The dynamic characteristics and influencing factors of debt structure of the public companies in China

The dynamic characteristics and influencing factors of debt structure of the public companies in China



Zhefan Piao, Xiaoqi Feng

School of Finance, Zhejiang University of Finance & Economics (China)



Received March 2013

Accepted June 2013


Piao, Z., Feng, X. (2013). The dynamic characteristics and influencing factors of debt structure of the public companies in China. Journal of Industrial Engineering and Management, 6(4), 876-894. http://dx.doi.org/10.3926/jiem.736



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Abstract:

Purpose: In a macroeconomic environment with the non-tradable shares reform, financial crisis, tax reform and monetary policy, to examine the dynamic characteristics and factors of the debt maturity structure, this research tends to offer an empirical analysis about Chinese listed companies in different industries.

Design/methodology/approach: Learned from Leary (2009), Voutsinas and Werner (2011), this study designs a model of debt maturity structure with an unbalanced panel data set. Consists of 1352 Chinese listed companies with 8124 observations during the period of 2003‑2011, the sample passed Hausman test, and the findings support the fixed effects model.

Findings: Besides the factors that have been confirmed by previous researches, debt maturity structure is also sensitive to other factors, such as economic expectations, monetary policy, financial restrictions and changes in tax rates.

Research limitations/implications: There are still many cases, which affect the debt maturity structure, are worth of further exploring, for instance, the impact of lagged monetary policy, the determinants of short-term debt ratio and the cost of operating.

Practical implications: From the macro point of view, research in this area enables the government to introduce more suitable policies that direct and promote the development of the bond market. From the micro point of view, it spurs corporations to choose proper finance structure. Firms can learn from the research to adopt the efficient method and term of financing as well as debt structure.

Originality/value: In some way, conclusions of this paper contribute to the study of dynamic characteristics and factors of debt maturity structure in Chinese listed companies.

Keywords: maturity structure, debt structure, dynamic characteristics, panel data

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1. Introduction

Since the Miller and Modigliani (1958) carried on the study of capital structure about modern enterprises, a large number of documents concerning the examination of capital structure theory appeared. With the development and innovation of the capital structure theory, the focus is gradually turning from the basic choice of leverage to the debt structure characteristics. And based on the development of the capital structure theory, there formed the trade-off theory, agency costs theory, information asymmetry hypothesis and tax hypothesis of debt maturity structure (Ho & Robinson, 1994). These basic theories have led to a series of derivate research on the determinants of debt structure (Bradley, Gregg & Han Kim, 1984; Titman & Wessels, 1988).

Compared with the mature financing environment abroad, the financing environment in China, under the economic transformation, is immature. The immature market has many restrictions that make the debt structure of the listed companies in China more complicated, for instance, the imbalanced development of capital markets and imperfect protection of investor. Especially after the financial crisis, what are the dynamic characteristics of the debt maturity structure in China's listed companies? Which factors affect the debt maturity structure? Can the western theories of debt maturity structure explain the debt structure problems in China? All these issues need theoretical analysis and empirical testing.

In this context, focusing on the debt structure of listed companies in China, this paper theoretically analyzes the impacts of macroeconomic factors and microeconomic factors on the debt structure. Furthermore, using a data set of 1352 companies during 2003-2011, this study analyzes the debt maturity structure empirically to test the dynamic characteristics and factors of the debt maturity structure in China. Researches in this area, from the macro point of view, enable the government to introduce more suitable policy to direct and promote the development of the bond market; from the micro point of view, they spur corporations to choose proper finance structure. Firms can learn from the researches to choose the efficient method and term of financing as well as debt structure.

2. Literature Review

Begin with the conclusion of Merton (1974), who held that the debt maturity structure is independent of enterprise value, many scholars began to study the debt maturity structure as well as the factors affect it. Now researches about the dynamic characteristics of debt maturity structure at home and abroad mainly focus on the following three aspects:

  • Researches on debt maturity structure theory. According to the existing literatures, the theory of debt maturity structure falls into three categories: agency costs (Jensen, 1986), the deadline supporting theory (Hart & Moore, 1994), and information economics theory (Flannery, 1986; Kale & Noe, 1990; Diamond, 1991). The agency cost theory holds that the liabilities operations of modern enterprise caused the conflicts between creditors and shareholders, and accompany the conflicts, the agency costs of debt appeared. The main views of agency cost theory are: First, the short-term debt helps companies to avoid the overinvestment problems and solve the problems of insufficient investments; second, debt maturity increased with the increase of the firm size. The main views of the deadline supporting theory are: the debt maturity should be corresponded with the terms of the corporate assets, and debt maturity has an inverse relationship with asset depreciation rate. The main point of information economics theory believes that, the risk of the borrower is positively correlated with the debt maturity, and companies generally prefer to issue short-term debts. What’s more, debt maturity is a non-monotonic function of the enterprise risk; borrowers of lowest risk or highest risk both have more short-term debts, while borrowers with moderate risk have more long-term debts.

  • Tests of debt maturity theory. The test of debt maturity structure theory mainly concentrated on the trade-off theory (Miller, 1977; Myers, 2001) and the pecking order theory (Myers, 1984; Myers & Majluf, 1984). The trade-off theory holds that, instead of equity finance, debt finance can increase the market value of the enterprise due to the exits of the tax shield. But the rising debt levels will increase the financial cost (Philosophov & Philosophov 2005; Bany-Ariffin, Mat Nor & McGowan Jr., 2010), and intensify the agency conflicts of the companies (Jensen & Meckling, 1976 for; Frankfurter & Philippatos, 1992). The pecking order theory believes that, financial managers have the information that investors do not have. Therefore, enterprises tend to prefer internal finance, which do not suffer from information asymmetry, instead of external finance. If external finance is still needed, companies will issue bonds first. They insist that specific target capital structure is inexistence. In the past 30 years, researches about the validity of these two theories have not been unanimously approved so far (Hovakimian, Hovakimian & Tehranian, 2004; Huang & Song, 2006; Kayo & Kimura, 2011; Gaud, Hoesli, & Bender, 2006; Frank & Goyal, 2004; Fuxiu, Yaohui, Zhengfei & Yan, 2008; Leary, 2009).

