Open Access

Dynamics of financial leverage across firm life cycle in Chinese firms: an empirical investigation using dynamic panel data model

China Finance and Economic Review20164:19

https://doi.org/10.1186/s40589-016-0041-z

Received: 6 July 2016

Accepted: 26 October 2016

Published: 25 November 2016

Abstract

Background

This study tries to investigate how firms adjust their leverage policy across the firm’s life cycle. For this purpose the study uses an extensive set of data of 867 A listed Chinese non-financial firms over a nineteen years period (1996-2014).

Methods

The study employs Arellano-Bover/Blundell-Bond dynamic panel data model to estimate adjustment rate of leverage and its determinants in three different life stages of Chinese firms. We find that adjustment rate of leverage varies for different life stages.

Results

In accordance with trade off theory of capital structure this study reports a low-high-low pattern of leverage across growth, maturity and decline stage of firms’ life respectively. For total leverage, dynamic panel data reports highest adjustment rate for growing firms, followed by mature firms and firms in declining stage of their life.

Conclusions

Both short term and long term leverage report similar pattern of leverage’s adjustment rate across the three stages of life cycle. The study provides useful insight in a unique market setting of Chinese financial markets.

Keywords

Firm life cycle Leverage Chinese firms Dynamic adjustment GMM

Background

This study investigates the dynamics of firm’s capital structure across firm’s life cycle. The seminal theory of Modigliani and Miller 1958 about capital structure irrelevance has created a ground for the development of a number of theories to explain the dynamics of capital structure which were followed by different empirical studies to prove or deny these theories. However, there are many questions still unanswered regarding variation in capital structure policies. One important consideration in this regard is firm life cycle. The preference of financing alternatives and evolution of firm revolves around its life cycle (Fluck 2000, Rocca et al. 2011). Life cycle affects firm’s numerous characteristics. For example, Berger and Udell (1998) reported that life cycle affects the demand for financial products in market. Drobetza and Wanzenried (2006) provided evidence in dividend policy variations across firm’s life cycle, and more recently, Connor and Byrne (2015) reported the influence of firm’s life cycle on corporate governance. This shows that corporate leverage policy should be considered for investigation under the changing life cycle of the firm so that policy makers are able to make changes in leverage policies according to changing life cycle conditions. In the past studies, researchers used univariate proxy for firm life cycle such as firm age or size to study how capital structure respond to dynamics of its life cycle (Berger and Udell 1998). Moreover, there are studies that confirm that firm does follow a target capital structure and firms adjust their leverage to an optimal level. Notable studies in this regard include studies conducted by Ahsan et al. (2016a), Getzmann et al. (2014), Bradley et al. (1984), and Bontempi and Golinelli (2001). Ahsan et al. (2016b) and Bontempi and Golinelli (2001) used unit root testing to confirm that a percentage of firm follow a target capital structure while Getzmann et al. (2014) used dynamic panel data model (GMM) to find that firm adjust their capital structure and estimated an adjustment rate for firms using generalized method of moment (GMM). More recently, Tian et al. (2015) using a sample of public sector manufacturing Chinese firms and studied their leverage adjustment across firm life cycle. This study differs from the study conducted by Tian et al. (2015) in various aspects. First, this study uses firm age, sales growth, and dividend payout ratio to measure firm life cycle while Tian et al. (2015) used cash flows to measure life cycle of the firm. Further, we employ dynamic panel data model to estimate adjustment rate of leverage while Tian et al. (2015) used fixed effect model. Our study further differs in sample size and sample period Tian et al. (2015) reported a different adjustment rate for leverage across birth (68.52%) and decline (48.72%) stages of firm’s life cycle. Our approach of multivariate firm life cycle measurement is in accordance with Ahsan et al. (2016b). Ahsan et al. (2016b) found different adjustment rate for growing, mature, and declining firms while analyzing a large data set of Pakistani non-financial firms.

However the examination of leverage adjustment across firm life cycle lacks extensive research. For this purpose, this study follows Anthony and Ramesh (1992) and Ahsan et al. (2016b) and employ a multivariate approach in measurement of firm life cycle to an unbalanced data of 15,005 firm level observations (1996–2014) and categorize firms into three categories, i.e., growing firms, mature firms, and declining firms. Further, the study uses dynamic panel data model to estimate adjustment rate in these three life stages of firms and also examines various multilevel determinants of leverage (firm-level, industry-level, and country-level determinants).

The study contributes useful literature and insight on capital structure of an emerging economy. Further, the findings are of great importance while considering that prevalent financing alternatives in China are bank based loans and firms report lower long-term leverage ratio. This low-high-low pattern of leverage ratio across growth, maturity, and decline stage by Chinese firms indicates that leverage policy is in accordance with trade-off theory. The study found that adjustment rate for total leverage is 60-29-26% across growth, maturity, and decline stage. For short-term leverage adjustment rate is 90-78-88% across growth, maturity, and decline stages of firm life cycle. Long-term leverage reports an adjustment rate of 75-44-53% across growth, maturity, and decline stages. These findings suggest that in growth stage, there are more investment opportunities, and thus, leverage is vigorously altered by firms in growth stages.

