Debt maturity structure of Chinese companies

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Pacific-Basin Finance Journal 16 (2008) 268 – 297 www.elsevier.com/locate/pacfin

Debt maturity structure of Chinese companies ☆ Kailan Cai a , Richard Fairchild b , Yilmaz Guney c,⁎ b c

a Bank of New York, Hong Kong University of Bath, United Kingdom University of Hull, United Kingdom

Received 25 January 2007; accepted 9 June 2007 Available online 19 June 2007

Abstract Numerous studies have focused on the theoretical and empirical aspects of corporate capital structure since the 1960s. As a new branch of capital structure, however, debt maturity structure has not yet received as much attention as the debt-equity choice. We use the existing theories of corporate debt maturity to investigate the potential determinants of debt maturity of the Chinese listed firms. In addition to the traditional estimation methods, the system-GMM technique is used to explicitly control for the endogeneity problem. We find that the size of the firm, asset maturity and liquidity have significant effects in extending the maturity of debt employed by Chinese companies. The amount of collateralized assets and growth opportunities also tend to be important. However, proxies for a firm's quality and effective tax rate apparently report mixed or unexpected results. Debt market and equity market conditions are also examined in relation to corporate loan maturity. The system-GMM results show that market factors seem to influence debt maturity decisions. Finally, corporate equity ownership structure has also been found to have some impact on debt maturity mix. © 2007 Elsevier B.V. All rights reserved. JEL classification: G3; G32 Keywords: Chinese firms; Corporate debt maturity; System-GMM; Market conditions; Ownership structure

1. Introduction Since the seminal works of Modigliani and Miller, the capital structure of corporations has attracted intense scrutiny. Recently, researchers have been examining how a firm's debt maturity ☆ The views and opinions expressed in this paper belong to the authors and do not necessarily represent those of Bank of New York. The authors thank the editor (Jun-Koo Kang) and an anonymous referee for their helpful comments. ⁎ Corresponding author. Tel.: +44 1482 46 3079. E-mail address: [email protected] (Y. Guney).

0927-538X/$ - see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.pacfin.2007.06.001

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mix, the choice between short-term and long-term debt, is determined. There are several reasons why debt maturity structure might be important. For instance, the liability structure of firms may be aligned to their asset structure in order to avoid possible corporate liquidations. A firm might choose debt maturity policy to address agency problems. Furthermore, firms can signal the quality of their earnings by choosing a specific maturity mix. Moreover, corporate debt maturity matters if firms happen to consider flexibility in financing, cost of financing, and refunding risk. Diamond and Rajan (2001) also emphasize its importance with reference to credit availability and financial crises. The theories of corporate debt maturity structure were first designed during the 1980s and early 1990s (Barnea et al., 1980; Brick and Ravid, 1985; Flannery, 1986; Lewis, 1990; Diamond, 1991). The theories based on signaling (Flannery, 1986; Kale and Noe, 1990) and agency costs (Myers, 1977; Barnea et al., 1980) favor the use of short-term debt. The tax-based theories show the benefit of long-term debt (Brick and Ravid, 1991). The empirical tests of debt maturity structure of US firms started during the mid 1990s (Barclay and Smith, 1995; Guedes and Opler, 1996; Stohs and Mauer, 1996) and the research continues (Johnson, 2003; Berger et al., 2005; Datta et al., 2005; Billett et al., 2007). Recently, researchers have focused on the determinants of corporate debt maturity structure in Western Europe (Ozkan, 2000; Antoniou et al., 2006) and in Japan (Cai et al., 1999). The existing studies of empirical debt maturity structure have predominantly focused on developed countries. Except Arslan and Karan's (2006) work on Turkish firms, we are not aware of any other published work that examines the debt maturity mix of firms in a developing country. We aim to fill this gap in the literature. The objective of this study is, therefore, to investigate the potential determinants of debt maturity structure of Chinese listed companies in three stock exchanges in China for the period 1999–2004.1 As the biggest emerging and transition economy in the world, China has experienced high rates of economic growth for over 10 years. In contrast to most advanced countries, however, China has not yet established sophisticated and mature capital markets to support the funding requirement of companies and entrepreneurs. The dominant source of financing is still financial institution loans. The National Bureau of Statistics of China reports that the average ratio of total financial institution loans to GDP is 109.48% for the period 1994–2004, which is much higher than the proportions of stocks (1.12%), corporate bonds (0.27%) and government bonds (4.27%).2 The corporate bond market and stock market in China provide limited financing support relative to the role of financial institutions. Furthermore, China's corporate governance structure, institutional structure, banking system and the legal environment are argued to be different from developed countries (see e.g., Chen et al., 2006; Fan et al., 2007; Gul, 1999; Lam and Du, 2004). This would have different implications for the severity of agency problems, information asymmetries, bankruptcy procedures and taxation. What is more, most Chinese firms were controlled by the state and government ownership is still prevalent, which may provide a manager–owner relation that is different from other countries. Consequently, it is interesting to apply the debt maturity theories that were designed especially with respect to the specificities for advanced economies to the companies in an emerging market.3 1

The structure of the three exchanges and three kinds of shares in China are introduced in Appendix A. Appendix A shows all the relevant country-level aggregate data. 3 There is a wealth of evidence arguing that firms’ financing decisions are affected by some country-specific factors. For a detailed discussion, see Antoniou et al. (2006), Booth et al. (2001), La Porta et al. (1997) and Rajan and Zingales (1995), among others. 2

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Our empirical analysis reveals that firm size, asset maturity and the liquidity factors tend to be significant in explaining debt maturity mix, consistent with the predictions of maturity theories. Given the estimated positive relation between maturity and growth opportunities, we demonstrate that the overinvestment problem has been paid more attention and is more discernible than the underinvestment problem. The findings based on traditional estimation methods weakly support the theories concerning effective tax rate and volatility in earnings. However, once the endogeneity problem (correlation between the error term and regressors) is tackled by the system-GMM method, the expected negative and significant impact of tax rate on debt maturity is found. In addition, our model does not provide any meaningful support to Flannery's (1986) separating equilibrium theory. The results are generally robust to partitioning the sample into several groups with respect to firm size, firm growth, debt maturity orientation, industrial classification, and to alternative variable measures. Furthermore, we attempt to investigate the effects of money market and capital market conditions with the inclusion of the term structure of interest rates, stock market volatility, interest rate volatility, and market equity premium factors in the model. The system-GMM findings reveal that lower volatility in interest rates and in the stock market lengthens debt maturity. Other market factors (i.e., term structure of interest rates and market equity premium) significantly affect the choice between short and long-term loan as they shorten debt maturity. These findings are a bit surprising due to the notion that Chinese corporate sector and economy is yet to be market oriented. As a final robustness check, we examine whether corporate ownership structure exerts any influence on debt maturity decisions of Chinese firms. It is found that individual shareholders prefer short-term debt and firms with higher share ownership concentration tend to opt for shortermaturity debt. The remainder of this paper is organized as follows. In Section 2, we review the theories of debt maturity and the relevant empirical hypotheses. In Section 3 we describe the sample. The methodology is described in Section 4. In Section 5, we analyze the results of the individual year tests, general model tests, individual industry tests and robustness tests. Section 6 concludes the paper. 2. Debt maturity theories and empirical proxies In this section, we consider debt maturity theories in order to derive our dependent and explanatory variables. 2.1. A brief review of empirical tests of debt maturity Researchers first examined corporate debt maturity by considering a few determinants in isolation. Titman and Wessels (1988) indicate that small firms tend to use more short-term debt than larger firms. Mitchell (1993) reports three findings: Debt maturity is negatively related to a firm's business risks; firms with many growth opportunities tend to issue short-term debt; firms with high-quality projects may choose short-term debt. Kim et al. (1995) argue that debt maturity increases with the volatility of interest rates. Since the late 1990s, more direct tests of debt maturity have begun to emerge, which consider various determinants simultaneously. Barclay and Smith (1995) finds that: a) large firms have few growth options, and tend to be financed by long-term debt; b) firms with information asymmetries tend to issue short-term debt, but there is little support for firms using debt maturity to signal their quality; c) corporate tax is insignificantly related to debt maturity. Stohs

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and Mauer (1996) conclude that: a) debt maturity is positively related to firm size, asset maturity, and leverage, and negatively related to earnings volatility, effective tax rate, and firm quality; b) debt maturity is non-monotonically related to bond ratings, which is consistent with Diamond's (1991) theory; c) growth opportunities are insignificantly or positively related to debt maturity. Guedes and Opler (1996) find that: a) debt maturity has a positive relation with asset duration, corporate tax rate, and firm size; b) it is negatively correlated to growth opportunities and earnings volatility. Antoniou et al. (2006) propose that debt maturity is related to the firm-specific, country-specific and macroeconomic factors. They argue that the debt maturity theories are consistent with their UK findings but the results for French and German firms lend mixed support to those theories. 2.2. The dependent variable In our model, the dependent variable is debt maturity, DEBTM. Antoniou et al. (2006) and Barclay and Smith (1995), among other, use the ratio of long-term debt to total debt to measure the debt maturity. They consider long-term debt firstly as debt with a maturity of more than one year, and then as debt with a maturity of more than three years. Stohs and Mauer (1996) employ a weighted method to calculate all debts, debt-like obligations (e.g., capital leases), and current liabilities. We use the ratio of long-term debt to total debt to measure the debt maturity.4 2.3. Explanatory variables Similarly to Antoniou et al. (2006) and Stohs and Mauer (1996), we divide the main debt maturity theories into four categories: agency costs, signaling and liquidity risks, matching and tax effect theories. Under each theory, we discuss the corresponding proxies and define their measurement to test the theories. 2.3.1. Agency theories Underinvestment problem. Myers (1977) argues that if a firm is financed by risky debt, managers who act in equityholders' interests may refuse to take projects with positive NPV because they want to reduce the higher probability of default in risky debt. He argues that this underinvestment incentive can be controlled by issuing short-term debt which matures before the investment option is exercised. Barnea et al. (1980) agree with Myers' approach to eliminate underinvestment by short-term debt. Furthermore, they argue that both shortening debt maturity and issuing long-term debt with a call provision have identical effects in eliminating this agency cost. Datta and Iskandar-Datta (2000) examine a sample of US bond-IPOs from 1971 to 1994 and find a negative relation between debt maturity and future growth opportunities. Overinvestment problem.Hart and Moore (1995) argue that long-term debt can control management's overinvestment problem when firms have future growth opportunities. They argue that if firms have little or no long-term debt, managers have more incentives to invest in negative NPV projects to get more perquisites. They conclude that the optimal debt maturity may be derived from the trade-off between costs and benefits of short-term debt.

4 Long-term debt (LTD) is calculated as total debt minus short-term debt and current portion of LTD. The short-term debt and current portion of LTD is the portion of debt payable within one year including the current portion of LTD and sinking fund requirements of preferred stock or debentures.

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2.3.2. Proxies for agency theories Growth opportunities. We measure a firm's growth opportunities by the variable GROW, which is the book value of total assets plus the market value of equity minus the book value of equity, scaled by the book value of total assets. In the underinvestment theory, if growth opportunities are high, a firm should use more short-term debt. In the overinvestment theory, long-term debt can help to control the overinvestment behavior of management, which means the sign of GROW should be positive. Our empirical hypothesis, therefore, is that DEBTM is directly or inversely related to the GROW. Firm size. Warner (1977) finds that the ratio of bankruptcy costs to firm value tends to decrease as the firm size increases. Titman and Wessels (1988) suggest that small firms tend to be financed by short-term debt because they may face high transaction costs when they issue long-term debt or equity. We measure a firm's size (SIZE) by the natural logarithm of its total sales in 1999 prices. In robustness tests, we use the natural logarithm of the total assets (LN (TA)) and that of the market value (LN (MV)) to measure firm size. We expect that debt maturity is directly related to firm size. 2.3.3. Signaling and liquidity risk Separating equilibrium.Flannery (1986) argues that if the market cannot distinguish between good firms and bad firms, good firms may choose to issue short-term debt to signal their quality. This happens if long-term debt faces higher credit deterioration than short-term debt, and only good firms can afford the positive transaction costs of rollover of short-term debt. Extending Flannery's work, Kale and Noe (1990) indicate that even without the transaction costs in choosing debt maturity, Flannery's separating equilibrium may still exist. They argue that if the changes in firm value are positively correlated, good firms will issue short-term debt and bad firms will issue long-term debt. Titman (1992) also extends Flannery's separating equilibrium. Departing from Flannery's work, he includes interest rate uncertainty and financial distress costs. He argues that firms with a favorable future may borrow short-term debt and swap the floating-rate obligation for the fixedrate obligation in order to achieve the optimal financing structure. Control rents and liquidity risk.Diamond (1991) indicates the optimal debt maturity is attained by trading off between the benefit of short-term debt and liquidity risk.5 He argues that if control rents are very high, borrowers may issue long-term debt to avoid high liquidation costs. Shortterm debt is used to address the information sensitivity. Furthermore, he proposes that there is a non-monotonic relationship between debt maturity and the borrower's credit rating. Firms with very high and very low credit ratings choose short-term debt, and firms with medium credit rating tend to choose long-term debt. 2.3.4. Proxies for signaling and liquidity risk theories Firm quality. We do not examine Diamond (1991) proposition that debt maturity and credit ratings are non-monotonically related because most of the Chinese corporate bonds do not have standard international credit ratings. We test Flannery (1986) separating equilibrium theory (i.e., only high quality firms can issue short-term debt). Following Stohs and Mauer (1996), a

5 The benefit of short-term debt is the benefit from the improvement of credit rating after good news arrives in the future. The liquidity risks are risks for borrowers to lose their control rents due to lenders' rejection in providing refinancing.