  • Factors affect the debt maturity structure. The existing literatures suggest that, the main factors affecting debt maturity structure are firm size (Taub, 1975; Chen & Strange, 2005; Zuoping-Xiao, 2009; Zengfu-Li, Yan-Gu & Yujun-Lian, 2012), profitability(Titam & Wessels, 1988; Nunes & Serrasqueiro, 2007), non-debt tax shield (Bradley et al., 1984; Lord & McIntyre, 2003), tangible assets (Titam & Wessels, 1988; Gaud et al., 2006), accounts payable (Atanasova & Wilson, 2004; Steijvers, 2004), tax rates (Gordon & Lee, 2001; Zuoping-Xiao, 2009; Zengfu-Li, Yan-Gu & Yujun-Lian, 2012), ownership (Huacheng, Chunling & Chuan, 2007; Kun & Junrui, 2012), Bank of dependence (Carpenter, Fazzari & Petersen, 1994; Cantillo & Wright, 2000; Leary, 2009; Voutsinas & Werner, 2011) and so on. However, the positive or negative impact of these factors is a big controversial issue. Recently, the studies of Bougheas, Mizen and Yalcin (2006), Faulkender and Petersen (2006), Leary (2009) and Qinglu, Xiang and Qingchuan (2012) found the importance of financial constraints and monetary policy.

There are a large number of literatures researching on capital structure of listed companies in China, but rarely considering the factors and the dynamic characteristics of debt maturity structure under the environment of shareholder structure reform (begin in 2005), financial crisis (2008), tax rate reform (the new corporate income tax law passed through on March 16, 2007, and implemented on January 1, 2008) and monetary policy.

3. Methodology and data set

3.1. Sample

Consisting of companies listed in the A-share and B-share stock market of China over the period 2003-2011, the data set of this paper was taken from the CSMAR Solution database, and was filtered by following limitations:

  • Excluding the listed companies in financial sector, because the accounting management and the liabilities characteristics of the enterprises in financial sector and other enterprises are different.

  • Excluding the listed companies of ST * ST, SST, S * ST and S, because the financial structure of these companies prevalently have problems.

  • Excluding the companies with missing accounting data and abnormal stock price changes, and the assets value of it unchanged.

The resulting data set consists of 8124 observations (see Table 1).

Table 1 presents the changes in the number of the state-owned enterprises and non-state-owned enterprises in various sectors during 2003-2011. As seen from the table, the number of non-state-owned enterprises was significantly greater than the state-owned enterprises after the reform of shareholder structure. And this trend becomes more apparent after 2008.

Industry

Nature of enterprise

2003

2004

2005

2006

2007

2008

2009

2010

2011

Food and beverage

Non state-owned enterprises

9

12

14

17

19

22

45

56

61

State owned enterprise

42

42

41

40

33

35

17

17

20

Petrochemical

Non state-owned enterprises

10

16

11

20

33

44

85

130

179

State owned enterprise

94

100

99

98

98

95

56

56

36

Electronics

Non state-owned enterprises

8

9

9

12

19

31

43

81

97

State owned enterprise

30

32

33

32

43

37

22

26

25

Metal and nonmetal

Non state-owned enterprises

11

14

16

17

29

35

66

110

137

State owned enterprise

80

81

79

86

85

83

55

40

31

Machinery and equipment

Non state-owned enterprises

27

32

38

41

69

81

147

223

313

State owned enterprise

128

140

139

141

137

139

97

112

100

Pharmaceutical and biotech

Non state-owned enterprises

14

25

24

23

32

45

68

82

106

State owned enterprise

55

59

56

59

52

38

21

22

19

Real estate

Non state-owned enterprises

28

30

31

39

33

38

67

78

83

State owned enterprise

67

68

66

67

64

64

48

36

30

Wholesale and retail

Non state-owned enterprises

9

11

12

17

21

33

62

74

89

State owned enterprise

72

74

74

70

68

60

33

32

26

Total

Non state-owned enterprises

116

149

155

186

255

329

583

834

1065

State owned enterprise

568

596

587

593

580

551

349

341

287

Total

684

745

742

779

835

880

932

1175

1352

Table 1. Distribution table of companies in different industries over the period 2003-2011

Figure 1 and 2 shows that, the average long-term debt ratio of state-owned enterprises is higher than the non-state-owned enterprises after 2006, and the reverse happens with the average short-term debt ratio after 2007. In general, the Asset-liability ratios of the state-owned enterprises are higher than that of the non-state-owned enterprises. This can be explained as that, due to the existence of the natural link between the state-owned enterprises and the five state-owned big banks, the state-owned enterprises faced better financing environment than the non-state-owned enterprises.

Figure 3 shows the changes in the asset-liability ratio and short-term liabilities rate of state-owned enterprises and non-state-owned enterprises during the year 2003-2011. As seen in the figure, there is a strong positive relationship between the asset-liability ratio and short-term debt ratio to both state-owned enterprises and non-state-owned enterprises. There are two questions need to be thought about: First, the corporate bond market in China is underdeveloped, and corporate debt finance depends mainly on the currency market. Then, although short-term debt can reduce the cost of capital, it may bring financial distress.


Figure 1. Long-term debt ratio of the state-owned enterprises and non-state-owned enterprises over the period 2003-2011

Figure 2. Short-term debt ratio of the state-owned enterprises and non-state-owned enterprises over the period 2003-2011


Figure 3. The tendency of the Asset-liability ratio and short-term debt ratio of the state-owned enterprises and non-state-owned enterprises over the period 2003-2011

3.2. Variables

This research is designed to examine the dynamic characteristics and factors of the debt maturity structure in various industries of Chinese listed companies. It takes into consideration of important changes in the macroeconomic environment, like tradable share, financial crisis, tax reform and monetary policy. Inspired by existing domestic and international literatures, following variables have been set with the consideration of the macroeconomic environment in China. Specific definition of the variables is shown in Table 2.

Variable

Variable definition

Leverage

Total debts/ total assets

Short-term leverage

(Commercial paper + short-term borrowings + short-term corporate bonds + long -term debt and maturities within 1 year) / total assets

Long-term leverage

(Long-term corporate bonds + long-term debt)

Bankdep1

Dummy variable. 1 if total debt increased than the year before, and 0 if not.