China as a unique market setting

China as an emerging market and world’s second largest economy make it a potentially distinctive setting to study the relationship between stock liquidity and capital structure. China is a unique setting to conduct such type of study. Due to its less sophisticated capital markets, bank as the major financing alternative and high ownership concentration make the relationship between life cycle and capital structure more crucial from research point of view.

Moreover, before 2004, shares of state-owned and legal entities could be traded in stock exchange. This situation is further complicated by the fact that control rights remain with Chinese government. Shares held by state-owned shareholders exceed other shares held by other shareholders (individuals and NSOEs) in Chinese companies. Guo et al. (2013) reported that by end of September 2006, largest shareholders who held 56% of shares were state shares controlled by Chinese government and other state asset management companies.

Capital markets and financing alternatives in China

The considerable economic restructuring and reform undergone by the Chinese economy over the last 30 years have led to a marked increase in the number of shareholding companies. Chinese firms, state-owned enterprises (SOEs), and non-state-owned enterprises (NSOEs). SOEs and NSOEs differ in the nature of their ownership, agency relations, and bankruptcy risks.

The stock market in China has become an increasingly important part of China’s economy since the partial privatization of SOEs and the establishment of the Shanghai and Shenzhen Stock Exchanges in the early 1990s. The number of listed firms increased from 50 in 1992 to 1378 in 2004, with the total market value of publicly traded shares exceeding RMB (i.e., Renminbi, the Chinese currency) 3960 billion by the end of 2004. There were 353 NSOEs listed on the exchanges at the end of 2004, approximately 25% of the total number of listed firms. Although considerably smaller than SOEs, NSOEs had a total market value of RMB 479 billion by the end of 2004 or 12.1% of the total stock market value. Since 1979, China has launched a series of economic reforms to reorient its economy toward a market-based one. The most recent of these reforms is corporatization of previously owned SOEs. Corporatization involves initial public offering of a minority portion of state shares to individual investors who can trade their shares freely on the Shanghai and Shenzhen Stock Exchanges, while the majority ownership of these newly listed companies is still controlled by parent state enterprises. The government still remains the majority shareholder and retains two key control rights: the ultimate decision right concerning disposal of assets and mergers and acquisitions and the appointment of chief executive officers (CEOs) (Qian 1996).

Capital markets in China are young and less sophisticated in China as compared to other developed countries. Shanghai and Shenzan Stock exchanges were established in 1990, and it marks the beginning of securities market in China. China securities regulatory commission was introduced in 1992. Poncet et al. argue that capital market imperfections are prevalent in Chinese capital markets. Until 1998, the largest Chinese banks (most of them were state owned) were advised not to give credit to Chinese private companies. It was because of low political stature of these companies. Since 1998, these impediments in financing due to political pecking order should have been alleviated. However, research evidence suggest that financing constraints for private Chinese companies are still there due to social and political factors (Huang 2003). Numerous research indicates that financial constraints are impediments to investment, growth, and survival of the company (Stein 2003, Hubbard 1998). This implies that Chinese firms (especially private firms) have fewer alternatives of debt financing.

The remainder of the study is arranged as follows. The “Background” section presents a review of prior study and the theoretical framework. Data description and research methodology constitute the “Methods” section. The “Results and discussion” section provides detailed analysis and discussion of the findings. In the “Conclusions” section, we provide a conclusion and some policy implications.

Literature review and theoretical framework

Life cycle and target leverage

There exist numerous studies that confirm that firms passes through various life stages starting from birth to a possible death. However, these studies vary as far as the number of stages in life cycles is concerned. For example, Chandler (1962) and Anthony and Ramesh (1992) suggest three life cycle stages of growth, maturity, and decline and argue that firms strategic approach and alternatives greatly varies across these three life cycles. Miller and Friesen (1980) suggested four life cycles, i.e., birth, growth maturity, and revival. Dickinson (2011) identified birth, growth, maturity, decline, and revival as five stages in life cycle of a firm. In the presence of all these differences, most of the theories and studies about life cycle agreed upon growth, maturity, and decline as three stages of firm life cycle. However, researches also have difference of opinion whether firms follow target leverage across various firm’s life cycle stages. In light of this fact, this study provides a detailed literature about theories that explains firm’s leverage adjustment and targeting across various stages of a firm’s life cycle.