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firm's quality (QUAL) is measured as the ratio of changes of earning per share between time t and time t + 1, scaled by the stock price at time t. We chose this approach because we deem that in an environment with information asymmetries, insider's private information can be measured by changes of a firm's future earnings. Hence, the empirical hypothesis is that DEBTM is inversely related to QUAL. Moreover, in the robustness test, we replace QUAL with the profitability ratio (PROFIT), which is earnings before interest and tax (EBIT) divided by total assets. Liquidity. Myers and Rajan (1998) introduced a paradox theory of liquid assets. Intuitively, highly liquid firms should have ample cash flows to repay their debt. Thus, a firm with a large amount of liquid assets should easily obtain external financing. However, the authors also emphasize the importance of illiquid assets used in the core business. They argue that because illiquid assets ‘are there’, this gives creditors more time to assess their value and risk and consequently the firm with certain amount of illiquid assets may find it much easier to issue longterm debt. On the other hand, Morris (1992) argues that firms with longer maturity hold greater liquidity in case they cannot meet the fixed payments of long-term debt during economic recessions. We measure liquidity (LIQUID) by current assets to current liabilities ratio. In robustness tests, we replace LIQUID with current assets to total assets ratio (CA/TA) and by the collateral ratio of net fixed assets to total assets (FA/TA). We expect that DEBTM is directly related to LIQUID (ability to meet short-term liabilities) and FA/TA (proportion of illiquid assets); and inversely linked to CA/TA (proportion of liquid assets). Leverage ratio.Morris (1992) argues that long-term debt may help firms to postpone the exposure to bankruptcy risk; therefore, high leverage firms tend to use long-term debt. Stohs and Mauer (1996) indicate that a large proportion of long-term debt inevitably produces a higher value for average debt maturity. Leland and Toft (1996) conclude that the leverage level relies on the debt maturity, and firms with lower leverage level tend to be financed by short-term debt. On the contrary, Dennis et al. (2000) show that the leverage is inversely related to debt maturity by employing simultaneous equation regressions. They argue that this happens because agency costs of underinvestment may be limited by reducing leverage and shortening debt maturity. We measure leverage (LEVER) by the ratio of the book value of total debt divided by the book value of total assets. DEBTM may be positively or inversely related to LEVER as the literature presents conflicting arguments. Volatility in earnings.Kane et al. (1985) show, using an option valuation model, that the volatility of asset returns is inversely related to debt maturity. Sarkar (1999) finds a negative relation between risk and debt maturity. He argues that high volatility in earnings increases the probability of financial distress, which leads to high bankruptcy risk. In order to avoid this risk, firms tend to issue short-term debt. Following the method of Stohs and Mauer (1996), we measure the volatility (VOLA) by the ratio of the standard deviation of the first difference in EBIT, scaled by the average book value of assets. We predict that DEBTM is negatively related to VOLA. 2.3.5. Matching principle Myers (1977) argues that the diversification of assets may increase the amount of debt the firm can borrow. Furthermore, he indicates that assets may be regarded as the protection for the repayment of debt. In order to match assets with debt, he suggests that the exposure of debt should be reduced in parallel with the decline in the value of assets. Hart and Moore (1994) argue that assets should be matched with debt because debt should be matched either with the return streams or with the rate of depreciation of the collateral. The return streams and the collaterals can be both regarded as assets.

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Graham and Harvey (2001) conducted a survey in which they asked 392 CFOs how firms choose between short-term debt and long-term debt. They found that matching maturity between assets and liabilities was the most popular answer. 2.3.6. Proxy for matching principle Asset maturity. Following Stohs and Mauer's (1996) method, asset maturity (ASTM) was measured by the sum of the weighted maturity of current assets and the weighted maturity of fixed assets. We calculate the unweighted maturity of current assets by the ratio of current asset to cost of goods sold. This method was employed because we deem most current assets (e.g., inventories) are used to produce goods. This ratio can therefore reflect the speed of consuming current assets, which can be used to measure current asset maturity. We calculate the unweighted maturity of fixed assets by the ratio of net fixed assets to the depreciation, which is also a speed of consuming fixed assets. We then added two unweighted values together by their proportion to the sum of current assets and net fixed assets. We, therefore, expect a positive relationship between DEBTM and ASTM. As a robustness check, we also use net fixed assets scaled by depreciation expense (MATCH) to test the matching theory. We expect that MATCH is also positively related to the DEBTM. 2.3.7. Tax theories Term structure of interest rates.Brick and Ravid (1985) test the tax effects with the existence of default risks, agency costs, and a non-flat term structure of interest rates. They argue that if the term structure of interest rates is increasing, the optimal financing approach is to issue long-term debt, because the interest tax shield on debt is accelerated with interest rates, which increase the value of the firm. On the other hand, if the term structure of interest rates is decreasing, it is better to issue short-term debt at present. Apart from Brick and Ravid's (1985) work, which first examines capital structure, and then examines debt maturity, Lewis (1990) examines capital structure and debt maturity simultaneously. He finds that debt maturity does not affect firm value if the only imperfection is taxation, assuming there is no difference in the tax expenses calculated by short-term and long-term debt. Effective tax rate.Kane et al. (1985) use an option valuation model to look for the optimal debt maturity in a multi-period environment. By the trade-off between tax shield advantages and costs of bankruptcy and issuance floatation, they find that debt maturity is directly related to the issuance floatation costs, and is inversely related to the tax shield advantage (i.e. effective tax rate) and the volatility of firm value. Interest rate volatility.Kim et al. (1995) argue that firms have a tendency to issue long-term debt when interest rate volatility increases. Their argument is based on the notion that higher taxtiming option value implies higher value of the firm. They explain that the value of a tax-timing option increases with the option maturity and volatility, and obviously the long-term debt has longer maturity and more volatility than short-term debt. 2.3.8. Proxies for tax theories Effective tax rate. We measure effective tax rate (TAX) with the ratio of tax expense to pre-tax profit. Kane et al. (1985) indicate that the tax shield advantage is inversely related to debt maturity. In other words, if the effective tax rate is low, then firms prefer to issue long-term debt. Thus, we expect to find a negative relationship between DEBTM and TAX. Interest rate volatility. We measure short-term interest volatility (IRV-S) by taking the standard deviation of monthly short-term [0–6 months] lending rates over the previous year, and long-term interest volatility (IRV-L) by taking the standard deviation of long-term [5 years and longer]

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Table 1 The industrial classification of firms Industries Automobile and parts Chemicals Electricity Electronic and electrical equipment General industrials Healthcare equipment and services Industrial metals Industrial engineering Mining Oil and gas producers Oil equipment and services Technology hardware and equipment Total

Population firms

Sample firms

44 87 42 55 25 2 56 82 17 10 2 51 473

23 50 26 28 15 2 32 44 7 5 1 26 259

A and B

A and H

3 2 2 3 – – 2 8 – – – 1 21

– 1 – 1 – – 2 3 – – – 1 8

‘Population firms’ means the number of firms available in DataStream database. ‘Sample firms’ means the number of qualified firms after data filtering. A and B means the number of population firms that issue share A and share B. A and H means the number of population firms that issue share A and share H.

lending rates over the previous year. The expected relation between IRV-L (or IRV-L) and DEBTM is positive. Term structure of interest rates. The standard measure for this variable (TERM) is the difference between the month-end returns on long-term government bond and short-term treasury-bill. Due to data limitations, we used short-term and long-term lending rates as above to proxy for the government bond and t-bill rates, respectively. We predict a direct link between TERM and DEBTM. 3. The sample 3.1. Data description The data are collected from DataStream over a period of 6 years from 1999 to 2004. DataStream contains 37 industries with 1159 listed firms in three stock exchanges in China. We focus on the industrial corporate sectors only, which contains 12 industries and 473 firms. Standard data filtering has been applied. We ignored the companies that do not have complete data. One firm can issue two kinds of shares in more than one stock exchange in China. We handle this situation by merging two observations of two exchanges (e.g., share price) into one weighted observation.6 This procedure eliminated 29 firms. Some of the observations related to ASTM, LEVER and MATCH variables were either outliers or inconsistent figures, which were deleted from our data set. We were left with 259 firms and 1554 observations. Table 1 shows the structure of data. 3.2. Descriptive statistics Panel A in Table 2 reports the descriptive statistics for the variables used in the main regression model. The variables in Panel B are used to check the sensitivity of results to variable definitions. 6 For example, we calculated the combined share price by the sum of the weighted price of Share A (in RMB) and the weighted price of Share B (in US$ or HKD).

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Table 2 Descriptive statistics Variables

Mean

Minimum

Median

Maximum

Standard deviation

No. of observations

Panel A: Variables for the main model DEBTM 0.23 0.00 GROW 2.38 0.37 SIZE 13.45 6.54 QUAL 0.01 − 0.42 ASTM 8.20 0.66 TAX 0.16 − 7.04 LIQUID 1.38 0.03 VOLA 0.07 0.00

0.14 1.94 13.40 0.00 6.57 0.15 1.21 0.04

1.00 19.84 17.38 0.52 50.25 10.20 6.98 0.88

0.26 1.65 1.29 0.05 6.53 0.43 0.79 0.09

1554 1554 1554 1554 1554 1554 1554 259

Panel B: Variables for robustness check PROFIT 0.04 − 3.12 CA/TA 0.48 0.03 FA/TA 0.38 0.00 LEVER 0.29 0.00 LN(TA) 14.19 11.52 LN(MV) 14.46 11.25 MATCH 15.00 0.60

0.05 0.50 0.35 0.28 14.12 14.48 12.48

0.32 0.94 0.92 0.99 18.03 16.90 285.73

0.12 0.17 0.19 0.15 0.99 0.80 11.99

1554 1554 1554 1554 1554 1554 1554

The dependent variable (DEBTM) is the ratio of long-term debt divided by total debt. GROW is the book value of total assets plus the market value of equity minus the book value of equity, scaled by the book value of total assets. SIZE is measured by the natural logarithm of the total sales, in 1999 prices using producer price index. QUAL is the ratio of changes of earnings per share (EPS) between time t + 1 and time t, scaled by the stock price at time t. ASTM is the sum of the weighted maturity of current assets and fixed assets. TAX is the ratio of tax expense to the pre-tax profit. LIQUID is the current assets divided by current liabilities. VOLA is the ratio of the standard deviation of the first difference of earnings before interest and tax (EBIT), scaled by the average book value of assets. PROFIT is the ratio of EBIT to total assets. CA/TA is the ratio of current assets to total assets. FA/TA is the ratio of net fixed assets to total assets. LEVER is the ratio of total debt to total assets. LN(TA) is the natural logarithm of the total assets in 1999 prices. LN(MV) is the natural logarithm of the market value of the firm in 1999 prices. MATCH is the net fixed assets scaled by depreciation expenses.

The mean value for DEBTM is 0.23, which implies that short-term debt is popular among Chinese firms. Compared to the firms in developed countries, this figure is quite low (see e.g., Antoniou et al., 2006; Barclay and Smith, 1995). This could be attributed to the underdeveloped debt markets in China, and banks being the main sources of financing for firms. The variable GROW takes the average value of 2.38, which is considerably greater than the market-to-book ratio of firms in advanced economies reported by the literature. It implies that Chinese listed firms have given high expectations to their investors regarding their future prospects. Huang and Song (2006) state that Chinese firms have low long-term debt and high market-to-book ratios probably because they prefer equity financing once they get listed and the bond market is quite immature. The mean value for TAX is 0.16, which may imply that the effective tax burden is not relatively very high in China. It seems that although statutory corporate tax rates in China are very similar to the ones in developed countries Chinese entities tend to be granted more options and incentives to reduce their tax levy.7 Referring to the average figures of LIQUID and PROFIT, although Chinese firms do not seem to have a liquidity problem in the short-term, their return on assets does not look particularly high. 7 The corporate tax rate for domestic firms is currently 33%. However, their effective rate could be as low as 15% or even lower if companies operate in special economic development regions. The tax rate is 15% for firms having joint ventures with foreign firms.