Bankdep2

Dummy variable. 1 if current liabilities increased than the year before, and 0 if not.

Money Policy

Dummy variable. 1 if interest rate increased than the year before, and 0 if not

Money Policy(t-1)

Dummy variable. 1 if the growth rate of total loans in all banks increased than the year before, and 0 if not and unchanged.

Tangfassets

Total tangible fixed assets/total assets

EBIT

EBIT/total assets

Retearnings

(Profit reserves+various voluntary reserves +retained earnings carnings forward)/total assets

Non-debt tax shields

Over the period 2003-2008[PROFIT-(T/0.33)]/total assetsover the period 2009-2011[PROFIT-(T/0.25)]/total assets. PROFIT is the net profit before tax, and T is the taxes of the sample corporate.

Accountspay

(Notes payable and accounts payable)/total assets

Logsales

Natural logarithm of sales and operating revenue

Equity to debt ratio

Equity/Debt

Nature of enterprise

Dummy variable. 1 if state-owned corporate, and 0 if not (A firm is classified as a state-owned corporate only if the ownership share of the state is more than 0)

Gdp Growth

[(Gdpt-Gdpt-1)/ Gdpt-1]*100%

Industryi

Dummy variable. The food and beverage industry has the value of 1, petrochemical industry of 2, electronics industry of 3, metal and nonmetal industry of 4, machinery and equipment industry of 5, pharmaceutical and biotech industry of 6, real estate industry of 7, and wholesale and retail industry of 8.

Table 2. Variables definitions

3.3. Methodology

According to the dynamic characteristics of the debt maturity structure (asset-liability ratio, long-term debt ratio and short-term debt ratio) of the listed companies in China, this paper builds a panel data model. Learned from Leary (2009), Voutsinas and Werner (2011), we designed the following models:

,

(1)

,

(2)

Where yi,t is for Leverage, Short-term leverage and Long-term leverage; Bankdep1 is for bank dependence 1; Bankdep2 is for bank dependence 2; Monetarypolicy is monetary policy; βi,t is the coefficient of xi,t; a is the constant term; xi,t is for Tangfassets, EBIT, Retearnings, Non-debt overtax shields, Accountspay, Logsales, Gdp Growth and Equity to debt over ratio; Wt is a dummy variable, and 1 If it belonging to the t cross-section, and 0 if not, t=1,2,…T; Di is a dummy variable, and 1 If it belonging to the i cross-section, and 0 if not, i=1,2; Industry is a dummy variable; ui is the fixed effects; εi,t is the residuals.

4. Results and discussion

4.1. Descriptive statistics

Table 3 depicts the results about the descriptive statistics of the financial indicators in the panel data set, which consists of 8124 observations of different ownership over the period 2003-2011. Among them, the number of state-owned enterprises observations is 4452 and non-state-owned enterprises is 3672. The average ratio of leverage, long-term leverages and short-term leverage of state-owned enterprises are 0.527, 0.072 and 0.455, higher than the non-state-owned enterprises of 0.449, 0.065 and 0.3844. And the Retearnings of non-state-owned enterprises is at an average of 0.073, significantly higher than the state-owned enterprises (-0.112). What’s more, the average equity to debt ratio of non-state-owned enterprises is 2.443, significantly higher than the state-owned enterprises (1.487). As a result, the asset quality of non-state-owned enterprises is better than that of state-owned enterprises.

Nature of enterprise


Leverage

Long-term leverage

Short-term leverage

Tangfassets

EBIT

Retearnings

Non-debt tax shields

Accountspay

Logsales

Equity to debt ratio

Non
state-owned enterprises

Mean

0.4495

0.0650

0.3844

0.9625

0.0675

0.0729

0.0584

0.1225

2.0844

2.4433

N

3672

3672

3672

3672

3672

3672

3672

3672

3666

3672

Std.

0.2388

0.0963

0.1995

0.0344

0.0562

0.9893

0.0592

0.0955

0.5748

3.6322

Min

0.0203

0.0000

0.0117

0.7555

-0.3979

-58.150

-0.4324

0.0000

-1.4407

-0.8325

Max

5.9700

2.2968

3.6732

1.0000

0.4927

0.7171

0.4424

0.5621

4.2097

48.359

State owned enterprise

Mean

0.5269

0.0722

0.4547

0.9670

0.0619

-0.1128

0.0507

0.1235

2.1672

1.4874

N

4452

4452

4452

4452

4452

4451

4452

4451

4444

4452

Std.

1.2442

0.1600

1.1128

0.0364

0.5930

6.4025

0.5936

0.0998

0.5980

1.9868

Min

0.0283

0.0000

0.0260

0.4930

-1.0210

-251.76

-1.1183

0.0000

-0.1823

-0.9879

Max

82.5596

8.8267

73.732

1.0000

39.313

12.773

39.313

0.5544

4.4948

34.361

Table 3. The descriptive statistics of the panel data for enterprise's financial indicators in different ownerships (where m is the mass, x is the displacement)

It depicts the descriptive statistics results of the financial indicators about different industries during the year of 2003-2011 in Table 4.

Industry


Leverage

Long-leverage

Short-m leverage

Tangfassets

EBIT

Retearnings

Non-debt tax shields

Accountspay

Logsales

Equity to debt ratio

The food and beverage

Mean

0.451

0.044

0.407

0.950

0.063

0.058

0.052

0.080

2.081

2.089

N

542

542

542

542

542

542

542

542

542

542

Std.

0.210

0.058

0.196

0.043

0.080

0.235

0.086

0.068

0.557

3.154

Min

0.027

0.000

0.019

0.772

-0.311

-1.659

-0.325

0.000

0.517

-0.459

Max

1.848

0.350

1.846

1.000

0.392

0.647

0.364

0.414

3.855

35.440

Petrochemical

Mean

0.458

0.084

0.374

0.963

0.061

0.102

0.047

0.110

2.135

2.104

N

1260

1260

1260

1260

1260

1260

1260

1260

1260

1260

Std.

0.189

0.102

0.168

0.035

0.063

0.128

0.066

0.075

0.460

3.204

Min

0.020

0.000

0.019

0.773

-0.322

-1.319

-0.362

0.000

0.102

-0.051

Max

1.054

0.535

1.034

1.000

0.502

0.576

0.497

0.422

3.980

48.360

Electronics

Mean

0.371

0.048

0.322

0.970

0.054

0.092

0.045

0.118

1.955

3.191

N

589

589

589

589

589

589

589

589

590

589

Std.