According to pecking order theory, in the context of asymmetric information and profitability, a firm would prefer internal financing, followed by less risky debt and equity in the last resort (Myers 1984, Myers and Majluf 1984). Thus, on the basis of pecking order theory of capital structure, a firm does not follow a target level of leverage and rather the firm follows a pattern in its leverage policy. Furthermore, a firm can improve its informativeness and profits thus changing its financing mix accordingly. During firm’s early stage, information asymmetry is higher and profitability is lower. As time changes and growth approaches, information asymmetry is considerably reduced. However, in growth stages, earnings are not retained or firms have no or less retained earnings. In such times, firms tend to raise more debt as a source of financing its investment. As time of maturity approaches, firms tend to retain earnings and these are reasons for debt financing decreases. Moreover, in maturity, firms have less investment needs and thus firms may also raise equity in maturity stages because of less information asymmetry. During decline phase profit decreases and also the retained earnings. This compels firms to go again for debt financing. Thus Pecking Order Theory (POT) suggest that at growth stage debt financing will be higher, at maturity it will be moderate and at decline stage it will be higher again (see Table 1). On the basis of these arguments we formulate the following hypothesis about firm leverage across three stages of firm life cycle.

H1: The firm follows high-low-high pattern of leverage across three stages of firm life cycle.

However, the competing trade-off capital theory (TOT) of capital structure postulates that in a perfect environment, a firm makes a trade-off between the associated benefits and cost with financing alternatives. Thus, on the basis of this trade-off analysis, a firm targets an optimal level of leverage. This is the static form of trade-off theory. Moreover, capital structure is affected by a number of exogenous and endogenous factors. These factors change over time especially across the stages of a firm life cycle. As a result, the firm tries to adjust its capital structure according to dynamic environment and this makes capital structure a dynamic decision (Fischer et al. 1989). According to TOT irrespective of a firm life stage, a firm should raise more debt to have a larger tax shield benefit; however, as debt increases, the firm bankruptcy and financial distress risk also increases. Thus, a firm always looks to achieve a breakeven point for its tax benefit and bankruptcy costs. Bankruptcy chances are higher in growth and decline stage of a firm life cycle; thus, during these stages a firm will avoid to raise more debt. Although more debt will result in higher tax benefits, the firm will avoid raising more debt during growth and decline stage. Thus, according to trade-off theory of capital structure, firm leverage is expected to follow a low-high-low pattern across three stages of firm’s life cycle (Modigliani and Miller 1958, Modigliani and Miller 1963), (see Table 1).

H2: The firm’s leverage follows a low-high-low pattern across three stages of firm’s life cycle.

Another important theory is the agency cost theory. According to agency cost theory (ACT), managers and shareholders are at conflict with respect to the use of free cash flows and resources of the firms. These conflicts get more severe if there is a high amount of free cash flows in an organization. Jensen and Meckling (1976) suggest that these conflicts can be solved through the use of optimal level of debt, since debt repayment will eat up free cash flows and thus managers can be prevented from investing in value decreasing projects. When a firm is growing, there are more investment opportunities and less free cash flows at growth stage compel firm to raise more debt. At maturity stage, when investment opportunities shrink, the firm is expected to raise lesser debt. While at decline stage, debt acts as a controlling mechanism when a firm is at the helm of shrinking. Thus, according to ACT, firm leverage is expected to follow a high-low-high pattern across the three stages of firm life cycle but still agency theory gives no clear explanation of a targeted optimal capital structure (Jensen 1986).

H3: The firm follows a high-low-high pattern of leverage across the three stages of firm’s life cycle.

Diamond (1989) suggests that firm reputation varies across firm’s life, and thus, it can greatly explain firm’s financing preferences across different stages of firm’s life cycle. Growing firms have less history or past record and have low reputation. They are characterized by less debt capacity, and thus, this information asymmetry results in lower debt for firms in growing stages. At maturity and decline, a firm has a track record and history. At these stages, there is lesser information asymmetry and thus these firms have reputation. Based on these reasons, firms at maturity and decline stage of their lives raise more debt. As a result, Diamond suggests a low-high-high pattern of leverage across three life stages of firm’s life cycle (see Table 1).

H4: The firm follows a low-high-high pattern of leverage across the three stages of firm’s life cycle.

There is also a market timing theory of capital structure. The firms analyze market condition and change their capital structure policy according to market conditions. Baker and Wurgler (2002) suggest that capital structure can be explained through the development of bond and stock market. So according to this theory, it is not possible to predict a leverage pattern across firm’s life cycle.

Variables’ description

Table 2 represents description of all the explanatory variables and their relationship with leverage and leverage adjustment rate. For dependent variable of leverage, this study uses financial leverage following the empirical studies of Delcoure (2007), Sheikh and Qureshi (2014), Ahsan et al. (2016b), and Tian et al. (2015). The study uses three proxies of leverage: short-term leverage (SL) which is the ratio of short-term loan to total assets, long-term leverage (LT) is the ratio of long term loan to assets, and total leverage (TL) is the ratio of total liabilities to total assets.
Table 1

Leverage pattern across firm’s life cycle as suggested by capital structure theories

Capital structure theories

Leverage pattern

Growth

Maturity

Decline

Pecking order theory

High

Low

High

Trade-off theory

Low

High

Low

Agency cost theory

Low

High

High

Diamond’s theory

Low

High

High

Market timing theory

?