Table 3 Correlation matrix GROW

SIZE

QUAL

ASTM

TAX

LIQUID

VOLA

LN(MV)

PROFIT

CA/TA

FA/TA

LEVER

LN(TA)

− 0.12 0.24 a 0.07 a 0.26 a 0.03 0.26 a − 0.15 a 0.28 a 0.14 a − 0.36 a 0.46 a 0.03 0.35 a 0.10 a

− 0.55 a − 0.06 b 0.05 b − 0.07 a 0.11 a 0.26 a 0.19 a − 0.10 a 0.04 − 0.15 a − 0.08 a − 0.59 a 0.01

0.05 b −0.22 a 0.11 a −0.02 −0.21 a 0.42 a 0.19 a 0.06 a 0.19 a −0.03 0.87 a −0.10 a

0.05 b −0.02 −0.07a −0.11 a −0.03 −0.03 −0.06 b 0.09 a 0.04 0.09 a 0.04

0.01 −0.17a −0.07 a 0.09 a 0.03 −0.52 a 0.51 a 0.13 a 0.01 0.76 a

0.00 − 0.08 a 0.07 b 0.07 a − 0.02 0.04 − 0.03 0.10 a 0.01

− 0.11 a 0.15 a 0.19 a 0.31 a − 0.19 a − 0.49 a − 0.03 − 0.09 a

− 0.11 a − 0.37 a 0.05 − 0.14 a 0.10 a − 0.25 a − 0.08 a

0.24 a −0.14 a 0.18 a −0.15 a 0.48 a 0.06 b

0.02 0.08 a −0.23 a 0.19 a 0.05 b

− 0.80 a − 0.09 a − 0.09 a − 0.21 a

0.04 0.25 a 0.24 a

0.06 b 0.07 a

− 0.01

a

The dependent variable (DEBTM) is the ratio of long-term debt divided by total debt. GROW is the book value of total assets plus the market value of equity minus the book value of equity, scaled by the book value of total assets. SIZE is measured by the natural logarithm of the total sales, in 1999 prices using producer price index. QUAL is the ratio of changes of earnings per share (EPS) between time t + 1 and time t, scaled by the stock price at time t. ASTM is the sum of the weighted maturity of current assets and fixed assets. TAX is the ratio of tax expense to the pre-tax profit. LIQUID is the current assets divided by current liabilities. VOLA is the ratio of the standard deviation of the first difference of earnings before interest and tax (EBIT), scaled by the average book value of assets. PROFIT is the ratio of EBIT to total assets. CA/TA is the ratio of current assets to total assets. FA/TA is the ratio of net fixed assets to total assets. LEVER is the ratio of total debt to total assets. LN(TA) is the natural logarithm of the total assets in 1999 prices. LN(MV) is the natural logarithm of the market value of the firm in 1999 prices. MATCH is the net fixed assets scaled by depreciation expenses. a and b shows that the correlation coefficient is significant at the 1% and 5% level, respectively.

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GROW SIZE QUAL ASTM TAX LIQUID VOLA LN(MV) PROFIT CA/TA FA/TA LEVER LN(TA) MATCH

DEBTM

277

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Finally, regarding their leverage ratio of 29%, they tend to stand between low-levered AngloSaxon firms and high-levered continental European and Japanese firms. Our descriptive statistics are quite comparable to Huang and Song (2006) who examine the determinants of capital structure of Chinese firms between 1994 and 2003. 3.3. Correlation matrix Table 3 is the correlation matrix between the variables. The signs of the correlation coefficients between the dependent variable and independent variables are generally consistent with predictions, except for the QUAL and TAX. The reasons for these unexpected signs may be that shareholder wealth is not always the key concerns of company managers and the effective tax rate is relatively low in China.8 The relation between alternative proxies is significant with high correlation coefficients, except one case. We use PROFIT to replace the QUAL in explaining a firm's quality in robustness test but the correlation coefficient is too low. It maybe that QUAL and PROFIT represent two different attributes. 4. Methodology We examine the debt maturity mix of Chinese firms using the firm-specific characteristics discussed in Section 2, and incorporate time and industry dummies into the econometric model. We adopt several regression methods and execute various tests to look at the sensitivity of regression results. 4.1. Estimation methods CLA method. This is a cross-sectional method, where all explanatory variables are lagged one period and averaged over the period. In other words, we use the figures in 2004 for the dependent variable, and take the average values for all explanatory variables over the period 1999–2003. This method has been suggested by Rajan and Zingales (1995) in order to alleviate the potential endogeneity problem. CA method. The nature of this technique is again cross-sectional, where all variables including the dependent variable are averaged over the period 1999–2004. This method uses time-series averages values of each variable based on 1554 observations for 259 firms. It helps to eliminate the problem of serially-correlated residuals, which may cause the t-statistics to be potentially overstated. Pooled, fixed-effects, and random-effects methods. The pooled method simply adds time-series and cross-sectional observations together and then uses the OLS technique. Greene (2003) indicates that the crucial point to decide which technique has the best fitness power is to examine whether there are unobserved variables, and whether these unobserved variables are correlated with the observed regressors of the model. In a simple explanation, if the regression equation embodies all variables it should contain, the best method is the common OLS. If the equation does not embody all variables (i.e., omitted variable case), and the unobserved variables are correlated with the observed variables, the best choice is fixed effects. If the equation has an omitted variable 8 Firth et al. (2006) argue that if a firm's dominant shareholder is the Chinese State, the CEO's incentive pay depends on operating income of the firm. If a firm's dominant shareholder is private one or having foreign investors, the CEO's incentive pay tends to focus on shareholder wealth.

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279

problem but the unobserved variables are not correlated with the included variables, the best choice is then random effects. System-GMM (generalized method of moments). Under the system-GMM, the model is estimated in both levels and first differences; i.e., in stacked regressions level equations are simultaneously estimated using differenced lagged regressors as instruments. This newly developed instrumental variable technique explicitly controls for the potential problems of unobserved firm heterogeneity and endogeneity.9 Executing the wrong method leads to some errors, such as biased estimated coefficients, inefficiency in regressions, etc. Hence, we employ all the above techniques in order to detect possible misspecifications due to the incorrect choice in estimation methods. 4.2. Testing approach Year-by-year tests. We run the regression model in each year from 1999 to 2004. This method is used to observe the stability of estimated coefficients. The common OLS method is conducted and only industry dummies are included in the model. General tests. We use six estimation methods (i.e., CLA, CA, pooled, fixed-effects, random effects, system-GMM). Time dummies and industry dummies are included in the systemGMM, pooled and random-effects estimates. When employing CA and CLA techniques, we use industry dummies only. For the fixed-effects technique, we use time dummies only as the industry classification is itself a fixed effect. Furthermore, we do not include the variable VOLA in the fixed-effects estimations because this variable is the same for all years within a firm. Industry tests. Our sample provides us with the opportunity to examine the explanatory power of the model in different industries. We classified 12 industries in Table 1 into 7 groups with respect to firms' industrial activities and by considering the size of sub-samples so that small sample bias is avoided. Robustness tests. Various alternative proxies are used for the same variable. We substitute SIZE by LN(TA) and LN(MV) separately to measure the firm size. Moreover, QUAL is replaced by PROFIT to test the signaling theory. We also replace ASTM by MATCH to test the matching theory. Another robustness check is to substitute LIQUID with CA/TA and FA/TA variables separately to test firm liquidity. We, additionally, run the model with and without LEVER to examine the impact of financing mix on debt maturity mix. Lastly, we adopt some empirical checks with respect to firm size, growth and loan maturity classifications; and the influence of macroeconomic factors and corporate ownership structure on debt maturity decisions. 5. Results 5.1. Year-by-year tests Table 4 reports the cross-sectional regression results in each year. The findings reveal that the coefficient estimates on SIZE, ASTM, and LIQUID variables consistently have the predicted

9

For a detailed discussion of the advantages of employing the system-GMM method see Antoniou et al. (2006), Arellano and Bover (1995), and Blundell and Bond (1998).

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Table 4 Cross sectional analysis of debt maturity mix determinants of Chinese firms in each year using OLS estimates

Sign

GROW

SIZE

QUAL

ASTM

TAX

LIQUID

VOLA

Intercept

+/−

+

−

+

−

+

−

+/−

0.1021⁎⁎⁎ (0.0232) 0.0981⁎⁎⁎ (0.0330) 0.1322⁎⁎⁎ (0.0329) 0.1079⁎⁎⁎ (0.0182) 0.1009⁎⁎⁎ (0.0229) 0.1208⁎⁎⁎ (0.0280)

− 0.0462 (0.1483) − 0.2058⁎ (0.1201) 0.0965 (0.2320) − 0.0587 (0.1065) 0.1465 (0.1886) 0.1773 (0.2000)

− 0.7298⁎⁎⁎ (0.2313) − 0.8252⁎⁎⁎ (0.2401) − 1.0262⁎⁎⁎ (0.2978) − 0.8275⁎⁎⁎ (0.2087) − 1.0860⁎⁎⁎ (0.1872) − 1.0910⁎⁎⁎ (0.1718)

Years 1999 − 0.0095 0.0473⁎⁎⁎ 0.1854 0.0121⁎⁎⁎ 0.0621 (0.0136) (0.0153) (0.3041) (0.0026) (0.1070) 2000 0.0039 0.0579⁎⁎⁎ −0.2282 0.0105⁎⁎⁎ 0.0210 (0.0075) (0.0157) (0.4830) (0.0026) (0.0663) 2001 0.0109 0.0674⁎⁎⁎ 0.3029 0.0101⁎⁎ − 0.0007 (0.0152) (0.0176) (0.3743) (0.0039) (0.0493) 2002 − 0.0158 0.0547⁎⁎⁎ 0.1693 0.0119⁎⁎⁎ − 0.0114 (0.0110) (0.0134) (0.2115) (0.0021) (0.0094) 2003 0.0182⁎⁎ 0.0680⁎⁎⁎ 0.3497 0.0157⁎⁎⁎ − 0.0207 (0.0086) (0.0115) (0.2268) (0.0030) (0.0173) 2004 0.0297⁎ 0.0631⁎⁎⁎ 0.2841⁎ 0.0160⁎⁎⁎ 0.0122 (0.0161) (0.0107) (0.1531) (0.0031) (0.0367)

Adj. R2 F-statistic

0.2849

6.7112⁎⁎⁎

0.2055

4.7094⁎⁎⁎

0.2546

5.8969⁎⁎⁎

0.3621

9.1365⁎⁎⁎

0.4223 11.478⁎⁎⁎ 0.4258 11.6316⁎⁎⁎

See notes to Table 2 for variable definitions. Estimated standard errors robust to heteroscedasticity are given in the parentheses. ⁎⁎⁎, ⁎⁎, and ⁎ denotes significance level at the 1%, 5%, and 10%, respectively. Industry dummies are included in all estimations.

positive signs over the period. Furthermore, in almost all cases the coefficients of these three variables are significant at the 1% level. This may demonstrate that for Chinese listed firms, firm size, asset maturity and liquidity are very important in deciding the financing strategy for debt maturity structure. In other words, Chinese firms tend to issue longer maturity if they are larger or more liquid. They also seem to match the maturity of their assets with that of liabilities. Regarding the other results, GROW appears to produce mixed support for the theories. In 1999 and 2002, the corresponding signs are insignificantly negative, which weakly supports the underinvestment hypothesis. In other years, the signs are positive (insignificant in 2000 and 2001; significant in 2003 and 2004), which gives some support to the overinvestment hypothesis. China has recently maintained a very high economic growth rate; average rate being 9.5% during 1994–2004. In order to maintain this high growth in the future, these results may imply that Chinese firms should be cautious about overinvestment problems. The coefficients on the variables TAX and VOLA have different signs in different years but they are always insignificant over the whole period, except the significant and negative coefficient of VOLA in 2000. Hence, the pure cross-sectional analysis implies effective tax rate and earnings volatility do not have any significant influence on debt maturity structure of Chinese companies. The relevance of QUAL for debt maturity mix is not that different as only in 2004 the underlying coefficient is significant with unexpectedly positive sign. 5.2. Tests for the general model Table 5 shows the regression results for the general model obtained by CLA, CA, pooled, fixed-effects, random-effects and system-GMM methods. In what follows, we examine the implications of each independent variable.