0.183

0.073

0.170

0.025

0.064

0.165

0.067

0.084

0.529

4.156

Min

0.028

0.000

0.012

0.802

-0.509

-1.820

-0.528

0.000

-1.074

0.044

Max

0.958

0.517

0.907

1.000

0.245

0.551

0.233

0.548

3.784

34.279

Metal and nonmetal

Mean

0.516

0.102

0.413

0.968

0.064

0.096

0.049

0.117

2.440

1.476

N

1055

1055

1055

1055

1055

1054

1055

1054

1055

1055

Std.

0.175

0.097

0.161

0.031

0.058

0.192

0.061

0.076

0.671

2.277

Min

0.032

0.000

0.018

0.796

-0.265

-4.287

-0.284

0.000

0.487

0.020

Max

0.981

0.479

0.971

1.000

0.599

0.591

0.561

0.422

4.171

30.489

Machinery and equipment

Mean

0.459

0.040

0.418

0.963

0.059

0.086

0.052

0.166

2.115

2.045

N

2104

2104

2104

2104

2104

2104

2104

2104

2103

2104

Std.

0.197

0.071

0.182

0.032

0.051

0.165

0.054

0.112

0.585

2.826

Min

0.028

0.000

0.028

0.695

-0.431

-2.795

-0.460

0.000

-0.182

-0.560

Max

2.271

2.083

0.980

1.000

0.352

0.510

0.347

0.562

4.495

34.978

Pharmaceutical and biotech

Mean

0.402

0.045

0.357

0.951

0.074

0.119

0.064

0.092

1.950

2.741

N

800

800

800

800

800

800

800

800

800

800

Std.

0.194

0.060

0.180

0.039

0.069

0.170

0.072

0.077

0.502

3.618

Min

0.028

0.000

0.023

0.741

-0.257

-0.962

-0.285

0.000

0.385

0.035

Max

0.966

0.413

0.954

1.000

0.493

0.717

0.442

0.459

3.740

34.362

Real estate

Mean

0.707

0.141

0.566

0.986

0.089

-0.952

0.081

0.068

1.911

0.940

N

937

937

937

937

937

937

937

937

925

937

Std.

2.692

0.316

2.404

0.031

1.287

14.058

1.287

0.064

0.579

1.361

Min

0.045

0.000

0.045

0.493

-1.021

-251.76

-1.118

0.000

-1.441

-0.988

Max

82.560

8.827

73.733

1.000

39.313

12.774

39.313

0.454

3.856

21.006

Wholesale and retail

Mean

0.553

0.048

0.504

0.965

0.056

0.080

0.046

0.164

2.337

1.191

N

837

837

837

837

837

837

837

837

835

837

Std.

0.182

0.067

0.175

0.040

0.052

0.132

0.054

0.124

0.581

1.461

Min

0.069

0.000

0.063

0.750

-0.398

-1.507

-0.432

0.000

0.511

-0.114

Max

1.128

0.411

0.931

1.000

0.365

0.617

0.359

0.560

4.210

13.579

Total

Mean

0.492

0.069

0.423

0.965

0.064

-0.029

0.054

0.123

2.130

1.919

N

8124

8124

8124

8124

8124

8123

8124

8123

8110

8124

Std.

0.936

0.135

0.835

0.036

0.441

4.786

0.441

0.098

0.589

2.890

Min

0.020

0.000

0.012

0.493

-1.021

-251.76

-1.118

0.000

-1.441

-0.988

Max

82.560

8.827

73.733

1.000

39.313

12.774

39.313

0.562

4.495

48.360

Table 4. The descriptive statistics of the financial indicators panel data in different industries

Among them, the number of food and beverage industry observations is 542, the petrochemical industry is 1260, the electronics industry is 589, the metal and nonmetal industry is 1055, the machinery and equipment industry is 2104, the pharmaceutical and biotech industry is 800, the real estate industry is 937, and the wholesale and retail industry is 837. The average ratio of leverage, long-term leverages and short-term leverage of the real estate industry are 0.527, 0.072 and 0.455, apparently higher than any other industries. And the average Accountspay of machinery and equipment industry and the wholesale and retail industry are 0.166 and 0.164, generally higher than any other industries. In addition, the electronic industry (3.19) has the highest equity to debt ratio and the real estate industry (0.94) the lowest. So asset structure of the real estate industry is different from that of other industries, and its debt structure was significantly greater than other industries.Alpha

Table 5 and Table 6 are the results about the Pearson correlation test of each variable. Leverage is significantly positively correlated with Long-term leverage and Short-term leverages (0.776, 0.995); especially the correlation between Leverage and Short-term leverage almost approaches 1. Distinctively, both Leverage and Short-term leverage have strong positive relationship with EBIT, Non-debt over tax shields, Accountspay, and Logsales, while Long-term leverage and Accountspay are significantly negatively related. This illustrates that, corporations with stronger profitability have higher asset-liability ratio and short-term debt rate, and mainly depend on short-term debt to solve the accounts payable rate problems. Leverage, Long-term leverage, and Short-term leverage have a significantly negative correlation with Retearnings and Equity to debt ratio (-0.161, -0.182 and -0.151), which indicates that the debt maturity structure can be reduced as the Retearnings and equity increased.


Leverage

Long- leverage

Short- leverage

Tangfassets

EBIT

Retearnings

Non-debt tax shields

Accountspay

Logsales

Leverage

1









Long-term leverage

.776**

1








Short-term leverage

.995**

.707**

1







Tangfassets

.011

.043**

.005

1






EBIT

.955**

.707**

.955**

.018

1





Retearnings

-.57**

-.418**

-.573**

-.020

-.57**

1




Non-debt tax shields

.950**

.703**

.951**

.022*

1.00**

-.576**

1



Accountspay

.052**

-.114**

.077**

-.004

-.02*

.028*

-.023*

1


Logsales

.324**

.176**

.274**

.081**

.18**

.206**

.166**

.366**

1

N

8124

8124

8124

8124

8124

8124

8124

8124

8124

** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed)

Table 5. Correlations matrix, table of Pearson correlation test for each variable

Table 6 shows that Leverage, Long-term leverage and Short-term leverage are inversely related to industry and the ownership of enterprise significantly, indicating that the debt levels of mechanical and equipment, medical biology, real estate, and wholesale and retail industry are higher than those of the food, petrochemical, electronics and metal industry. Moreover, the debt level of state-owned enterprises is higher than that of the non-state-owned enterprises. Leverage and Short-term leverage have a significantly negative correlation with the Year (-0.032 and -0.039), showing that, the debt financing circumstance of enterprises in the sample, which is affected by monetary policy and financial restrictions, is increasingly tightening over the period 2003-2011.