?

?

Table 2

Control variables, their proxies and proposed relationship with leverage

 

Variable

Notation

Measurement

Relationship with leverage

Relationship with adjustment rate

Firm level

Tax shield

TS

Ratio of tax paid and gross profit

+

+

Bankruptcy risk

ZS

Altman’s Z score

+/−

+

Business risk

BR

Annual change in net profit

+/−

+/−

Non-debt tax shield

NDTS

Ratio of depreciation to total assets

?

Agency cost

AGC

Ratio of operating expense over sales

+

?

Growth

GR

Annual change in total assets

+/−

+

Current profitability

CP

Net profit scaled by total assets

+/−

+

Past profitability

PP

Retained earnings ratio

+/−

+

Liquidity

LIQ

Ratio of current assets to current liabilities

+/−

+

Tangibility

TANG

Ratio of net fixed assets to total assets

+

?

Collateral value

CV

Ratio of gross fixed assets at cost to total assets

+

?

Firm size

SIZE

Natural logarithm of firm’s assets

+/−

+

Industry level

Industry leverage

IL

Mean of industry leverage

+

+

Industry profitability

IP

Mean of industry profit

?

?

Country level

Inflation rate

IR

Annual inflation rate based on consumer prices

+

+

Exchange rate

ER

Yearly exchange rate of rmb to us dollar

?

?

Economic growth

EG

Annual per capita gdp rate

+/−

+

Capital formation

CF

Ratio of gross capital formation to gdp

?

?

Table 3

Criteria for life cycle distribution

Stage

DP

SG

AGE

Growth

Low

High

Young

Maturity

Medium

Medium

Adult

Decline

High

Low

Old

DP dividend payout ratio, SG sales growth, AGE natural log of firm’s age

Methods

In order to investigate capital structure and explanatory variables, this study uses an extensive set of data. The study uses the data of 867 A-listed firms listed on Chinese Stock exchanges. The data is annual and acquired from RESET Chinese database. Industry-level data is calculated from firm-level data. All the macroeconomic level data is collected from World Bank database. Data is collected over a period of 1996–2014. Data is panel consisting of 15,005 observations. Data is unbalanced with respect to time (year) and space (firms) dimensions.

We classify firms into three life stages, i.e., growing, mature, and declining firms. This study follows a multivariate methodology to divide firms into three categories (Anthony and Ramesh 1992). Univariate methodology is not used because it gives few measurement errors and is not driven by firm size effects and risk preferences. Numerous studies can be found which employed the multivariate methodology to classify firms into growth, mature, and decline categories (Teixeira and Santos 2006, Jenkins et al. 2004, Ahsan et al. 2016b). This study uses dividend payout ratio, firm’s age, and firm’s annual percentage change of sales for the classifications of firms into growing, mature, and declining firms. We calculate the median values of annual change in sales and dividend payout ratio for 5 years prior period. Then, we use the median values of sales, dividend payout ratio, and age to classify the firms. We classify the firms according to a criteria based on three life cycle stages (Table 3).

This criterion is based on the fact that during growth stage, dividend payout ratio is low, firms have lower sales, and firms are relatively young. As firm grows toward maturity, dividend payout ratio increases so as the sales. At decline stage, dividend payout ratio is the highest, sales growth is the lowest, and firms are of old age. We assign a value of 1 to growing firms, 2 to mature firms, and 3 to declining firms We sum up the median values of sales growth, median values of dividend payout, and log values of age for each firm in a single year. The minimum value of this summation is 3, and the maximum value is 9. Based on these thresholds, we divide firms into three categories.
  • Growth firms: a firm having a composite yearly score of less than 4 is classified as a firm in growth stage

  • Mature firms: a firm having a composite yearly score between 4 and 7 is classified as mature firm

  • Declining firms: firms having a composite yearly score of more than or equal to 8

As a calculation of median for sales growth and dividend payout ratio, we use the prior 5-year data; as a result, our yearly firm level observations reduces to 10,274 from 15,005. Yearly firm level observations for growing firms are 1539, for mature firms the observations are 7726, and for declining firms the number is 1009 firm year observations.

Table 4 reveals that dividend payout ratio is the lowest during growth stage and then starts to increase as firm matures and then reaches to highest ratio in the declining stage. Similarly, sales growth is highest for growing stage and then become lower in maturity and lowest during the declining stage. Youngest firms have lowest mean of age value, while firms in declining stage have the highest mean for age.
Table 4

Descriptive statistics of firms classification variables

Variables

Observation

Mean

Standard deviation

Growth stage

 Dividend payout ratio (median)

1539

0.058

0.094

 Sales growth (median)

1539

0.300

0.159

 Age

1539

2.670

0.199

Mature stage

 Dividend payout ratio (median)

7726

0.202

0.244

 Sales growth (median)