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281

Table 5 Determinants of debt maturity mix of Chinese firms using various estimation methods Independent variables Predicted CLA sign

CA

Pooled

Fixedeffects

GROW

+/−

SIZE

+

QUAL

−

ASTM

+

TAX

−

LIQUID

+

VOLA

− +/−

0.0051 (0.0097) 0.0645⁎⁎⁎ (0.0111) 1.1396⁎ (0.5998) 0.0169⁎⁎⁎ (0.0022) −0.0261 (0.0374) 0.1063⁎⁎⁎ (0.0202) 0.0932 (0.0923) −1.0263⁎⁎⁎ (0.1810) 0.4687 13.6489⁎⁎⁎ 259 –

0.0025 (0.0044) 0.0584⁎⁎⁎ (0.0055) 0.2414⁎⁎ (0.1072) 0.0119⁎⁎⁎ (0.0009) − 0.0085 (0.0125) 0.1055⁎⁎⁎ (0.0072) 0.0235 (0.0687) − 0.8986⁎⁎⁎ (0.0866) 0.3333 34.7600⁎⁎⁎ 1,554 –

0.0010 (0.0051) 0.0319⁎⁎⁎ (0.0114) 0.1013 (0.0886) 0.0061⁎⁎⁎ (0.0010) − 0.0050 (0.0105) 0.1043⁎⁎⁎ (0.0083) −

Intercept

0.0154 (0.0137) 0.0663⁎⁎⁎ (0.0158) 1.8193⁎⁎ (0.8117) 0.0130⁎⁎⁎ (0.0024) − 0.0326 (0.0483) 0.0659⁎⁎⁎ (0.0254) 0.1348 (0.1731) − 1.0871⁎⁎⁎ (0.2557) 0.3580 8.9960⁎⁎⁎ 259 –

0.0012 (0.0045) 0.0482⁎⁎⁎ (0.0073) 0.1442⁎ (0.0868) 0.0082⁎⁎⁎ (0.0009) − 0.0057 (0.0103) 0.1052⁎⁎⁎ (0.0074) − 0.0138 (0.1159) − 0.3940⁎⁎ − 0.7325⁎⁎⁎ (0.1634) (0.1134) 0.6052 0.1941 9.8533⁎⁎⁎ 17.2658⁎⁎⁎ 1,554 1,554 – –

–

–

–

–

Adjusted R2 F-statistic No. observations Sargan [ p-value] Correlation 1/ (correlation 2)

Randomeffects

–

SystemGMM 0.0054 (0.0079) 0.0645⁎⁎⁎ (0.0148) 0.0247 (0.0832) 0.0029⁎ (0.0016) −0.0091⁎⁎⁎ (0.0035) 0.0793⁎⁎⁎ (0.0212) −0.0422 (0.3698) −0.7791⁎⁎⁎ (0.2382) 0.2581 48.07⁎⁎⁎ 1,554 91.24 [0.329] [0.000] [0.283]

See notes to Table 2 for variable definitions. Estimated standard errors robust to heteroscedasticity are given in the parentheses. ⁎⁎⁎, ⁎⁎, and ⁎ denotes significance level at 1%, 5%, and 10%, respectively. CLA is the ‘crosssectional, explanatory variables lagged and averaged’ regressions; CA is the ‘cross-sectional, all variables averaged’ regressions. For the random-effects, the adjusted R2 and F values are weighted statistics. Industry dummies are included in all specifications, except fixed-effects. Time dummies are included in pooled, system-GMM, fixedeffects and random-effects estimates. Sargan Test is the test of over identifying restrictions, asymptotically distributed as χ2 under the null of instruments' validity. Correlation 1 and Correlation 2 are the first and second order autocorrelation of residuals, respectively; which are asymptotically distributed as N(0,1) under the null of no serial correlation.

GROW has positive but insignificant coefficients under all specifications. This irrelevance of market-to-book ratio for debt maturity decisions was also reported by Billett et al. (2007), Kim et al. (1995) and Stohs and Mauer (1996) for US firms, and by Cai et al. (1999) for Japanese firms. To some extent, our findings lend some support to Hart and Moore's (1995) overinvestment argument that firms tend to use long-term debt to control managers' incentives to invest in negative NPV projects. It maybe that underinvestment problem is of a less concern for Chinese firms than overinvestment inefficiencies. The SIZE variable has consistently positive and significant coefficients across various methods, which is a very common finding in the literature (e.g., Barclay and Smith, 1995). This demonstrates that the size of Chinese firms is a very important factor in deciding maturity of loans, i.e., larger firms tend to issue longer-term debt. In fact, most of the large Chinese firms receive some support from the government; they have relatively low bankruptcy risks, and can easily obtain long-term financing.

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According to Flannery (1986) separating equilibrium theory, short-term debt can be used as a signal to show the high quality of the firm assuming that short-term debt faces less credit deterioration than long-term debt and high quality firms can afford the cost of rolling over shortterm debt. However, the estimated coefficients of QUAL have the opposite signs irrespective of estimation methods. What is more, contradicting most of the empirical literature,10 the coefficients are significant under CLA, CA, pooled and random-effects specifications. We use the QUAL as the proxy for private information which are known by the inside management but are unknown by outside investors. The positive relation between private growth information and debt maturity may be due to the following reasons: In China, the corporate debt market is very small and undeveloped, and bank loan plays a very strong role in financing firms (see Appendix A). Accordingly, borrowing short-term debt from banks may not signal the firm's good quality. On the contrary, borrowing short-term debt may show that the firm has a low credit rating and uncertain future prospects. Thus, the ability of borrowing long-term debt may be the signal to prove a Chinese firm's good quality. As a related study, Berger et al. (2005) also find conflicting evidence against Diamond (1991) and Flannery (1986) models for highrisk US firms. They attribute this to the notion that they use bank loans that are good for resolving information asymmetries, not public loans, which is parallel to the nature of our loan type.11 We find strong evidence that firms with long-term asset maturity tend to have long-term debt as the ASTM variable has the predicted positive signs and the coefficients are significant at the 1% level, except in system-GMM. It seems that, like in the US (e.g., Guedes and Opler, 1996) and Western Europe (Antoniou et al., 2006), Chinese firms are heavily subscribed to the matching principle. The results with respect to TAX do not contradict the empirical hypothesis. All the coefficients have the predicted negative sign but they are all insignificant, except in systemGMM. This can probably be explained by the relatively low effective tax rates in China, which could cause effective tax rates not to exert any significant influence on the debt maturity choice. Another explanation would be that the Chinese state holds substantial holdings on firms and banks but at the same time they collect tax revenues. This is not in line with the assumptions of tax theories. Dennis et al. (2000) and Guedes and Opler (1996) also reported insignificant tax coefficients. It should be noted that once the potential endogeneity problem is explicitly accounted for by the system-GMM method, the coefficient appears to be significant at the 1% level. As for the LIQUID variable, the coefficient is positive and significant at the 1% level in all cases. The results imply that a firm with less current liabilities employs more long-term debt in its capital structure. It maybe that lenders are concerned about the long-term prospects of their borrowers when lending for the long term and thus put ‘high liquidity’ requirements on such

10 Datta et al. (2005) and Esho et al. (2002) also report, although insignificant, positive coefficient estimates. Our findings confirm them after controlling for endogeneity (system-GMM) and firm heterogeneity (fixed-effects). Ang et al. (1997) base their analysis in an emerging market and find that the information asymmetries between the banks and firms are insignificant. Their results imply that large and old (quality) firms would like to rely on bank financing, which means quality firms can borrow long-term bank loan. 11 Due to data unavailability we do not test the non-monotonic relation between debt maturity and bond rating suggested by Diamond (1991).

Table 6 Determinants of debt maturity mix of Chinese firms: industry classification GROW 1. Automobile and parts Fixed 0.0259 (0.0159) CLA 0.0362 (0.0420)

0.0890⁎⁎⁎ (0.0343) 0.0093 (0.0588)

−0.0185 (0.0171) 0.0018 (0.0351)

0.0534⁎ (0.0312) 0.0480 (0.0349)

3. Electricity Fixed −0.0120 (0.0259) CLA −0.0082 (0.1003)

−0.0069 (0.0966) 0.1534⁎

QUAL − 0.0425 (0.2569) − 0.0885 (1.9216)

ASTM 0.0339⁎⁎⁎ (0.0075) − 0.0003 (0.0274)

0.6324⁎⁎⁎ (0.2259) 3.6657⁎⁎

0.0077⁎⁎⁎ (0.0020) 0.0233⁎⁎⁎

(1.7540)

(0.0069)

0.0095⁎⁎ (0.0041) 0.0208⁎⁎⁎

(0.0832)

−0.5269 (0.6564) −0.5800 (4.7929)

4. Electronic and equipment Fixed −0.0067 (0.0117) CLA 0.0323 (0.0272)

−0.0063 (0.0341) 0.0467 (0.0492)

5. Industrial engineering Fixed 0.0285⁎ (0.0169) CLA 0.0044 (0.0217)

0.0295 (0.0286) −0.0122 (0.0339)

6. Industrial metal Fixed 0.0293 (0.0199) CLA 0.0360 (0.0659)

0.0492 (0.0375) 0.0475 (0.0710)

CLA

7. Technology hardware and equipment Fixed 0.0019 0.0010 (0.0099) (0.0229) CLA −0.0241⁎ −0.0276 (0.0123) (0.0217)

TAX

0.1160 (0.0796) −1.0038⁎⁎⁎ (0.2880)

LIQUID 0.1241⁎⁎⁎ (0.0325) −0.1078⁎⁎ (0.0505)

VOLA – −1.72⁎⁎⁎ (0.6631)

−0.0083 (0.0159) −0.0308 (0.0339)

0.1235⁎⁎⁎ (0.0169) 0.0503 (0.0479)

–

0.1199⁎⁎⁎ (0.0293) 0.1127 (0.0823)

–

(0.0072)

−0.0661 (0.2443) −0.0377 (0.5605)

−0.1783 (0.2809) 2.9959 (3.1123)

0.0030 (0.0023) 0.00003 (0.0068)

−0.0091 (0.0182) 0.1087 (0.1235)

0.0280 (0.0219) 0.0568 (0.0780)

− 0.0834 (0.1910) − 0.8573 (1.3263)

0.0122⁎⁎ (0.0057) −0.0325⁎⁎⁎

−0.0162 (0.0479) −0.0099 (0.1316)

0.1099⁎⁎⁎ (0.0209) 0.0795 (0.0654)

0.1106⁎⁎⁎ (0.0354) 0.0659 (0.1116)

− 0.2756 (0.3491) 6.9572⁎⁎

(0.0112)

0.0086⁎⁎ (0.0037) 0.0283⁎⁎⁎

(3.0338)

(0.0093)

−0.0058 (0.0245) −0.0755 (0.2920)

0.0147 (0.1548) 0.5659 (1.0807)

0.00002 (0.0028) 0.0002 (0.0046)

−0.1495⁎⁎⁎ (0.0565) −0.0310 (0.1739)

0.1032⁎⁎⁎ (0.0275) −0.0146 (0.0283)

0.2408 (0.3448)

1.4571 (1.2008)

– −0.6068 (0.4076)

– −0.9072⁎ (0.4895)

– −2.4060⁎ (1.3505)

– 0.0258 (0.0799)

Intercept −1.4671⁎⁎⁎ (0.4992) 0.3073 (1.0152)

−0.6750 (0.4334) −0.7671 (0.5511)

0.3994 (1.3699) −2.0486⁎

Adj. R2

F-statistics

N

0.6280

8.0109⁎⁎⁎

0.1083

1.3818

23

0.6512

10.306⁎⁎⁎

300

138

0.2439

3.2587⁎⁎⁎

50

0.4813

4.9953⁎⁎⁎

156

0.3449

2.8808

0.5432

6.2264⁎⁎⁎

0.0674

1.2791

0.3989

4.2321⁎⁎⁎

0.0725

1.4808

0.6424

9.1721⁎⁎⁎

192

0.3827

3.7464⁎⁎⁎

32

0.2615

2.5251⁎⁎

156

26

(1.1076)

0.1771 (0.4698) −0.6151 (0.7585)

−0.5121 (0.4106) 0.4145 (0.4932)

−0.6556 (0.5750) −0.8118 (1.1038)

−0.0712 (0.3311) 0.5116 (0.3802)

− 0.0890 a

0.7079

168 28

264 44

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2. Chemical Fixed

SIZE

26

283

See notes to Table 2 for variable definitions Estimated standard errors robust to heteroscedasticity are given in the parentheses. CLA is the cross-sectional, explanatory variables lagged and averaged’ regressions. Industry (time) dummies are included in the CLA (Fixed) regressions. ⁎⁎⁎, ⁎⁎, and ⁎ denote significance level at the 1%, 5%, and 10%, respectively. a The corresponding non-adjusted R2 is 0.2158.