Leverage

Long-term leverage

Short-term leverage

Industry

Year

Nature of enterprise

Equity to debt ratio

Leverage

1







Long-term leverage

.776**

1






Short-term leverage

.995**

.707**

1





Industry

.052**

.022*

.055**

1




Year

-.032**

.020

-.039**

-.041**

1



Nature of enterprise

.041**

.026*

.042**

.006

-.457**

1


Equity to debt ratio

-.161**

-.182**

-.151**

-.106**

.195**

-.165**

1

N

8124

8124

8124

8124

8124

8124

8124

** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed)

Table 6. Correlations matrix, table of Pearson correlation test for each variable

4.2. Regressions results

First, we analyze the dynamic characteristics and factors of Leverage, Short-term leverage and Long-term leverage under the influence of the current monetary policy, and the main results of fixed effects regression are shown in Table 7.

Leverage is positively correlated with Bankdep1, EBIT, Accountspay and Logsales significantly, indicating that enterprises with bigger asset size and higher profitability have easier access to bank loans, thus resulting in an increase of asset-liability ratio. This is consistent with the conclusions of many researches both in China and abroad (Leary, 2009; Voutsinas & Werner, 2011; Xunan-Feng, 2012). Leverage has a significantly positive association with Year2005, Year2006, Year2008, Year2009 and Year2011, stating that regardless of the financial constraints and the impact of monetary policy, the listed companies in China tend to depend on long-term bank debt finance. Significantly, Leverage is negatively related to Retearnings and Equity to debt ratio, indicating that the higher the equity ratio of the corporation the lower the asset-liability ratio is. In addition, Leverage was negatively associated with the Non-debt tax shields and Nature of enterprise, which declaring that the asset-liability ratio reduced due to the decline of tax ratio. And relative to non-state-owned enterprises, the state-owned enterprises push down the asset-liability ratio more. The value of R2 (within) and R2 (between) are 0.574 and 0.689, which indicate a good fit for the model created. And the P value of Hausman test is 0, so a fixed effects model was supported.

Variable

Leverage

Short-term leverage

Long-term leverage

Bankdep1


Bankdep2


Money Policy


Tangfassets


EBIT


Retearnings


Non-debt tax shields


Accountspay


Logsales


Nature of enterprise


equity to debt ratio


GDPg


Year2004


Year2005


Year2006


Year2007

Year2008


Year2009


Year2010


Year2011


Constant


0.0318 ***

(9.58)

0.0025

(0.78)

0.0065

(0.84)

0.0651

(1.74)

3.9874***

(28.5)

-0.2483***

(-37.5)

-4.2197***

(-30.87)

0.1814***

(11.2)

0.0654***

(14.07)

-0.0064**

(-2.34)

-0.0347***

(-42.39)

0.0031

(1.56)

0.0107

(1.39)

0.0236***

(5.42)

0.0346***

(7.66)

(omitted)

0.0149***

(3.34)

0.0376***

(7.54)

0.0054

(0.67)

0.0355***

(3.42)

0.2357***

(5.51)

0.0576***

(20.54)

-0.0539***

(-19.89)

0.0143**

(2.2)

0.0433

(1.38)

0.0167

(0.14)

-0.0923***

(-16.55)

-0.0299

(-0.26)

-0.1717***

(-12.59)

0.0177***

(4.52)

0.0006

(0.28)

-0.0090***

(-13.08)

-0.0011

(-0.65)

-0.0107

(-1.65)

0.0056

(1.52)

-0.0198***

(-5.22)

(omitted)

0.0133***

(3.54)

0.0312***

(7.43)

0.0146**

(2.18)

0.0182**

(2.09)

0.0265

(0.74)

-0.0257***

(-7.52)

0.0564***

(17.08)

-0.0078

(-0.99)

0.0217

(0.57)

3.9706***

(27.63)

-0.1560***

(-22.94)

-4.1898***

(-29.85)

0.3530***

(21.23)

0.0477***

(9.99)

-0.0070**

(-2.5)

-0.0257***

(-30.55)

0.0042*

(2.05)

0.0213***

(2.71)

0.0180***

(4.03)

0.0545***

(11.74)

(omitted)

0.0016

(0.35)

0.0064

(1.24)

-0.0092

(-1.13)

0.0173

(1.62)

0.2091***

(4.76)

R-sq: within

between

overall

corr(u_i, Xb)

sigma_u

sigma_e

rho

chi2

Hausman

0.5738

0.6886

0.6866

0.0316

0.1232

0.0713

0.7489

841.38

0.0000

0.1835

0.1934

0.2125

0.0559

0.0588

0.0601

0.4897

101.05

0.0000

0.4819

0.6909

0.6276

0.0476

0.1049

0.0733

0.6720

412.81

0.0000

P-values are in parenthesis; *** Indicates statistical significance at the 0.01 level; ** Indicates statistical significance at the 0.05 level. * Indicates statistical significance at the 0.10 level

Table 7. Fixed-effects Regression of model-1

Short-term leverage has significantly positive relation to Bankdep1, Money Policy and Logsales, declaring that corporations with more profit would increase the ratio of short-term debts in the crunch. This is in line with the report of Wenchao-Ma and Siyue-Hu (2012). Short-term leverage is positively and significantly related to Year2008, Year2009, Year2010, Year2011, showing that after the financial crisis, the deterioration of the operating environment led a number of listed companies to make up the gap of working capital by short-term debts. There is a negative and significant relationship between Short-term leverage and Bankdep2, Retearnings and Equity to debt ratio, indicating that corporations with high equity to debt ratio have low short-term debt ratio. In addition, Short-term leverage has a negative and significant correlation with Accountspay, stating that listed companies in China mainly rely on long-term liabilities to solve the problems of Accounts Payable. However, one needs to think over this question from the cost of working capital. The P value of Hausman test is 0, which supports the fixed effects model. While the value of R2 (within) and R2 (between) are 0.183 and 0.193, suggesting a poor fit for the model.