7726

0.138

0.191

 Age

7726

2.873

0.201

Decline stage

 Dividend payout ratio (median)

1009

0.456

0.202

 Sales growth (median)

1009

0.057

0.093

 Age

1009

3.020

0.105

Sales growth is the median of annual sales growth for prior 5 years. Dividend payout ratio is the median of dividend payout ratio for prior 5 year. Age is the natural logarithm of firm’s age

Statistical model and estimation strategy

According to Flannery and Rangan (2006), firms maintain a target leverage if there exist no market friction. In this context, we develop the following static model for leverage and explanatory variables.
$$ \begin{array}{l}\mathrm{LEV}\kern0.5em =\kern0.5em {\beta}_0++{\beta}_1{\mathrm{TS}}_{it}+{\beta}_2{\mathrm{ZS}}_{it}+{\beta}_3{\mathrm{BR}}_{it}+{\beta}_4{\mathrm{NDTS}}_{it}+{\beta}_5{\mathrm{AgC}}_{it}\\ {}+{\beta}_6{\mathrm{GROW}}_{it}+{\beta}_7{\mathrm{CP}}_{it}+{\beta}_8{\mathrm{PP}}_{it}+{\beta}_9{\mathrm{LIQ}}_{it}+{\beta}_{10}{\mathrm{TANG}}_{it}+{\beta}_{11}{\mathrm{CV}}_{it}\\ {}{\beta}_{12}{\mathrm{SIZE}}_{i_t}+{\beta}_{13}{\mathrm{INDLEV}}_{j_t}+{\beta}_{14}{\mathrm{INDP}}_{j_t}+{\beta}_{15}{\mathrm{INF}}_t+{\beta}_{16}{\mathrm{ER}}_t+{\beta}_{17}{\mathrm{EG}}_t\\ {}{\beta}_{18}{\mathrm{CF}}_t+{e}_{it}\end{array} $$
(1)

In Eq. 1, LEV it is the firm i’s leverage at time t. TS it is the tax shield of firm i at time t. ZS it is Altman’s Z score of firm i at time t. BR it represents business risk of a firm i at time t. NDTS it is non-debt tax shield of a firm i at time t. AgC it represents agency costs of a firm i at time t. GROW it is the annual growth rate of a firm i at time t. CP it represents current profit of a firm i at time t while PP it is past profits of a firm i at time t. LIQ it represents liquidity of a firm i at time t. TANG it represents the tangibility ratio of a firm i at time t while CV it is collateral value of a firm i at time t. SIZE it is the firm i’s size at time t. INDLEV jt represents the industry leverage of an industry j at time t. INDP jt is the industry mean profit of an industry j at time t. INF t represents inflation rate at time t. ER t is exchange rate at time t. EG t represents economic growth at time t. CF t is gross capital formation.

However, firms inhabit a market that is under the effects of frictions; thus, due to these market imperfections, it is difficult for a firm to immediately adjust to its target capital. This phenomenon becomes more relevant if we consider the adjustment costs associated with adjustment to a target leverage. Thus, another equation based on partial adjustment of leverage emerges.
$$ {\mathrm{LEV}}_{it}-{\mathrm{LEV}}_{it-1}=\gamma \left({{\mathrm{LEV}}^{*}}_{it}-{\mathrm{LEV}}_{it-1}\right)+{\delta}_{it} $$
(2)
LEV it is firm i’s leverage at time t and δ it is the error term. By substituting Eq. 2 into Eq. 1, we get the following equation.
$$ \begin{array}{l}{\mathrm{LEV}}_{it}\kern0.5em =\kern0.5em {\beta}_0\gamma +\left(1-\gamma \right){\mathrm{LEV}}_{t-1}+\gamma {\beta}_1{\mathrm{TS}}_{it}+\gamma {\beta}_2{\mathrm{ZS}}_{it}+\gamma {\beta}_3{\mathrm{BR}}_{it}+\gamma {\beta}_4{\mathrm{NDTS}}_{it}+\gamma {\beta}_5{\mathrm{AgC}}_{it}\\ {}+\gamma {\beta}_6{\mathrm{GROW}}_{it}+\gamma {\beta}_7{\mathrm{CP}}_{it}+\gamma {\beta}_8{\mathrm{PP}}_{it}+\gamma {\beta}_9{\mathrm{LIQ}}_{it}+\gamma {\beta}_{10}{\mathrm{TANG}}_{it}+\gamma {\beta}_{11}{\mathrm{CV}}_{it}\\ {}\gamma {\beta}_{12}{\mathrm{SIZE}}_{i_t}+\gamma {\beta}_{13}{\mathrm{INDLEV}}_{j_t}+\gamma {\beta}_{14}{\mathrm{INDP}}_{j_t}+\gamma {\beta}_{15}{\mathrm{INF}}_t+\gamma {\beta}_{16}{\mathrm{ER}}_t+\gamma {\beta}_{17}{\mathrm{EG}}_t\\ {}\gamma {\beta}_{18}{\mathrm{CF}}_t+{\eta}_i+{\lambda}_t+{\upsilon}_{it}\end{array} $$
(3)
η i in Eq. 3 corresponds to firm-specific effects while λ t is the time-specific effects. Simplifying Eq. 3, the following equation is obtained.
$$ \begin{array}{l}{\mathrm{LEV}}_{it}\kern0.5em =\kern0.5em {\beta}_0\gamma +\rho {\mathrm{LEV}}_{t-1}+{\delta}_1{\mathrm{TS}}_{it}+{\delta}_2{\mathrm{ZS}}_{it}+{\delta}_3{\mathrm{BR}}_{it}+{\delta}_4{\mathrm{NDTS}}_{it}+{\delta}_5{\mathrm{AgC}}_{it}\\ {}+{\delta}_6{\mathrm{GROW}}_{it}+{\delta}_7{\mathrm{CP}}_{it}+{\delta}_8{\mathrm{PP}}_{it}+{\delta}_9{\mathrm{LIQ}}_{it}+{\delta}_{10}{\mathrm{TANG}}_{it}+{\delta}_{11}{\mathrm{CV}}_{it}\\ {}+{\delta}_{12}{\mathrm{SIZE}}_{i_t}+{\delta}_{13}{\mathrm{INDLEV}}_{J_t}+{\delta}_{14}{\mathrm{INDP}}_{j_t}+{\delta}_{15}{\mathrm{INF}}_t+{\delta}_{16}{\mathrm{ER}}_t+{\delta}_{17}{\mathrm{EG}}_t\\ {}+{\delta}_{18}{\mathrm{CF}}_t+{\eta}_i+{\lambda}_t+{\upsilon}_{it}\end{array} $$
(4)