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loan covenants (Antoniou et al., 2006; Morris, 1992). This demonstrates that good liquidity is an essential factor for Chinese firms to borrow long-term debt. Finally, the coefficients estimated for the volatility in earnings (VOLA) variable by various methods are all insignificant. The findings then show that the changes in earnings do not provide any significant effect on debt maturity decisions of Chinese firms. The state is the controlling shareholder for most Chinese listed firms. Hence, suffering from financial distress due to volatile earnings would not be a major concern for such firms, which may suggest the irrelevance of VOLA in debt maturity decisions. Stohs and Mauer (1996) find a variability coefficient of similar quality only for their cross-sectional estimates. In the literature fixed-effects estimations are more common than random-effects. However, our results are not sensitive to the choice between these two econometric specifications, nor are they among six methods in Table 5, except the TAX variable. This can emphasize the robustness of our regression results. Furthermore, the diagnostic tests show that the system-GMM estimates are reliable as the validity of instruments dated t − 1 and further is confirmed by Sargan test and the correlation tests expectedly indicate the presence of first-order autocorrelation and the absence of second-order autocorrelation. 5.3. Regression results across industry groups In this section, we analyze debt maturity mix determinants in 7 industry classifications derived from 12 sectors in Table 1 by employing fixed-effects (considering unobservable firm-specific factors) and CLA (considering potential endogeneity problem) regressions only. We did not employ system-GMM method as it would generate biased results for small sub-samples for each industry. The results are reported in Table 6. In the following, we discuss only the most discernible findings for each industry. In the automobile and parts industry, the coefficients on GROW have positive signs. This may imply that the overinvestment issues are important in this industry. The TAX and VOLA variables have the expected negative signs for the CLA method. The results for the other variables are generally in line with the findings for the full sample. In the chemical industry, contrary to the theory, the coefficients for QUAL are significant. The tax shield effects do not seem to be too irrelevant with insignificant but negative TAX coefficients. In the electricity industry, QUAL has insignificant coefficients but with the predicted signs in both specifications. This may imply that the signaling theory (e.g., Flannery (1986) separating equilibrium) get some support from this industry. It is also the only industry that provides negative GROW coefficients in both regressions, which shows that the electricity industry may be facing underinvestment distortions. In the electronic and equipment industry, although fixed-effects estimation produces high adjusted R2 no single factor appears to significantly affect debt maturity mix of firms in this business. For the industrial engineering firms, GROW has positive and significant (under fixed-effects) coefficients. This may imply that the overinvestment effect is a concern for them. In the industrial metal industry, ASTM has significant and predicted positive signs. The SIZE and TAX variables have predicted signs but the coefficients are insignificant. The coefficient estimate on VOLA is negative and significant. Finally, for the technology hardware and equipment firms, the TAX variable has negative and significant (under fixed-effects) coefficients, which confirms the theory.

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5.4. Robustness tests: Variable definitions and modeling We conduct some robustness tests regarding six attributes by fixed-effects and system-GMM regressions in Table 7 and compare them with the findings in Table 5. Replacing SIZE with LN(TA) or LN(MV). The results in the first panel of Table 7 show that using natural logarithm of total assets or market value of equity does not change the quality of the findings and the explanatory power of our model.12 Replacing QUAL with PROFIT. Profitability has been under scrutiny in the capital structure theories especially in the pecking order theory. The overinvestment problem that arises from the free cash flow has also been investigated in financing choice theories. Using PROFIT instead of QUAL in Table 7 has produced that expected negative coefficient but the level of significance is higher than 10%. This substitution has not changed the results regarding the other variables. Replacing ASTM with MATCH. The results are generally insensitive to this empirical check, except the coefficient estimate on MATCH found by system-GMM regression is not significant. Hence, ASTM seems to be the better measure. Replacing LIQUID with CA/TA or FA/TA. Previously, the LIQUID variable measured by working capital ratio had a direct impact on debt maturity. Replacing it with CA/TA in the fourth panel of Table 7, we detect an inverse relation. This is expected as higher proportion of current assets in total assets should not support long-term debt usage. On the other hand, using FA/TA in Table 7 produced very similar results to Table 5. The impact of leverage. The LEVER variable has positive signs and the coefficients are significant for both estimates in Table 7. This confirms Morris (1992) who argues that highlylevered firms may have bankruptcy probabilities and thus prefer loans with long maturities to shield themselves.13 5.5. Robustness tests with respect to maturity, size and growth classifications In this section, we divide the sample into three equal parts in each classification. For instance, we have 259 firms and classify 86 firms as small, 87 as middle size and the remaining 86 as large firms, based on their average size over the sample period. In order to distinguish better between small and large firms, we dropped the firms in the middle range. The same procedure applies when classifying firms as low-growth, medium-growth and high-growth; and firms with short-, medium- and long-maturity structure of debt. To our knowledge, no debt maturity study has attempted these classifications for empirical checks. The estimation methods adopted are CLA and fixed-effects. The results are presented in Table 8. 5.5.1. Maturity classification For firms that prefer mainly short-term debt (Panel A1, Table 8), the relation between debt maturity and its determinants shows that majority of the coefficients are insignificant. Firm liquidity has a positive and strong impact on loan maturity using fixed-effects estimates. It is interesting to report a significantly negative coefficient for ASTM using CLA estimates. This 12

We do not report the results based on LN(MV) to conserve space but they are available on request. If leverage is strongly positively associated with debt maturity and negatively associated with growth, one needs to control for leverage to prevent the downward bias in GROW's coefficient (Stohs and Mauer, 1996). Our correlation matrix reveals that we do not have this problem, and as a result, the estimates for the GROW variable are unaffected with the inclusion of leverage in the model. 13

286

Table 7 Robustness check of the general results F-statistic

0.6079

9.9500⁎⁎⁎

0.3362 Diagnostics

134.8⁎⁎⁎ [0.659; 0.000; 0.433]

0.6051

9.8464⁎⁎⁎

0.2297 Diagnostics

64.73⁎⁎⁎ [0.770; 0.000; 0.328]

0.5961

9.5220⁎⁎⁎

0.2357 Diagnostics

34.83⁎⁎⁎ [0.415; 0.000; 0.324]

0.5574

8.2716⁎⁎⁎

0.2040 Diagnostics

45.89⁎⁎⁎ [0.876; 0.000; 0.262]

0.5592

8.3245⁎⁎⁎

0.2270 (0.4084)

−0.1705 (0.1717) −0.5705⁎⁎⁎ (0.1958)

0.2526 Diagnostics

45.11⁎⁎⁎ [0.869; 0.000; 0.303]

VOLA

LEVER

Intercept

Adj.R 2

F stat.

–

0.3230⁎⁎⁎

−0.4806⁎⁎⁎

0.6174

10.2841⁎⁎⁎

−0.2062 (0.3168)

(0.0499) 0.2270⁎⁎ (0.0932)

(0.1614) −0.6837⁎⁎⁎ (0.2450)

0.3198 50.77⁎⁎⁎ [ 0.590; 0.000; 0.324]

SIZE

QUAL

ASTM

TAX

LIQUID

VOLA

+ or −

+

−

+

−

+

−

0.0736⁎⁎⁎ (0.0182) 0.0894⁎⁎⁎ (0.0131)

0.0903 (0.0878) 0.0937 (0.0737)

0.0050⁎⁎⁎ (0.0010) 0.0088⁎⁎⁎ (0.0016)

−0.0053 (0.0105) −0.0096⁎⁎⁎ (0.0030)

0.1042⁎⁎⁎ (0.0082) 0.0982⁎⁎⁎ (0.0137)

–

2. Substituting QUAL for PROFIT Fixed 0.0010 0.0319⁎⁎⁎ (0.0051) (0.0115) GMM 0.0040 0.0537⁎⁎⁎ (0.0080) (0.0123)

−0.0357 (0.0429) −0.0464 (0.0333)

0.0062⁎⁎⁎ (0.0010) 0.0044⁎⁎ (0.0020)

−0.0053 (0.0105) −0.0105⁎⁎⁎ (0.0029)

0.1045⁎⁎⁎ (0.0083) 0.0871⁎⁎⁎ (0.0215)

–

3. Substituting ASTM for MATCH Fixed −0.0003 0.0177 (0.0051) (0.0113) GMM 0.0053 0.0555⁎⁎⁎ (0.0083) (0.0163)

0.0984 (0.0897) 0.0267 (0.0787)

0.0010⁎⁎ (0.0004) 0.0001 (0.0005)

−0.0046 (0.0106) −0.0095⁎⁎ (0.0039)

0.0961⁎⁎⁎ (0.0083) 0.0769⁎⁎⁎ (0.0220)

–

4. Substituting LIQUID for CA/TA Fixed 0.0052 0.0264⁎⁎ (0.0054) (0.0124) GMM 0.0059 0.0623⁎⁎⁎ (0.0066) (0.0117)

−0.0027 (0.0935) −0.0369 (0.0634)

0.0028⁎⁎ (0.0011) 0.0026⁎ (0.0015)

−0.0043 (0.0111) −0.0113⁎⁎⁎ (0.0043)

−0.0597 (0.0562) −0.0825⁎ (0.0490)

5. Substituting LIQUID for FA/TA Fixed 0.0045 0.0214⁎ (0.0054) (0.0121) GMM 0.0021 0.0513⁎⁎⁎ (0.0068) (0.0127)

−0.0062 (0.0933) −0.0571 (0.0670)

0.0018 (0.0012) 0.0015⁎

−0.0039 (0.0111) −0.0097⁎⁎

0.1700⁎⁎ (0.0675) 0.2818⁎⁎

(0.0009)

(0.0049)

(0.1214)

6. Including LEVER GROW

SIZE

QUAL

ASTM

TAX

LIQUID

Fixed

0.0285⁎⁎

0.0774 (0.0873) 0.0242 (0.0774)

0.0065⁎⁎⁎ (0.0010) 0.0047⁎⁎⁎ (0.0018)

−0.0038 (0.0104) −0.0062⁎⁎ (0.0031)

0.1241⁎⁎⁎

Sign

1. Substituting SIZE for LN(TA) Fixed 0.0050 (0.0052) GMM 0.0101 (0.0067)

GMM

0.0026 (0.0050) 0.0034 (0.0075)

(0.0113) 0.0503⁎⁎⁎ (0.0165)

(0.0087) 0.1115⁎⁎⁎ (0.0227)

−0.0375 (0.1109)

−0.2782 (0.3650)

0.0463 (0.3228)

– 0.0081 (0.2205)

–

Intercept

−1.0101⁎⁎⁎ (0.2653) −1.2894⁎⁎⁎ (0.2102)

−0.3945⁎⁎ (0.1644) −0.6394⁎⁎⁎ (0.1919)

−0.1559 (0.1593) −0.6423⁎⁎ (0.2508)

−0.1550 (0.1719) −0.5625⁎⁎⁎ (0.1740)

See notes to Table 2 for variable definitions Estimated standard errors robust to heteroscedasticity are given in the parentheses. ⁎⁎⁎, ⁎⁎, and ⁎ denote significance level at the 1%, 5%, and 10%, respectively. Under ‘Diagnostics’ we report the [p]-values for Sargan, Correlation 1 and Correlation 2 tests, respectively. These values confirm the validity of system-GMM results and that of instrument set in which all explanatory variables were treated as endogenous.