Long-term leverage has positive association with Bankdep2, EBIT, Accountspay, Logsales and GDPg, significantly. It indicates that enterprises with strong profitability have easier access to bank loans, thus led to an increase of their asset-liability ratio. While the enterprises will increase the ratio of long-term debt since they take an optimistic view about the economic situation. This is in accordance with many researching results in both China and aboard (Leary, 2009; Wenchao-Ma & Siyue-Hu, 2012; Xunan-Feng, 2012). Long-term leverage is positively and significantly related to Year2004, Year2005, Year2006, declaring that the long-term bank debt finance of listed companies in China is related to economic expectations and financial restrictions. Long-term leverage has significant and negative correlation with Bankdep1, Retearnings and Equity to debt ratio, showing that the higher the equity ratio of the corporate is, the lower the asset-liability ratio is. Moreover, Long-term leverage is negatively associated with the Non-debt tax shields and Nature of enterprise, which declaring that the asset-liability ratio reduced due to the decline of tax ratio. And relative to non-state-owned enterprises, the state-owned enterprises have lower asset-liability ratio. With a good fit for the model, the R2 (within) and R2 (between) have the value of 0.482 and 0.691, and the P value of Hausman test is 0, so a fixed effects model was accepted.

Table 8 shows the results of the fixed effects regression under the influence of monetary policy, which has been lagged once.

Leverage has significantly positive correlation with Bankdep1, EBIT, Accountspay and Logsales. It indicates that enterprises with bigger asset size and higher profitability have easier access to bank loans, thus result in an increase of their asset-liability ratio, which is consistent with the empirical results in Table 7. Significantly, Leverage is positively related to Year2006, but it is negatively correlated with Year2008, stating that the debt structure of listed companies in China is vulnerable to the impact of financial constraints and monetary policy.

Leverage is negatively correlated to Retearnings and Equity to debt ratio significantly, this indicates that the corporate with higher equity ratio would have a lower asset-liability ratio. In addition, Leverage was negatively associated with the Non-debt tax shields, Nature of enterprise and GDPg, which declaring that the asset-liability ratio reduced due to the decline of tax ratio. And relative to non-state-owned enterprises, the state-owned enterprises will have lower asset-liability ratio. At the same time, enterprises will decrease the asset-liability ratio since they have optimistic economic expectations, which is in contrast with the conclusions of Table 7. With a good fit for the model, R2 (within) and R2 (between) have the value of 0.572 and 0.690, and the P value of Hausman test is 0, so the regression model of Leverage supports the fixed effects model.

Variable

Leverage

Short-term leverage

Long-term leverage

Bankdep1


Bankdep2


Money Policy(t-1)


Tangfassets


EBIT


Retearnings


Non-debt tax shields


Accountspay


Logsales


Nature of enterprise


equity to debt ratio


GDPg


Year2004


Year2005


Year2006


Year2007

Year2008


Year2009


Year2010


Year2011

Constant


0.0265***

(9.11)

0.0030

(1.08)

0.0008

(0.09)

0.0296

(0.77)

2.9317***

(20.83)

-0.1408***

(-16.04)

-3.1893***

(-23.13)

0.1670***

(10.41)

0.0471***

(9.91)

-0.0044

(-1.76)

-0.0477***

(-47.21)

-0.0024***

(-3.37)

-0.0142

(-1.66)

-0.0054

(-1.6)

0.0204**

(2.21)

(omitted)

-0.0169***

(-5.28)

0.0028

(0.31)

-0.0072

(-0.8)

(omitted)

0.4261***

(10.65)

0.0509***

(19.49)

-0.0505***

(-20.12)

-0.0116

(-1.5)

0.0029

(0.08)

-0.3029**

(-2.4)

0.0044

(0.55)

0.2325*

(1.95)

-0.1605***

(-11.16)

0.0099**

(2.32)

0.0007

(0.29)

-0.0135***

(-14.89)

-0.0044***

(-6.75)

-0.0377***

(-4.93)

-0.0194***

(-6.39)

-0.0384***

(-4.64)

(omitted)

-0.0160***

(-5.58)

-0.0130

(-1.62)

-0.0016

(-0.19)

(omitted)

0.1602***

(4.46)

-0.0244***

(-7.42)

0.0535***

(16.96)

0.0124

(1.28)

0.0267

(0.62)

3.2346***

(20.38)

-0.1452***

(-14.67)

-3.4219***

(-22.01)

0.3274***

(18.11)

0.0372***

(6.93)

-0.0051

(-1.79)

-0.0342***

(-30.02)

0.0019**

(2.38)

0.0236**

(2.45)

0.0140***

(3.67)

0.0588***

(5.65)

(omitted)

-0.0009

(-0.24)

0.0158

(1.56)

-0.0056

(-0.55)

(omitted)

0.2660***

(5.89)

R-sq: within

between

overall

corr(u_i, Xb)

sigma_u

sigma_e

rho

chi2

Hausman

0.5716

0.6902

0.6842

0.0752

0.1175

0.0596

0.7955

973.51

0.0000

0.1871

0.1815

0.2125

0.0915

0.0627

0.0534

0.5794

228.47

0.0000

0.4666

0.6846

0.6175

0.0813

0.1017

0.0672

0.6964

463.87

0.0000

P-values are in parenthesis; *** Indicates statistical significance at the 0.01 level; ** Indicates statistical significance at the 0.05 level. * Indicates statistical significance at the 0.10 level

Table 8. Fixed-effects Regression of model-2

Short-term leverage has significantly positive relationship with Bankdep1 and Logsales, indicating that more profitable corporations would increase their ratio of short-term debt. Short-term leverage was positively associated with the Non-debt tax shields, declaring that the asset-liability ratio increased due to the decline of tax ratio. There is significant and negative relation between the Short-term leverage and Year2004, Year2005, Year2006, Year2008, and it is in contrast with the conclusions of Table 7. What’s more, Short-term leverage is significantly and negatively related to Bankdep2, Retearnings and Equity to debt ratio, indicating that corporations with higher equity ratio have lower short-term debt ratio. In addition, Short-term leverage has a negative correlation with EBIT, Accountspay and CDPg, and it is statistically significant. This states that listed companies in China mainly rely on long-term liabilities to solve the problems of Accounts Payable. At the same time, the enterprises will decrease the asset-liability ratio since they have optimistic expectations. The P value of Hausman test is 0, which supports the fixed effects model. While the value of R2 (within) and R2 (between) are 0.183 and 0.193, suggesting the poor fit for the model.