In Eq. 4, α = γβ 0; ρ = (1 − γ); δ k  = γβ k ; and λ t ν it  = γe it

The study employs two-step generalized method of moments to estimate the dynamic Eq. 4 to address the issues of endogeneity. The study uses GMM’s method of Arellano-Bover (1995)/Blundell-Bond (2000) to estimate Eq. 4. We estimate Eq. 4 across firm life cycle.

Results and discussion

Descriptive statistics

Table 5 represents the descriptive statistics. From mean values of short term (SL) and long (LL) term leverage, it can be inferred that Chinese firms use more short term leverage than long term leverage. Mean value is highest for total leverage (TL). Table 5 shows as firms go toward maturity and decline stage its leverage increases except for long term leverage (LL). Mean value for total leverage (TL) is 0.58 during growth, 0.63 during maturity, and 0.53 during decline stage. This low-high-low pattern of total leverage (TL) is in line with the trade-off theory of capital structure. For tax shield (TS), the mean values increase from growth to maturity and remain the same for decline stage. Mean for bankruptcy probability (Z score (ZS)) is the highest during growth stage (6.93). Mean for agency costs (AgC = 0.06) is highest during growth stage so as the mean of firm’s growth rate (GROW = 0.81). This is in line with agency theory. Agency conflicts are higher during growth stage of a firm. Further, mean of current profit (CP = 0.06) is highest during growth stage. It decreases as firm approaches decline stage (0.05). Another interesting descriptive statistic is the mean of past profit (PP). Past profits are measured through retained earnings. The mean of PP in growth stage is 0.52, 0.47 in mature stage, and 0.48 during decline stage. This shows that firm has high retained earnings during growth stage, and at maturity and decline stage, firm do not retained many earnings.
Table 5

Descriptive statistics

 

SL

LL

TL

TS

ZS

BR

NDTS

AgC

GROW

CP

PP

LIQ

TANG

CV

SIZE

AGE

INDSL

INDLL

INDTL

INMP

INF

ER

EG

CF

Growth stage (no. of observations = 1539)

 Mean

0.17

0.10

0.58

0.07

3.06

−0.06

0.03

0.05

0.18

0.04

0.52

1.34

0.26

0.53

22.05

2.67

0.17

0.10

0.59

0.05

2.83

7.07

9.93

45.00

 Std. Dev.

0.15

0.11

0.26

3.51

20.25

28.64

0.02

0.07

0.47

0.16

2.82

1.32

0.19

0.21

1.33

0.20

0.07

0.05

0.20

0.96

2.04

0.77

1.97

2.91

 Min

0.00

0.00

0.05

−134.83

−104

−177

0.00

0.00

−0.88

−2.98

−75.21

0.03

0.00

0.00

18.34

1.61

0.00

0.00

0.16

−0.92

−0.77

6.09

7.35

37.66

 Max

2.50

0.80

5.42

15.79

551

1060

0.21

0.53

10.70

1.05

0.98

28.18

0.94

1.00

27.81

2.95

1.06

0.31

3.31

37.77

5.86

8.28

14.19

47.83

Maturity stage (no. of observations = 7226)

 Mean

0.17

0.10

0.63

0.15

6.93

−0.40

0.03

0.06

0.81

0.06

0.47

1.39

0.24

0.55

21.89

2.87

0.17

0.10

0.60

0.04

2.68

7.17

9.90

44.65

 Std. Dev.