K. Cai et al. / Pacific-Basin Finance Journal 16 (2008) 268–297

Adj. R 2

GROW

Table 8 Determinants of debt maturity mix of Chinese firms: maturity, size and growth classifications

Sign

GROW

SIZE

QUAL

ASTM

TAX

LIQUID

VOLA

+ or −

+

−

+

−

+

−

0.0514 (0.0716) 0.8333 (0.7451)

Panel A2: firms holding debt with long-term maturity Fixed 0.0169 0.0619⁎ (0.0146) (0.0371) CLA 0.0396 0.0901⁎⁎ (0.0492) (0.0389)

−0.1168 (0.2362) 0.9071 (1.0428)

Panel B1: small firms Fixed 0.0034 (0.0068) CLA 0.0050 (0.0180) Panel B2: large firms Fixed −0.0052 (0.0113) CLA −0.0516 (0.0316) Panel C1: low-growth firms Fixed – CLA

–

Panel C2: high-growth firms Fixed – CLA

–

0.0352⁎⁎⁎ (0.0083) −0.0130 (0.0214)

–

–

(0.0462)

0.1465⁎⁎⁎ (0.0144) 0.0287 (0.0438)

0.0009 (0.0007) −0.0063⁎⁎

−0.0026 (0.0070) 0.0841⁎

(0.0028)

(0.0498)

0.0102⁎⁎⁎ (0.0022) 0.0098⁎ (0.0054)

−0.0046 (0.0206) −0.1091⁎⁎

0.0224 (0.0682)

0.3515 (0.5328)

0.1883 (0.1956) 4.0322 (2.6847)

0.0047⁎⁎⁎ (0.0012) 0.0085⁎⁎ (0.0040)

0.0382 (0.0302) −0.0668 (0.1997)

0.0649⁎⁎⁎ (0.0127) 0.0118 (0.0369)

–

−0.0522 (0.1245) −1.2824 (1.2223)

0.0051⁎⁎ (0.0024) 0.0193⁎⁎ (0.0085)

−0.0173 (0.0136) −0.0959 (0.0583)

0.1181⁎⁎⁎ (0.0196) 0.0524 (0.0517)

–

0.0852⁎⁎⁎ (0.0226) 0.0945⁎⁎⁎ (0.0265)

0.0577 (0.1030) 0.0298 (1.0471)

0.0027 (0.0021) 0.0239⁎⁎⁎ (0.0079)

−0.0116 (0.0116) 0.0222 (0.0650)

0.1028⁎⁎⁎ (0.0174) 0.0969⁎⁎⁎ (0.0365)

–

0.0159 (0.0167) 0.0551⁎⁎ (0.0267)

0.2720 (0.2093) 3.1995 (2.7038)

0.0032⁎⁎ (0.0016) 0.0146⁎⁎⁎ (0.0046)

0.0211 (0.0331) −0.0099 (0.2075)

0.1034⁎⁎⁎ (0.0123) 0.0580 (0.0416)

–

– –

– –

0.2582 (0.4571)

0.0658 (0.1371)

−0.2403 (0.3522)

0.3955 (0.3107)

Adj. R 2

− 0.0704 (0.0977) − 0.1113 (0.1545)

− 0.7295 (0.5391) − 1.0373⁎

F-statistics

N

0.0711

1.4112

516

−0.0152

0.9248

86

0.4021

4.6089⁎⁎⁎

516

0.1978

2.3973⁎⁎⁎

86

0.5455

7.5076⁎⁎⁎

516

0.1665

1.9990⁎

0.6779

12.412⁎⁎⁎

516

0.4114

5.2447⁎⁎⁎

86

0.7153

14.6210⁎⁎⁎

516

0.4677

5.9801⁎⁎⁎

86

0.4819

6.0431⁎⁎⁎

516

0.1606

1.9571⁎

(0.5780)

0.0627⁎⁎ (0.0295) − 0.1294 (0.1449)

0.1188⁎⁎⁎ (0.0379) 0.0124 (0.1421)

− 1.1141⁎⁎⁎ (0.3385) − 1.4921⁎⁎⁎ (0.3977)

− 0.1849 (0.2222) − 0.9515⁎⁎ (0.3904)

86

K. Cai et al. / Pacific-Basin Finance Journal 16 (2008) 268–297

Panel A1: firms holding debt with short-term maturity Fixed −0.0020 0.0063 (0.0032) (0.0070) CLA 0.0147 0.0088 (0.0094) (0.0104)

Intercept

86

See notes to Table 2 for variable definitions Estimated standard errors robust to heteroscedasticity are given in the parentheses. ⁎⁎⁎, ⁎⁎, and ⁎ denote significance level at the 1%, 5%, and 10%, respectively. CLA is the ‘cross-sectional, explanatory variables lagged and averaged’ regressions. To be consistent with the classifications, SIZE is dropped in Panel B and GROW is dropped in Panel C.

287

288

K. Cai et al. / Pacific-Basin Finance Journal 16 (2008) 268–297

finding implies that such firms do not apply the matching principle; on the contrary, they may have a mismatching principle. In addition, CLA results report unexpectedly significant and positive coefficient for TAX. For firms that prefer mainly long-term debt (Panel A2, Table 8), the coefficients of SIZE, ASTM, TAX and LIQUID are significant with theory-consistent signs. The GROW coefficients are insignificant but they have negative signs in Panel A1 and positive signs in Panel A2. This may show that over(under)investment issues may be important for firms who have relatively high amount of long (short)-term debt. The QUAL does not impact maturity decisions significantly but it has the expected negative sign in Panel A2 using fixed-effects. Lastly, the coefficients on VOLA have unexpected positive signs but they are insignificant for both types of firms. 5.5.2. Size classification The coefficients on the ASTM and LIQUID variables in Panel B have the predicted positive signs and they are mostly significant for both small and large firms. The GROW coefficient is insignificant and positive for small firms in Panel B1 but negative for large firms in Panel B2. These findings may mean that large firms with profitable growth options tend to opt for loans with shorter maturity while their counterparts prefer longer maturity debt. This may support the signaling theory that good firms (in our case large firms that are unlikely to go bankrupt and that have good investment prospects) choose short-term debt. This explanation gets little support with negative (although insignificant) QUAL coefficient for large firms. The insignificant but positive QUAL coefficient for small firms may imply that acquiring long-term debt from banks is particularly important for small Chinese listed firms to show their good quality. TAX and VOLA variables seem to have the same impact on large and small firms' maturity decisions with insignificant coefficients; as in both cases higher taxes or less volatile earnings implies shorter loan maturity. 5.5.3. Growth classification The coefficient estimates on SIZE, ASTM and LIQUID variables in Panel C are always positive and generally significant for both low-growth and high-growth firms. The QUAL variable, on the other hand, has positive but insignificant estimated coefficients in both groups. The results regarding TAX again show that high or low growth firms' loan maturity decisions are independent from corporate tax rates. Finally, the results for the VOLA are sub-sample dependant with respect to the sign of coefficients. The coefficient is expectedly negative for low-growth firms. However, we detect a positive coefficient for high-growth firms. It implies that firms with volatile earnings tend to borrow long term. High volatility in earnings may trigger bankruptcy. Therefore, firms with valuable growth opportunities would prefer long-term debt in order to protect their profitable projects for longer periods against any liquidation in the future. 5.6. Robustness tests: The impact of macroeconomic factors Apart from ‘short and long-term interest volatility’ and ‘term structure of interest rates’ variables that were discussed in Section 2.3, as being other macroeconomic factors, we also investigate the relevance of stock market return volatility (SMV) and market equity premium (EQYP) to debt maturity decisions. SMV is the standard deviation of the monthly stock exchange return index, compiled by DataStream, over the previous year. EQYP is the difference

K. Cai et al. / Pacific-Basin Finance Journal 16 (2008) 268–297

289

between the rates of return on equity markets and on treasury-bills (short-term lending rate in our case). Companies may review market conditions (i.e., timing the market) in an effort to minimize financing costs (see Antoniou et al., 2006; Baker et al., 2003). Hence, we consider a possible link between the movements in debt markets and stock markets and hence incorporate SMV and EQYP in our extended model. Managers may avoid issuing shares if they think the volatility in the stock exchange is high. In order to prevent mispricing of their shares, they may choose issuing alternative securities, i.e., long-term debt. This leads us to propose a direct relation between SMV and DEBTM. One may also expect a positive relation between equity premium and debt maturity. This is because higher premium on equity financing can force manager not to issue stocks as this form of financing becomes relatively expensive. Instead, they may opt for long-term debt, whose maturity matches better than short-term debt to share capital's (infinite) maturity. Lagging market-based variables may be necessary to allow company manager to adjust themselves to the new financing conditions. IRV-S, IRV-L, TERM, SMV and EQYP variables were therefore lagged 6 months according to a firm's fiscal year-end month. To see whether the inclusion of macroeconomic factors in the model has changed the main results in Table 5, one needs to compare the findings based on system-GMM, pooled and fixedeffects estimates in Table 5 and Table 9. It appears that the results in both tables are very similar as the coefficients on the firm-specific variables tend to have the same signs and significance levels. As for the market-specific factors, the coefficients are always negative for all macroeconomic variables using any econometric method but the significance level varies across methods. The pooled and fixed-effect estimates show that TERM has no significant impact on debt maturity of Chinese firms, which is consistent with the empirical findings of Kim et al. (1995) for their full time period and Datta et al.'s (2005) fixed-effects results, and Stohs and Mauer (1996). However, under system-GMM, the coefficient becomes significant. The market equity premium (EQYP) does not play a noteworthy role either under pooled and fixed-effects estimates. This finding confirms what Antoniou et al. (2006) report for French firms. However, the same authors detect a significantly positive relation between debt maturity and equity premium for UK and German firms, which implies that the results are country-specific. Again, EQYP has a significant coefficient under system-GMM. The findings in Table 9 reveal that interest rate volatility has negative impact on debt maturity and the relation is significant in Panels B and C, and when using system-GMM. Thus, we can argue that more volatile interest rates (short or long) cause Chinese managers to shorten debt maturity, which is contrary to what Kim et al. (1995) report for US firms but in line with what Antoniou et al. (2006) report for UK firms. Finally, system-GMM results suggest that stock market volatility (SMV) seems to influence Chinese managers' debt maturity decisions as more volatility leads them to choose short-term debt. Overall, market-oriented factors appear to be influential in forming the corporate debt maturity mix with respect to the system-GMM results. This is probably surprising if one was to consider China as a non-market oriented economy. 5.7. Robustness tests: The impact of corporate ownership structure Corporations' financing decisions should normally depend on, among other factors, who owns and manages them. The presence of conflict of interests among managers, shareholders and debtholders may influence the level and maturity of debt financing (Myers, 1977; Hart and Moore, 1995). Some studies (e.g., Wiwattanakantang, 1999) have detected a significant relation

290

Table 9 The influence of macroeconomic factors on debt maturity decisions of Chinese firms

Sign

GROW

SIZE

QUAL

ASTM

TAX

LIQUID

VOLA

TERM

EQYP

IRV-S

IRV-L

SMV

+ or −

+

−

+

−

+

−

+

+

+

+

+

Panel A Pooled 0.0029

0.0587⁎⁎⁎

Fixed GMM

Panel B Pooled 0.0028

0.0584⁎⁎⁎

0.2419⁎⁎ 0.0120⁎⁎⁎ (0.1066) (0.0009) 0.0011 0.0317⁎⁎⁎ 0.1032 0.0061⁎⁎⁎ (0.0045) (0.0109) (0.0877) (0.0010) 0.0051 0.0641⁎⁎⁎ 0.0134 0.0035⁎⁎ (0.0080) (0.0149) (0.0817) (0.0017)

(0.0041) (0.0055) Fixed GMM

Panel C Pooled 0.0031

0.0585⁎⁎⁎

0.2382⁎⁎ 0.0120⁎⁎⁎ (0.1065) (0.0009) 0.0016 0.0306⁎⁎⁎ 0.0986 0.0062⁎⁎⁎ (0.0044) (0.0110) (0.0876) (0.0010) 0.0052 0.0641⁎⁎⁎ 0.0135 0.0035⁎⁎ (0.0080) (0.0148) (0.0817) (0.0017)

(0.0041) (0.0054) Fixed GMM

Panel D Pooled 0.0024

0.0584⁎⁎⁎

0.2424⁎⁎ 0.0119⁎⁎⁎ (0.1069) (0.0009) 0.0008 0.0318⁎⁎⁎ 0.1024 0.0061⁎⁎⁎ (0.004) (0.0114) (0.0883) (0.0010) 0.0050 0.0641⁎⁎⁎ 0.0133 0.0035⁎⁎ (0.0080) (0.0148) (0.0817) (0.0017)

(0.0043) (0.0055) Fixed GMM

− 0.0087 0.1054⁎⁎⁎ (0.0125) (0.0072) − 0.0052 0.1042⁎⁎⁎ (0.0105) (0.0083) − 0.0091⁎⁎⁎ 0.0793⁎⁎⁎ (0.0035) (0.0212)

0.0224 − 0.5759 (0.0685) (1.0215) – − 0.4503 (0.8895) − 0.0422 − 0.0205⁎⁎⁎ (0.3698) (0.0061)

−0.8818⁎⁎⁎ 0.3342 (0.0949) −0.3950⁎⁎ 0.6058 (0.1571) −0.6473⁎⁎⁎ 0.2572 (0.1964)

39.9⁎⁎⁎

−0.0002 (0.0005) −0.0001 (0.0003) −0.0126⁎⁎⁎ (0.0037)

−0.8891⁎⁎⁎ 0.3345 (0.0876) −0.3828⁎⁎ 0.6061 (0.1564) −0.4908⁎⁎⁎ 0.2683 (0.1477)

40.04⁎⁎⁎

− 0.0376 −0.00006 (0.0300) (0.0004) – − 0.0415⁎ −0.00003 (0.0240) (0.0003) − 0.1574⁎⁎⁎ – −0.0126⁎⁎⁎ (0.0477) (0.0037)