Significantly, Long-term leverage has positive association with Bankdep2, EBIT, Accountspay, Logsales and GDPg, significantly. It indicates that enterprises with strong profitability have easier access to bank loans, thus led to an increase of their asset-liability ratio. While the enterprises will increase the ratio of long-term debt since they take an optimistic view about the economic. This is in accordance with many research both in China and aboard (Leary, 2009; Wenchao-Ma & Siyue-Hu, 2012; Xunan-Feng, 2012). What’s more, Long-term leverage is positively related to Year2004, Year2005, Year2006, declaring that the long-term bank debt finance of listed companies is related to economic expectations and financial restrictions. Long-term leverage was significantly and negatively correlated with Bankdep1, Retearnings and Equity to debt ratio, showing that the higher the equity ratio of the corporate is, and the lower the asset-liability ratio is. In addition, Long-term leverage is negatively associated with the Non-debt tax shields, declaring that the asset-liability ratio reduced due to the decline of tax ratio. With a good fit for the model, the value of R2 (within) and R2 (between) are 0.467 and 0.685, and the P value of Hausman test is 0, so a fixed effects model of Long-term leverage was supported.

5. Conclusions

Based on the debt maturity structure theory and learning from Leary (2009), Voutsinas and Werner (2011), this study designs a model to investigate the dynamic characteristics and factors of debt maturity structure. It offers an empirical analysis of Chinese listed companies in different industries under a macroeconomic environment of non-tradable shares reform, financial crisis, tax reform and monetary policy. Using a panel data set of 8124 observations during 2003-2011, we found that, besides the enterprise characteristic factors, corporate debt maturity structure is sensitive to economic expectations, monetary policy, financial restrictions and changes in tax rates. The results of the empirical study show that:

  • The debt maturity structure of state-owned enterprises is significantly higher than that of non-state-owned enterprises, indicating that state-owned enterprises faced more favorable financing environment than the non-state-owned enterprises;

  • Corporations with larger scale of assets and more profitable have higher asset-liability ratio, and the phenomenon is reversed when it comes to the corporations with higher equity to debt ratio.

  • Long-term debt ratio and asset-liability ratio is related to economic expectations, monetary policy, financial restrictions and changes in tax rates.

  • After the financial crisis, the deterioration of the operating environment caused a number of listed companies in China to make up the gap of working capital by short-term debt.

  • Resulting from reduction in tax rate, the short-term debts of corporations increased, but the long-term debts ratio and asset-liability ratio dropped down.

  • Listed companies in China mainly rely on long-term liabilities to solve the problems of Accounts Payable, however, ones still need think over this question from the point of view of the cost of working capital.

Overall, in some way, the conclusions of this paper contribute to the study of dynamic characteristics and factors of debt maturity structure. However, there is still a lot can be further studied, for instance, the impact of lagged monetary policy, the determinants of short-term debt ratio and the cost of operating.

Acknowledgements

This study was supported by MOE (Ministry of Education in China) Project of Humanities and Social Sciences (Project No. 10YJA790143) 2012, and The Innovation Team of Zhejiang University of Finance and EconomicsProject No. CCJFH103.

References

Atanasova, C.V., & Wilson, N. (2004). Disequilibrium in the UK corporate loan market. Journal of Banking and Finance, 28, 595-614. http://dx.doi.org/10.1016/S0378-4266(03)00037-2

Bany-Ariffin, A.N., Mat Nor F., & McGowan Jr., C.B. (2010). Pyramidal structure, firm capital structure exploitation and ultimate owners dominance. International Review of financial Analysis, 19,151-164. http://dx.doi.org/10.1016/j.irfa.2010.03.002

Bougheas, S., Mizen, P., & Yalcin, C. (2006). Access to external finance: Theory and evidence on the impact of monetary policy and firm-specific characteristics. Journal of Banking and Finance, 30, 199-227. http://dx.doi.org/10.1016/j.jbankfin.2005.01.002

Bradley, M., Gregg, J., & Han Kim, E. (1984). On the existence of an optimal capital structure: Theory and evidence. The Journal of Finance, 39, 857878. http://dx.doi.org/10.1111/j.1540-6261.1984.tb03680.x

Cantillo, M., & Wright, J. (2000). How do firms choose their lenders? An empirical investigation. The Review of Financial Studies, 13, 155189. http://dx.doi.org/10.1093/rfs/13.1.155

Carpenter, R.E., Fazzari, S.M., & Petersen, B.C. (1994). Inventory (Dis) investment, internal finance fluctuations, and the business cycle. Brookings Papers in Economic Activity, 2, 75122. http://dx.doi.org/10.2307/2534655

Chen, J., & Strange, R. (2005). The determinants of capital structure: Evidence from Chinese listed firms. Economic Change and Restructuring, 38, 11-35. http://dx.doi.org/10.1007/s10644-005-4521-7

Diamond, D.W. (1991). Debt maturity structure and liquidity risk. Quarterly Journal of Economics, 106, 709-737. http://dx.doi.org/10.2307/2937924

Faulkender, M., & Petersen, M.A. (2006). Does the source of capital affect capital structure? The Review of financial Studies, 19, 45-79. http://dx.doi.org/10.1093/rfs/hhj003

Flannery, M.J. (1986). Asymmetric information and risky debt maturity choice. Journal of Finance, 41, 19–37.http://dx.doi.org/10.1111/j.1540-6261.1986.tb04489.x

Frank, M.Z., & Goyal, V.K. (2004). Capital structure decisions: Which factors are reliably important? Unpublished working paper. British Columbia, Vancouver BC.