0.22

0.11

1.04

1.46

140.73

27.26

0.02

0.07

44.09

0.81

5.79

2.76

0.19

0.22

1.34

0.20

0.06

0.06

0.31

0.61

1.96

0.83

1.92

3.10

 Min

0.00

0.00

−0.19

−38.48

−515

−768

−0.03

0.00

−1.00

−20.6

−368.4

−5.13

0.00

0.00

14.08

1.61

0.00

0.00

0.16

−1.05

−0.7

6.09

7.35

37.66

 Max

6.67

1.82

55.41

102

8070

1450

0.18

0.92

3850

44.22

0.99

205

0.92

1.00

28.04

3.22

0.75

0.49

6.07

37.77

5.86

8.28

14.19

47.83

Decline stage (no. of observations = 1009)

 Mean

0.15

0.10

0.53

0.15

3.47

2.16

0.03

0.05

0.13

0.05

0.48

1.72

0.24

0.57

22.18

3.02

0.17

0.10

0.68

0.02

2.43

7.27

9.90

44.25

 Std. Dev.

0.28

0.12

1.22

0.32

18.24

71.57

0.02

0.06

0.26

0.15

0.58

3.35

0.19

0.21

1.06

0.10

0.23

0.06

1.28

0.13

1.95

0.86

1.88

3.30

 Min

0.00

0.00

0.01

−5.18

−14

−63.3

0.00

0.00

−0.77

−2.76

−10.94

0.06

0.00

0.02

17.58

2.80

0.02

0.00

0.25

−2.81

−0.7

6.09

7.35

37.66

 Max

6.98

0.62

36.38

3.68

567

2250

0.14

0.44

2.46

0.90

0.97

89

0.86

0.98

25.26

3.22

6.98

0.31

36.38

1.23

5.86

8.28

14.19

47.83

SL is short-term leverage ratio between short-term loan and total assets. LL is long-term leverage ratio between long-term leverage and assets. TL represents total leverage which is a ratio of total liabilities and total assets. TS represents tax shield calculated by dividing tax payments on net profit. ZS is Z score. BR is business risk measured through operating expense divided by total sales. NDTS is non-debt tax shield calculated through the ratio of depreciation to total assets. Ag C represents agency costs. CP is current profit growth, calculated through annual growth rate of net profit. PP is retained earnings ratio as a proxy for past profit. LIQ is firm liquidity measured through the ratio of current assets and current liabilities. TANG is tangibility measured through the ratio of fixed assets to total assets. CV is collateral value, and it is the ratio of gross fixed assets at cost to total book value of assets. SIZE is the natural logarithm of firm’s total assets. AGE is natural log of firm’s age from the date of its listing. INDSL is industry short-term leverage measured from firm level short term leverage. INDLL is industry long-term leverage measured from firm level long-term leverage ratio. INDTL is industry total leverage measured from firm level total leverage. INMP is industry annual mean profit calculated from firm level net profit. INF is inflation rate. ER is exchange rate between RMB and US dollar. EG is economic growth measured through annual gross domestic product rate. CF is capital formation ratio to total GDP

Correlation matrix

Tables 6, 7, and 8 represent correlation matrices of short-term, long-term, and total leverage, respectively. VIF corresponding to each explanatory variable is given at the end of each table. VIF is the variance inflation factor. VIF values are obtained after running OLS regression for all three proxies of leverage. All the VIF values are less than 10. The maximum values of VIF for short-term leverage correspond to capital formation (7.64); for long-term leverage, capital formation (CF) again reports the highest VIF of 7.55. For total leverage, exchange rate (ER) has the highest VIF value of 7.65. By looking at the correlation values and VIF values in Tables 6, 7, and 8, it can be inferred that there is no serious issue of multicolinearity.
Table 6

Correlation matrix (short-term leverage)

 

SL

SL(L1)

TS

BR

NDTS

AgC

GROW

CP

PP

LIQ

tang

CV

SIZE

INDSL

INMP

INF

ER

EG

CF

VIF

SL

1

                  

1.73

SL(L1)

0.68

1

                 

1.24

TS

0.02

0.02

1

                

1

BR

−0.01

0.01

0.00

1

               

1.01

NDTS

0.04

0.04

0.02

0.00

1

              

1.73

AgC

−0.03

−0.03

−0.02

−0.02

−0.08

1

             

1.1

GROW

−0.04

0.00

0.00

0.08

−0.10

−0.04

1

            

1.05

CP

−0.25

−0.19

−0.02

0.00

0.04

0.07

0.08

1

           

1.2

PP

0.00

0.01

−0.01

0.00

−0.01

0.01

0.01

0.03

1

          

1

LIQ

−0.31

−0.25

−0.01

−0.02

−0.28

0.18

0.03

0.14

0.01

1

         