−0.8959⁎⁎⁎ 0.3344 (0.0870) −0.3747⁎⁎ 0.6061 (0.1578) −0.4909⁎⁎⁎ 0.2684 (0.1477)

40.02⁎⁎⁎

− 0.0546 – (0.0584) − 0.0508 – (0.0453) − 0.0420⁎⁎⁎ – (0.0115)

−0.8781⁎⁎⁎ 0.3337 (0.0967) −0.3786⁎⁎ 0.6055 (0.1578) −0.4211⁎⁎⁎ 0.2687 (0.1267)

36.36⁎⁎⁎

− 0.0004 – (0.0004) − 0.0004 – (0.0003) − 0.0034⁎⁎⁎ (0.0011)

–

–

–

–

–

− 0.0086 (0.0125) − 0.0050 (0.0105) − 0.0095⁎⁎ (0.0038)

0.1057⁎⁎⁎ 0.0228 – (0.0071) (0.0684) 0.1043⁎⁎⁎ – – (0.0081) 0.0824⁎⁎⁎ 0.0431 – (0.0194) (0.3477)

–

− 0.0089 (0.0125) − 0.0052 (0.0105) − 0.0095⁎⁎ (0.0037)

0.1061⁎⁎⁎ 0.0217 – (0.0071) (0.0684) 0.1051⁎⁎⁎ – – (0.0081) 0.0825⁎⁎⁎ 0.0433 – (0.0193) (0.3477)

–

− 0.0085 (0.0125) − 0.0050 (0.0105) − 0.0096⁎⁎ (0.0037)

0.1055⁎⁎⁎ 0.0238 − 0.2881 (0.0072) (0.0686) (1.0721) 0.1044⁎⁎⁎ – − 0.1896 (0.0083) (0.9387) 0.0825⁎⁎⁎ 0.0435 − 0.0136⁎⁎⁎ (0.0193) (0.3477) (0.0040)

− 0.00006 (0.0005) − 0.0001 (0.0004) − 0.0029⁎⁎⁎ (0.0009)

– –

– –

Adj. R 2 F

− 0.0588 (0.0428) − 0.0601⁎

– (0.0337) − 0.1574⁎⁎⁎ – (0.0478)

–

−0.0001 (0.0005) −0.0001 (0.0004) −0.0122⁎⁎⁎ (0.0036)

9.97⁎⁎⁎ 75.2⁎⁎⁎

9.98⁎⁎⁎ 76.7⁎⁎⁎

9.98⁎⁎⁎ 76.9⁎⁎⁎

9.89⁎⁎⁎ 78.9⁎⁎⁎

TERM is the difference between the month-end returns on long-term government bond and short-term treasury-bill. EQYP is the difference between the rates of return on equity markets and on treasurybills. IRV-S is the standard deviation of monthly short-term lending rates over the previous year. IRV-L is the standard deviation of long-term lending rates over the previous year. SMV is the standard deviation of the monthly stock exchange return index over the previous year. See notes to Table 2 for other variables’ definitions. Estimated standard errors robust to heteroscedasticity are given in the parentheses. ⁎⁎⁎, ⁎⁎, and ⁎ denote significance level at the 1%, 5%, and 10%, respectively. The p-values for Sargan, Correlation 1 and Correlation 2 tests for Panels A, B, C and D are (0.276, 0.000, 0.283); (0.595, 0.000, 0.278); (0.593, 0.000, 0.2770); and (0.540, 0.000, 0.279), respectively. These values confirm the validity of system-GMM results and that of instrument set in which firm specific factors were treated as endogenous and market factors were treated as exogenous.

K. Cai et al. / Pacific-Basin Finance Journal 16 (2008) 268–297

0.2355⁎⁎ 0.0119⁎⁎⁎ (0.1056) (0.0009) 0.0016 0.0331⁎⁎⁎ 0.0956 0.0061⁎⁎⁎ (0.0047) (0.0113) (0.0875) (0.0010) 0.0054 0.0645⁎⁎⁎ 0.0247 0.0029⁎ (0.0079) (0.0148) (0.0832) (0.0017)

(0.0042) (0.0054)

Intercept

Table 10 The influence of ownership structure on debt maturity decisions of Chinese firms (1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

GROW

SIZE

QUAL

ASTM

TAX

LIQUID

VOLA

+

−

+

−

+

−

Sign + or −

(9)

(10)

(11)

(12)

LARGE TOP3

BIGH

INDIV

UNDIS

+ or −

+ or −

+ or −

+ or −

+ or −

−0.0526 0.0724⁎⁎ 0.1009 − 0.0021 – (0.0493) (0.0291) (0.1875) (0.1055) −0.0458 0.1127⁎⁎⁎ 0.1152 0.0052 – (0.0400) (0.0210) (0.1027) (0.0856)

Panel B CLA 0.0134 0.0659⁎⁎⁎ 1.7689⁎⁎ 0.0138⁎⁎⁎ (0.0161) (0.0186) (0.8168) (0.0028) CA 0.0012 0.0663⁎⁎⁎ 1.0347 0.0174⁎⁎⁎ (0.0110) (0.0127) (0.6404) (0.0026)

−0.0525 0.0728⁎⁎ 0.1065 – (0.0495) (0.0288) (0.1862) −0.0452 0.1129⁎⁎⁎ 0.1167 – (0.0398) (0.0211) (0.1028)

Panel C CLA 0.0135 0.0675⁎⁎⁎ 1.7611⁎⁎ 0.0137⁎⁎⁎ (0.0161) (0.0186) (0.8182) (0.0029) CA 0.0017 0.0672⁎⁎⁎ 1.0485 0.0174⁎⁎⁎ (0.0110) (0.0130) (0.6456) (0.0027)

−0.0515 0.0726⁎⁎ 0.0957 – (0.0492) (0.0291) (0.1839) −0.0457 0.1128⁎⁎⁎ 0.1135 – (0.0402) (0.0211) (0.1016)

–

Panel D CLA 0.0055 0.0697⁎⁎⁎ 1.6576⁎ (0.0156) (0.0176) (0.8323) CA −0.0058 0.0664⁎⁎⁎ 0.8523 (0.0112) (0.0121) (0.6338)

−0.0402 0.0727⁎⁎ 0.0902 – (0.0517) (0.0283) (0.1738) −0.0336 0.1151⁎⁎⁎ 0.1062 – (0.0408) (0.0212) (0.0947)

−0.5825⁎ – (0.3112) −0.3612 – (0.2282)

0.0144⁎⁎⁎ (0.0028) 0.0176⁎⁎⁎ (0.0026)

– –

0.0558 – (0.1040) 0.0379 – (0.0801)

–

0.0048 (0.0148) 0.0002 (0.0110)

Adj. R2

F

− 0.1080⁎ – (0.0622) − 0.0397 – (0.0532)

− 1.0857⁎⁎⁎ 0.3616 7.3⁎⁎⁎ (0.2967) − 1.0513⁎⁎⁎ 0.4575 10.4⁎⁎⁎ (0.2098)

− 0.1187⁎⁎ – (0.0573) − 0.0449 – (0.0492)

− 1.0948⁎⁎⁎ 0.3624 7.4⁎⁎⁎ (0.2977) − 1.0589⁎⁎⁎ 0.4581 10.6⁎⁎⁎ (0.2136)

− 0.1034⁎ – (0.0563) − 0.0378 – (0.0502)

− 1.0949⁎⁎⁎ 0.3619 7.4⁎⁎⁎ (0.3010) − 1.0519⁎⁎⁎ 0.4575 10.5⁎⁎⁎ (0.2136)

– –

− 0.6925⁎⁎ (0.3384) − 0.4510⁎ (0.2558)

− 0.5241 (0.4052) − 0.6606⁎⁎ (0.2997)

0.3688

7.6⁎⁎⁎

0.4660 10.8⁎⁎⁎

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Panel A CLA 0.0141 0.0674⁎⁎⁎ 1.7673⁎⁎ 0.0138⁎⁎⁎ (0.0160) (0.0187) (0.8158) (0.0029) CA 0.0016 0.0670⁎⁎⁎ 1.0478 0.0174⁎⁎⁎ (0.0109) (0.0129) (0.6463) (0.0027)

Intercept

LARGE is the share proportion of the largest blockholder. TOP3 is the sum of share proportion of the top three largest shareholders. BIGH is the number of shareholders with 5% or more stakes. INDIV is the number of non-institutional individual shareholders within top five shareholders. UNDIS is the proportion of undisclosed shareholdings. See notes to Table 2 for other variables’ definitions. Estimated standard errors robust to heteroscedasticity are given in the parentheses. ⁎⁎⁎, ⁎⁎, and ⁎ denote significance level at the 1%, 5%, and 10%, respectively. The mean (standard deviation) of LARGE, TOP3, BIGH, INDIV and UNDIS are 42% (0.16), 55% (0.14), 1.84 (1.02), 14% (0.24) and 39% (0.13), respectively. 291

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between ownership structure and the debt-equity choice in emerging markets. We are aware of only three papers that examine the effect of ownership structure on debt maturity decisions. Datta et al. (2005), and Guney and Ozkan (2005) find that higher managerial ownership leads to shorter-maturity debt for US and UK firms, respectively. This inverse relation may be due to the argument that shorter-maturity debt can mitigate the underinvestment problem when managers with higher equity ownership are aligned better to shareholders' interest. The use of (renewable) short-term debt may also lessen the costs related to managerial discretion as managers get monitored more frequently by various parties. On the other hand, Arslan and Karan (2006) examine the influence of ownership concentration and control on debt maturity structure of Turkish firms. They report that higher concentration lengthens the maturity of debt. We constructed ownership-related variables according to data availability in our database, ISI Emerging Markets. Out of 259 firms that have accounting and market data from Datastream, we have identified 226 Chinese firms with some ownership data.14 We measured three alternative variables that represent ownership concentration. These are LARGE (share proportion of the largest blockholder); TOP3 (sum of share proportion of the top three largest shareholders) and BIGH (number of shareholders with 5% or more stakes). In addition, two measures represent ownership identity, which are INDIV (number of non-institutional individual shareholders within top five shareholders) and UNDIS (proportion of undisclosed shareholdings). The regression results in Table 10 after controlling for ownership structure are very similar to the main results in Table 5, which highlights the robustness of the previous findings. The three proxies (LARGE, TOP3, BIGH) for the ownership concentration appear not to affect debt maturity decisions of Chinese firms. The only exception is in Panel D using CLA estimates, where higher concentration is inversely related to the maturity of debt. One can relate this negative impact to the monitoring hypothesis: As more dispersion among shareholders implies that an investor has fewer stakes in the company and thus less motivation to monitor the management team, it becomes more necessary to use short-term debt as a control mechanism. It further appears in Table 10 that a higher proportion of individual shareholders within top blockholders (INDIV) leads to a shorter-maturity debt. This could be due to the limited expertise of individuals, relative to (active) institutional investors, in monitoring managers. This finding may suggest that individual investors are not particularly motivated to curb agency distortions caused by managers. Finally, a higher proportion of undisclosed shareholdings (UNDIS) tends to be associated more with short-term debt. Overall, ownership concentration and control seems to have some moderate impact on debt maturity decisions of Chinese managers. 6. Summary and conclusion Debt maturity study is a relatively new domain in the capital structure research. The existing theories and most of the empirical studies focus mainly on developed markets. Therefore, we chose an emerging market, China, to examine the validity of these studies while examining the maturity mix of Chinese firms' liabilities. To the best of our knowledge, our paper is one of two

14

For a detailed analysis of corporate ownership structure in China, see Gul (1999) and Qi et al. (2000), among others.

