19.418 12.900 18.526 [26.589] [55.319] [14.133] [41.271] Use of capital market 0.134 0.162 0.177 0.135 (Outstanding share) [0.157] [0.247] [0.267] [0.179] Growth potential 2.121 1.760 0.691 1.839 (Market value to [8.017] [6.656] [3.385] [7.326] book value) Tax-shield 8.614 8.778 7.159 8.049 [1.779] [1.719] [3.978] [2.317] [Standard deviations are in parentheses] Table 2: Correlation coefficient Long term Short term Total Technical debt debt debt efficiency Short term debt -0.081 Total debt 0.620 0.729 Technical efficiency -0.010 -0.265 -0.213 Market share Sale 0.009 -0.119 -0.087 0.099 Concentration Sale 0.029 -0.062 -0.027 -0.013 Size 0.154 -0.127 0.005 0.084 Asset tangibility 0.249 -0.120 0.075 -0.069 Profitability 0.024 -0.361 -0.266 0.362 Assets Growth 0.137 0.188 0.241 -0.226 Use of capital market 0.091 -0.103 -0.018 0.046 Growth potential -0.022 0.074 0.043 -0.005 Tax-shield 0.163 -0.163 -0.043 0.081 Market share Concentration Sale Sale Size Short term debt Total debt Technical efficiency Market share Sale Concentration Sale 0.236 Size 0.511 0.091 Asset tangibility -0.002 0.152 0.192 Profitability 0.073 0.025 0.146 Assets Growth -0.080 -0.006 -0.170 Use of capital market 0.323 0.063 0.553 Growth potential 0.026 -0.031 -0.017 Tax-shield 0.258 0.065 0.475 Asset As sets tangibility Profitability Growth Short term debt Total debt Technical efficiency Market share Sale Concentration Sale Size Asset tangibility Profitability 0.067 Assets Growth -0.056 -0.550 Use of capital market 0.131 0.083 -0.050 Growth potential -0.039 -0.044 -0.030 Tax-shield 0.326 -0.005 -0.113 Outstanding Market to share book value Short term debt Total debt Technical efficiency Market share Sale Concentration Sale Size Asset tangibility Profitability Assets Growth Use of capital market Growth potential -0.035 Tax-shield 0.288 0.062 Table 3: Determinants of capital structure Specification 1 Long term Short term total leverage leverage leverage (Constant) -0.320 ** 0.540 *** 0.219 *** [-10.12] [15.25] [4.78] Technical efficiency -0.012 -0.215 *** -0.226 *** [-1.16] [-18.58] [-15.06] Market share Sale -0.280 *** -0.133 *** -0.413 *** [-6.06] [-2.56] [-6.17] Concentration Sale 0.083 ** -0.133 *** -0.046 [2.38] [-3.41] [-0.91] Size_Total asset 0.027 *** -0.012 *** 0.015 *** [12.51] [-5.08] [4.69] Asset tangibility Profitability Assets Growth Use of capital market Growth potential Non- debt tax-shield F 40.368 115.451 70.916 Durbin-Watson 1.944 1.905 1.971 Specification 2 Long term Short term total leverage leverage leverage (Constant) -0.357 *** 0.237 *** -0.120 ** [-13.20] [5.68] [-2.47] Technical efficiency -0.010 -0.134 *** -0.142 *** [-1.29] [-10.74] [-9.79] Market share Sale -0.245 *** -0.245 *** -0.493 *** [-7.50] [-4.85] [-8.40] Concentration Sale 0.012 -0.055 -0.039 [0.47] [-1.43] [-0.87] Size_Total asset 0.028 *** 0.012 *** 0.040 *** [14.34] [3.90] [11.29] Asset tangibility 0.150 *** -0.063 *** 0.085 *** [21.80] [-5.94] [6.88] Profitability -0.001 *** -0.005 *** -0.005 *** [-3.56] [-10.39] [-10.89] Assets Growth -1.631E-04 *** -4.094E-04 *** -0.001 *** [-2.77] [-4.49] [-5.40] Use of capital market -0.007 -0.039 *** -0.045 *** [-0.86] [-3.04] [-3.02] Growth potential -1.86E-04 1.46E-03 *** 1.27E-03 *** [-1.20] [6.08] [4.53] Non- debt tax-shield -0.002 *** -0.008 *** -0.010 *** [-3.14] [-7.83] [-8.41] F 102.965 56.582 55.368 Durbin-Watson 1.862 1.94 1.909 't'--scores values are in the parenthesis,' ***' indicates significant coefficients under 1 % confidence level,' **' indicates significant coefficients under 5% confidence level,' *' indicates significant coefficients under 10% confidence level) Table 4: Determinants of capital structure Long term leverage (Constant) -0.310 *** [-10.87] Technical efficiency -0.023 ** [-2.43] Market share-Sale -0.198 *** [-5.55] Concentration -Sale -0.099 * [-1.74] Size_ Total assets 0.026 *** [12.61] Asset tangibility 0.145 *** [19.27] Profitability -0.001 *** [-2.50] Assets Growth -1.00E-04 * [-1.70] Use of capital market -0.014 * [-1.70] Growth potential -8.48E-05[-0.56] Non-debt tax-shield -0.001 ** [-2.22] ID_ Chemicals 0.008[1.12] ID_ Construction & Materials 0.023 *** [2.86] ID_ Electricity 0.046 *** [5.37] ID_ Electronic, Electrical Equip. -0.006[-0.72] ID_ Food Producers -0.017 ** [-2.02] ID_ Forestry & Paper 0.066 *** [6.33] ID_ General Industrials -0.010[-1.02] ID_ General Retailers -0.044 *** [-5.64] ID_ Household Goods -0.016[-1.29] ID_ Industrial Engineering -0.020 *** [-2.56] ID_ Industrial Metals 0.001 *** [0.08] ID_ Industrial transport -0.021 ** [-2.31] ID_ Leisure Goods 0.009[0.54] ID_ Mining -7.40E-05[-0.01] ID_ Personal Goods -0.012[-1.44] ID_ Pharmaceuticals, Biotechnology -0.012[-1.61] ID_ Real Estate 0.018 ** [2.37] ID_ Software & Computer Services 0.007[0.65] ID_ Support Services 0.006[0.65] ID_ Technology Hardware & Equip. -0.005[-0.58] ID_ Travel & Leisure 0.022 * [1.65] TD_2000 0.004[0.79] TD_2001 0.002[0.34] TD_2002 -0.001[-0.25] TD_2003 0.002[0.51] TD_2004 0.004[0.85] TD_2005 3.48E-04[0.07] F 37.292 Durbin Watson 1.996 Short term leverage (Constant) 0.181 *** [4.10] Technical efficiency -0.180 *** [-12.41] Market share-Sale -0.388 *** [-7.01] Concentration -Sale -0.102[-1.15] Size_ Total assets 0.016 *** [5.16] Asset tangibility -0.036 *** [-3.11] Profitability -0.005 *** [-10.06] Assets Growth -4.32E-04 *** [-4.73] Use of capital market -0.038 *** [-3.00] Growth potential 0.001 *** [6.16] Non-debt tax-shield -0.006 *** [-6.25] ID_ Chemicals 0.003[0.29] ID_ Construction & Materials 0.007[0.55] ID_ Electricity -0.065 *** [-4.83] ID_ Electronic, Electrical Equip. 0.013[1.05] ID_ Food Producers 0.066 *** [4.98] ID_ Forestry & Paper 0.059 *** [3.67] ID_ General Industrials 0.038 *** [2.45] ID_ General Retailers -0.012[-0.97] ID_ Household Goods 0.029[1.50] ID_ Industrial Engineering -0.018[-1.49] ID_ Industrial Metals 0.018[1.47] ID_ Industrial transport -0.074 *** [-5.26] ID_ Leisure Goods 0.066 *** [2.47] ID_ Mining 0.018[1.05] ID_ Personal Goods 0.030 ** [2.39] ID_ Pharmaceuticals, Biotechnology 0.020 * [1.70] ID_ Real Estate 0.005[0.40] ID_ Software & Computer Services 0.059 *** [3.68] ID_ Support Services 0.065 *** [4.27] ID_ Technology Hardware & Equip. 0.024 * [1.85] ID_ Travel & Leisure 0.035 * [1.71] TD_2000 -0.009[-1.19] TD_2001 -0.002[-0.29] TD_2002 0.004[0.61] TD_2003 0.014 * [1.94] TD_2004 0.011[1.60] TD_2005 0.030 *** [3.79] F 24.186 Durbin Watson 2.067 Total leverage (Constant) -0.128 *** [-2.49] Technical efficiency -0.201 *** [-11.94] Market share-Sale -0.586 *** [-9.13] Concentration -Sale -0.196 * [-1.91] Size_ Total assets 0.042 *** [11.37] Asset tangibility 0.107 *** [7.88] Profitability -0.005 *** [-10.04] Assets Growth -0.001 *** [-5.06] Use of capital market -0.051 *** [-3.45] Growth potential 0.001 *** [4.96] Non-debt tax-shield -0.008 *** [-6.59] ID_ Chemicals 0.012[0.90] ID_ Construction & Materials 0.030 * [2.08] ID_ Electricity -0.019[-1.24] ID_ Electronic, Electrical Equip. 0.006[0.46] ID_ Food Producers 0.049 *** [3.17] ID_ Forestry & Paper 0.125 *** [6.69] ID_ General Industrials 0.027[1.53] ID_ General Retailers -0.056 *** [-4.03] ID_ Household Goods 0.012[0.55] ID_ Industrial Engineering -0.038 *** [-2.71] ID_ Industrial Metals 0.019[1.33] ID_ Industrial transport -0.095 *** [-5.80] ID_ Leisure Goods 0.075 ** [2.39] ID_ Mining 0.018[0.90] ID_ Personal Goods 0.017[1.17] ID_ Pharmaceuticals, Biotechnology 0.008[0.57] ID_ Real Estate 0.020[1.54] ID_ Software & Computer Services 0.065 *** [3.51] ID_ Support Services 0.069 *** [3.94] ID_ Technology Hardware & Equip. 0.018[1.23] ID_ Travel & Leisure 0.056 ** [2.38] TD_2000 -0.004[-0.47] TD_2001 0.002[0.21] TD_2002 0.006[0.66] TD_2003 0.019 ** [2.25] TD_2004 0.018 ** [2.16] TD_2005 0.032 *** [3.59] F 24.566 Durbin Watson 2.059 't' scores values are in the parenthesis, '***' indicates significant coefficients under 1% confidence level, '**' indicates significant coefficients under 5% confidence level, '*' indicates significant coefficients under 10% confidence level
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|Author:||Seelanatha, Senarath Lalithananda|
|Publication:||Economics, Management, and Financial Markets|
|Date:||Dec 1, 2010|
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