Frankfurter, G.M., & Philippatos, G.C. (1992). Financial theory and the growth of scientific knowledge: From Modigliani and Miller to “an organizational theory of capital structure”. International Review of Financial Analysis, 1, 1-15. http://dx.doi.org/10.1016/1057-5219(92)90011-R

Fuxiu, J., Yaohui, Q., Zhengfei, L., & Yan, L. (2008). Changes of tax rates, the cost of bankruptcy and the adjustments of the capital structure. Economic Research Journal, 4, 99‑111.

Gaud, P., Hoesli, M., & Bender, A. (2006). Debt-equity choice in Europe. International Review of Financial Analysis, 16, 201-222. http://dx.doi.org/10.1016/j.irfa.2006.08.003

Gordon, R., Lee, Y. (2001). Do taxes affect corporate debt policy? Evidence from U.S. corporate tax return data. Journal of Public Economics, 82, 195–224. http://dx.doi.org/10.1016/S0047-2727(00)00151-1

Hart, O., & Moore, J. (1994). A theory of debt based on the inalienability of human capital. Journal of economics, 109, 841-879.

Ho, K., & Robinson, C. (1994). The relevance of financial policy in perfect capital markets. International Review of Financial Analysis, 3, 97111. http://dx.doi.org/10.1016/1057-5219(94)90019-1

Hovakimian, A., Hovakimian, G., & Tehranian, H. (2004). Determinants of target capital structure: The case of dual debt and equity issues. Journal of Financial Economics, 71, 517540. http://dx.doi.org/10.1016/S0304-405X(03)00181-8

Huacheng, W., Chunling, L., & Chuan, L. (2007). An empirical study about the impact of the controlling shareholder on the cash dividend policy of the listed companies. Management World, 1, 122-127.

Huang, G., & Song, F.M. (2006). The determinants of capital structure: Evidence from China. China Economic Review, 17, 1436. http://dx.doi.org/10.1016/j.chieco.2005.02.007

Jensen, M. (1986). Agency costs of free cash flow, corporate finance and takeovers. American Economics Review, 76, 323-339.

Jensen, M., & Meckling, W.H. (1976). Theory of the firms: Managerial behavior, agency costs, and ownership structure. Journal of Financial Economics, 42, 159186.

Kale, J.R., & Noe, T.H. (1990). Risky debt maturity choice in a sequential game equilibrium. Journal of Finance Research, 13(8), 155–165.

Kayo, E., & Kimura, H. (2011). Hierarchical determinants of capital structure. Journal of Banking and Finance, 35, 358-371. http://dx.doi.org/10.1016/j.jbankfin.2010.08.015

Kun, S. & Junrui, Z. (2012). Ultimate controlling rights and capital structure policies. Chinese Journal of Management, 3, 466-472.

Leary, M.T. (2009). Bank loan supply, lender choice, and corporate capital structure. Journal of Finance, 64, 1143-1185. http://dx.doi.org/10.1111/j.1540-6261.2009.01461.x

Lord, R.A., & McIntyre, J.E.Jr. (2003). Leverage, imports, profitability, exchange rates, and capital investment: A panel data study of the textile and apparel industries 19741987. International Review of Financial Analysis, 12, 287310. http://dx.doi.org/10.1016/S1057-5219(03)00010-3

Merton, R.C. (1974). On the pricing of corporate debt: the risk structure of interest rates. Journal of Finance, 29, 449–470.

Miller, M.H. (1977). Debt and taxes. The Journal of Finance, 32, 261–275.

Miller, M.H., & Modigliani, F. (1958). The cost of capital, corporation finance and the theory of investment. American Economic Review, 48, 261297.

Myers, S.C. (1984). The capital structure puzzle. Journal of Finance, 39, 575–592. http://dx.doi.org/10.2307/2327916

Myers, S.C. (2001). Capital structure. The Journal of Economic Perspectives, 15, 81–102. http://dx.doi.org/10.1257/jep.15.2.81

Myers, S.C., & Majluf, N.S. (1984). Corporate financing and investment decisions when firms have information that investors do not have. Journal of Financial Economics, 13, 187221. http://dx.doi.org/10.1016/0304-405X(84)90023-0

Nunes, P.J.M., & Serrasqueiro, Z.M. (2007). Capital structure of Portuguese service industries: A panel data analysis. The Service Industries Journal, 27, 549562. http://dx.doi.org/10.1080/02642060701411690

Philosophov, L.V., & Philosophov, V.L. (2005). Optimization of corporate capital structure: A quantitative approach based on a probabilistic prognosis of risk and time of bankruptcy. International Review of Financial Analysis, 14, 191-209. http://dx.doi.org/10.1016/j.irfa.2004.06.010

Qinglu, J., Xiang, K., & Qingchuan, H. (2012). Monetary Policy, Investment Efficiency and Equity. Economic Research Journal, 5, 96-106.

Steijvers T. (2004). Existence of credit rationing for SME's in the Belgian corporate bank loan market. Unpublished Working paper. Limburg University Centrum.

Taub, A. (1975). The determinants of the firm's capital structure. Review of Economics and Statistics, 57, 410416. http://dx.doi.org/10.2307/1935900

Titman, S., & Wessels, R. (1988). The determinants of capital structure choice. Journal of finance, 43, 1-19. http://dx.doi.org/10.1111/j.1540-6261.1988.tb02585.x

Voutsinas, K., Werner, R.A. (2011). Credit supply and corporate capital structure: Evidence from Japan. International Review of Financial Analysis, 20, 320-334. http://dx.doi.org/10.1016/j.irfa.2011.05.002

Wenchao-Ma & Siyue-Hu (2012). Monetary policy, credit channels and capital structure. Accounting Research, 11, 39-48.

Xunan-Feng. (2012). Debt and Expropriation: Evidence from China’s Family-Controlled Listed Firms. China Economic Quarterly, 4, 943-968.

Zengfu, L, Yan, G., & Yujun, L. (2012).Changes of tax rates, the cost of bankruptcy and the adjustments of the capital structure. Journal of Financial Research, 5, 136-150.

Zuoping, X. (2009).Sectors and the choice of the corporate’s debt maturity structure - empirical evidence from Chinese listed companies. Market Herald, 07, 50-56.




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Journal of Industrial Engineering and Management, 2008-2020

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