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studies of the corporate debt maturity structure in an emerging economy, and the first one that studies Chinese firms. In this paper, we use a sample of 259 firms with 1554 observations in 12 industries from 1999 to 2004. We examine the main theories of debt maturity, which include agency costs (proxied by growth opportunities, firm size), the matching principle (proxied by asset maturity), signaling (proxied by firm quality), liquidity risk (proxied by leverage, volatility in earnings, liquidity), and tax effects (proxied by the effective tax rate). Pure cross-sectional, average cross-sectional, pooled, system-GMM, fixed-effects, and random-effects regressions have been used to check the robustness of the findings. This study finds that size of the firm, asset maturity and liquidity (working capital ratio) all have significant and positive impact on maturity of debt instruments. We show that the overinvestment problem tends to be more relevant than underinvestment inefficiencies in China. We only weakly support the debt maturity theories of taxation and liquidity risk. This could be due to the fact that the state in China has control over industrial firms and banks. It is important to note that after alleviating endogeneity concerns, in line with the theory, the system-GMM procedure produced a significant and negative coefficient for the TAX variable. Furthermore, the signaling theory does not seem to work in Chinese markets as we get the opposite results such that good quality firms may choose long-term debt. This is probably because the dominant financing source for firms in China is through banks. Banks are known to be more efficient than public debt markets in terms of monitoring, enforcing contract terms and gathering information, and thus in resolving agency problems and information asymmetries. Hence, it maybe that Chinese banks would not necessarily regard the issuance of long-term debt as a bad signal because the close relationships they have with their borrowers would allow them to assess precisely the riskiness and quality of firms. We have conducted a series of empirical checks. For instance, various alternative measures of a variable were used for comparison of the results. Estimations were also executed for seven sub-samples of industry groups, for low and high growth firms, for small and large firms, and for firms with short-term and long-term oriented debt maturity structure. Although these sub-samples provide some interesting findings, our main results are robust to these empirical checks. It would be interesting to see whether corporate managers in China consider market conditions before deciding the maturity of loans. For this, we tested the effects of term structure of interest rates, the volatility of interest rates and stock market return index, and market equity premium on loan maturity. The regression results for the market-related factors were found to be sensitive to whether one explicitly accounts for the endogeneity problem. The system-GMM findings show that higher volatility in interest rates and stock market as well as higher market equity premium and higher spread in term structure all lead to shorter maturity debt. Another set of empirical checks stems from incorporating corporate ownership and control variables into the analysis. The findings suggest that companies with more concentrated equity ownership and more individual shareholders tend to prefer short-term debt. In summary, our study reveals that the debt maturity structure of Chinese corporations is not only determined by standard firm-specific variables but it also depends on corporate ownership structure and some macroeconomic factors.

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Appendix A A.1. Panel A: Country-level aggregate figures for China (in 100 million RMB)

Year

Corporate bond

Government bond

Stocks

Financial institution loan

Total national tax

GDP

Economic growth, %

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 Average % to GDP

162 216 269 255 148 158 83 147 325 358 327 0.27%

1138 1511 1848 2412 3809 4015 4657 4884 5934 6280 6924 4.27%

327 150 425 1294 842 945 2103 1252 962 1358 1511 1.12%

39,976 50,544 61,157 74,914 86,524 93,734 99,371 112,315 131,294 158,996 178,198 109.48%

5127 6038 6910 8234 9263 10,683 12,582 15,301 17,636 20,017 24,166 13.51%

46,759 58,478 67,885 74,463 78,345 82,067 89,468 97,315 105,172 117,390 136,876 –

13.1 10.9 10.0 9.3 7.8 7.6 8.4 8.3 9.1 10.0 10.1 –

Corporate Bond is the amount of fund issued by corporate bonds. Government Bond is the amount of fund issued by the government. Stocks is the amount of fund acquired by stocks. (i.e. the issuing price multiplied by the issuing volume). Financial Institution Loan is the total loan issued by all financial institutions; it includes short-term, medium-term and long-term loans, credit loans and other loans. Total National Tax includes the valued-added tax, business tax, consumption tax, tariffs, agricultural and related tax and company income tax. Average Percentage to GDP was calculated using two steps: first, we divided each variable by the GDP of the corresponding year, and then, we averaged the 11 percentage values from 1994 to 2004.

A.2. Panel B: Shares types in China China has three stock exchanges: Shanghai Stock Exchange, Shenzhen Stock Exchange, and Hong Kong Stock exchange. There are three kinds of shares in Chinese stock markets: Share A: These shares are priced in RMB. These shares are bought or sold in RMB by Chinese citizens in Shanghai Stock Exchange and Shenzhen Stock Exchange. Firms who issue Share A are listed in Mainland China. Share B: These shares are priced in RMB. However, these shares are bought or sold in US dollars (in Shanghai Stock Exchange) or Hong Kong dollars (in Shenzhen Stock Exchange). The investors are Chinese citizens and international investors. Firms who issue Share B are listed in Mainland China. Share H: These shares are priced in Hong Kong dollars. These shares can be bought or sold in Hong Kong dollars in Hong Kong stock exchange. The investors are Hong Kong citizens and international investors. Until 20th August 2007, citizens from Mainland China were not allowed to buy Share H. Firms who issue Share H are listed in Hong Kong Market. A.3. Panel C: The structure of bond markets in China Total volume of all bonds in China is around 108.25 billion USD, which is relatively low compared to advanced countries. The value of sub components, in billion US$, are as follows: Government bond (34.33); Central Bank Bond (39.79); Policy Bank Bond (25.08); Commercial Bank Bond (2.52); corporate bond (2.95); short-term finance bond (3.18); The proportion of bonds issued by firms is 5.67%.

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295

Source: China Government Securities Depository Trust & Clearing Company (CDC), July 2006.

References Ang, J.S., Fatemi, A., Tourani-Rad, A., 1997. Capital structure and dividend policies of Indonesian firms. Pacific-Basin Finance Journal 5, 87–103. Antoniou, A., Guney, Y., Paudyal, K., 2006. The determinants of debt maturity structure: evidence from France, Germany and the UK. European Financial Management 12, 161–194. Arellano, M., Bover, O., 1995. Another look at the instrumental variable estimation of error-components models. Journal of Econometrics 68, 29–51. Arslan, O., Karan, M.B., 2006. Ownership and control structure as determinants of corporate debt maturity: a panel study of an emerging market. Corporate Governance: An International Review 14, 312–324. Barclay, M.J., Smith, C.W., 1995. The maturity structure of corporate debt. Journal of Finance 50, 609–631. Baker, M., Greenwood, R., Wurgler, J., 2003. The maturity of debt issues and predictable variation in bond returns. Journal of Financial Economics 70, 261–291. Barnea, A.N., Haugen, R.A., Senbet, L.W., 1980. A rationale for debt maturity structure and call provisions in the agency theoretic framework. Journal of Finance 35, 1223–1234. Berger, A., Espinosa-Vega, M.A., Frame, W.S., Miller, 2005. Debt maturity, risk, and asymmetric information. Journal of Finance 60, 2895–2923. Billett, M.T., King, T.D., Mauer, D., 2007. Growth opportunities and the choice of leverage, debt maturity, and covenants. Journal of Finance 62, 697–730. Blundell, R.W., Bond, S.R., 1998. Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics 87, 115–143. Brick, I.E., Ravid, S.A., 1985. On the relevance of debt maturity structure. Journal of Finance 40, 1423–1437. Brick, I.E., Ravid, S.A., 1991. Interest rate uncertainty and the optimal debt maturity structure. Journal of Financial and Quantitative Analysis 26, 63–82. Booth, L., Aivazian, V., Demirguc-Kunt, A., Maksimovic, A., 2001. Capital structures in developing countries. Journal of Finance 56, 87–130. Cai, J., Cheung, Y.L., Goyal, V.K., 1999. Bank monitoring and the maturity structure of Japanese corporate debt issues. Pacific-Basin Finance Journal 7, 229–250.

296

K. Cai et al. / Pacific-Basin Finance Journal 16 (2008) 268–297

Chen, G., Firth, M., Gao, D.N., Rui, O.M., 2006. Ownership structure, corporate governance, and fraud: evidence from China. Journal of Corporate Finance 12, 424–448. Datta, S., Iskandar-Datta, M., 2000. Debt structure adjustments and long-run stock price performance. Journal of Financial Intermediation 9, 427–453. Datta, S., Iskandar-Datta, M., Raman, K., 2005. Managerial stock ownership and the maturity structure of corporate debt. Journal of Finance 60, 2333–2350. Dennis, S., Nandy, D., Sharpe, I.G., 2000. The determinants of contract terms in bank revolving credit agreements. Journal of Financial and Quantitative Analysis 35, 87–110. Diamond, D.W., 1991. Debt maturity structure and liquidity risk. Quarterly Journal of Economics 106, 709–737. Diamond, D.W., Rajan, R., 2001. Banks, short term debt, and financial crises: theory, policy implications, and applications. Proceedings of Carnegie Rochester Series on Public Policy 54, 37–71. Esho, N., Lam, Y., Sharpe, I.G., 2002. Are maturity and debt type decisions interrelated? Evidence from Australian firms in international capital markets. Pacific-Basin Finance Journal 10, 549–569. Fan, D.K.K., Lau, C.M., Young, M., 2007. Is China's corporate governance beginning to come of age? The case of CEO turnover. Pacific-Basin Finance Journal 15, 105–120. Firth, M., Fung, P.M.Y., Rui, O.M., 2006. Corporate performance and CEO compensation in China. Journal of Corporate Finance 12, 693–714. Flannery, M.J., 1986. Asymmetric information and risky debt maturity choice. Journal of Finance 41, 19–37. Graham, J.R., Harvey, C.R., 2001. The theory and practice of corporate finance: evidence from the field. Journal of Financial Economics 60, 187–243. Greene, W.H., 2003. Econometric Analysis. Pearson Education. Guedes, J., Opler, T., 1996. The determinants of the maturity of corporate debt issues. Journal of Finance 51, 1809–1833. Gul, F.A., 1999. Government share ownership, investment opportunity set and corporate policy choices in China. PacificBasin Finance Journal 7, 157–172. Guney, Y., Ozkan, A., 2005. New insights on the importance of agency costs for corporate debt maturity decisions. Applied Financial Economics Letters 1, 233–238. Hart, O., Moore, J., 1994. A theory of debt based on the inalienability of human capital. Quarterly Journal of Economics 109, 841–879. Hart, O., Moore, J., 1995. Debt and seniority: an analysis of the role of hard claims in constraining management. American Economic Review 85, 567–585. Huang, G., Song, F.M., 2006. The determinants of capital structure: evidence from China. China Economic Review 17, 14–36. Johnson, S.A., 2003. Debt maturity and the effects of growth opportunities and liquidity risk on leverage. Review of Financial Studies 16, 209–236. Kale, J.R., Noe, T.H., 1990. Risky debt maturity choice in a sequential game equilibrium. Journal of Financial Research 13, 155–165. Kane, A., Marcus, A.J., McDonald, R.L., 1985. Debt policy and the rate of return premium to leverage. Journal of Financial and Quantitative Analysis 20, 479–499. Kim, C.S, Mauer, D.C., Stohs, M.H., 1995. Corporate debt maturity policy and investor tax-timing options: theory and evidence. Financial Management 24, 33–45. La Porta, R., Lopez-De-Silanes, F., Shleifer, A., Vishny, R.W., 1997. Legal determinants of external finance. Journal of Finance 52, 1131–1150. Lam, S., Du, J., 2004. Information asymmetry and estimation risk: preliminary evidence from Chinese equity markets. Pacific-Basin Finance Journal 12, 311–331. Leland, H.E., Toft, K.B., 1996. Optimal capital structure, endogenous bankruptcy, and the term structure of credit spreads. Journal of Finance 51, 987–1019. Lewis, C.M., 1990. A multiperiod theory of corporate financial policy under taxation. Journal of Financial and Quantitative Analysis 25, 25–43. Mitchell, K., 1993. The debt maturity choice: an empirical investigation. Journal of Financial Research 16, 309–320. Morris, J.R., 1992. Factors Affecting the Maturity Structure of Corporate Debt. Working paper. University of Colorado at Denver. Myers, S.C., 1977. Determinants of corporate borrowing. Journal of Financial Economics 5, 146–176. Myers, S.C., Rajan, R.G., 1998. The paradox of liquidity. Quarterly Journal of Economics 113, 733–771. Ozkan, A., 2000. An empirical analysis of corporate debt maturity structure. European Financial Management 6, 197–212. Qi, D., Wu, W., Zhang, H., 2000. Shareholding structure and corporate performance of partially privatized firms: evidence from listed Chinese companies. Pacific-Basin Finance Journal 8, 587–610.

K. Cai et al. / Pacific-Basin Finance Journal 16 (2008) 268–297

297

Rajan, R.G., Zingales, L., 1995. What do we know about capital structure? Some evidence from international data. Journal of Finance 50, 1421–1460. Sarkar, S., 1999. Illiquidity risk, project characteristics, and the optimal maturity of corporate debt. Journal of Financial Research 22, 353–370. Stohs, M.H., Mauer, D.C., 1996. The determinants of corporate debt maturity structure. Journal of Business 69, 279–312. Titman, S., 1992. Interest rate swaps and corporate financing choices. Journal of Finance 47, 1503–1516. Titman, S., Wessels, R., 1988. The determinants of capital structure choice. Journal of Finance 43, 1–19. Warner, J.B., 1977. Bankruptcy costs: some evidence. Journal of Finance 32, 337–347. Wiwattanakantang, Y., 1999. An empirical study on the determinants of the capital structure of Thai firms. Pacific-Basin Finance Journal 7, 371–403.