1.53

tang

0.10

0.07

0.04

0.00

0.60

−0.12

−0.05

−0.17

0.00

−0.33

1

        

1.93

CV

−0.04

−0.02

0.00

0.01

0.45

−0.10

−0.05

0.11

−0.01

−0.43

0.46

1

       

1.62

SIZE

−0.19

−0.20

−0.02

0.01

0.03

−0.21

0.10

0.04

0.00

−0.20

−0.04

0.05

1

      

1.38

INDSL

0.31

0.25

0.03

0.00

0.04

−0.02

0.00

−0.06

0.00

−0.07

0.11

−0.01

−0.23

1

     

1.29

INMP

−0.02

−0.02

0.00

0.00

−0.02

−0.01

0.00

0.01

0.00

0.01

−0.01

−0.02

0.00

−0.05

1

    

1

INF

0.03

−0.01

−0.02

0.02

−0.02

0.00

0.01

0.07

0.01

0.01

−0.03

0.00

0.01

0.02

0.00

1

   

1.45

ER

0.14

0.11

0.04

−0.02

0.13

−0.05

0.04

0.00

0.01

−0.05

0.20

0.06

−0.34

0.40

−0.03

−0.09

1

  

7.51

EG

0.11

0.09

0.04

0.00

0.09

−0.05

0.08

0.08

0.02

−0.07

0.15

0.04

−0.22

0.32

−0.02

0.18

0.69

1

 

2.35

CF

−0.14

−0.10

−0.04

0.02

−0.11

0.04

−0.04

−0.02

−0.01

0.05

−0.18

−0.05

0.32

−0.40

0.03

−0.15

−0.90

−0.74

1

7.64

SL is short-term leverage ratio between short-term loan and total assets. LL is long-term leverage ratio between long-term leverage and assets. TL represents total leverage which is a ratio of total liabilities and total assets. TS represents tax shield calculated by dividing tax payments on net profit. ZS is Z score. BR is business risk measured through operating expense divided by total sales. NDTS is non-debt tax shield calculated through the ratio of depreciation to total assets. Ag C represents agency costs. CP is current profit growth, calculated through annual growth rate of net profit. PP is retained earnings ratio as a proxy for past profit. LIQ is firm liquidity measured through the ratio of current assets and current liabilities. TANG is tangibility measured through the ratio of fixed assets to total assets. CV is collateral value, and it is the ratio of gross fixed assets at cost to total book value of assets. SIZE is the natural logarithm of firm’s total assets. AGE is natural log of firm’s age from the date of its listing. INDSL is industry short term leverage measured from firm level short-term leverage. INDLL is industry long term leverage measured from firm level long-term leverage ratio. INDTL is industry total leverage measured from firm level total leverage. INMP is industry annual mean profit calculated from firm level net profit. INF is inflation rate. ER is exchange rate between RMB and US dollar. EG is economic growth measured through annual gross domestic product rate. CF is capital formation ratio to total GDP

Table 7

Correlation matrix (long-term leverage)

 

LL

LL(L1)

TS

BR

NDTS

AgC

GROW

CP

PP

LIQ

TANG

CV

SIZE

INDLL

INMP

INF

ER

EG

CF

VIF

LL

1

                  

1.64

LL(L1)

0.84

1

                 

1.24

TS

−0.01

−0.01

1

                

1

BR

0.00

0.01

0.00

1

               

1.01

NDTS

0.04

0.08

0.02

0.02

1

              

1.76

AgC

−0.25

−0.24

0.04

−0.01

−0.08

1

             

1.12

GROW

0.08

0.00

0.00

0.01

−0.07

−0.03

1

            

1.04

CP

−0.12

−0.09

−0.01

0.03

0.06

0.06

0.13

1

           

1.2

PP

−0.01

−0.01

0.00

0.00

−0.01

0.01

0.00

0.03

1

          

1

LIQ

−0.13

−0.17

−0.01

−0.02

−0.26

0.13

−0.02

0.13

0.01

1

         

1.26

TANG

0.19

0.21

0.00

0.04

0.59

−0.14

−0.04

−0.17

0.00

−0.29

1

        

1.93

CV

0.31

0.30

0.01

0.02

0.46

−0.14

−0.02

0.10

−0.01

−0.35

0.46

1

       

1.62

SIZE

0.20

0.21

−0.01

−0.01

0.04

−0.19

0.07

0.07

−0.01

−0.15

−0.06

0.04

1

      

1.32

INDLL

−0.07

−0.06

0.00

0.01

0.05

−0.01

0.02

−0.05

0.00

−0.07

0.10

−0.02

−0.24

1

     

1.24

INMP

−0.02

−0.01

0.00

0.00

−0.02

−0.01

0.00

0.01

0.00

0.01

−0.01

−0.02

0.00

−0.06

1

    

1

INF

−0.03

0.01

−0.02

0.00

−0.01

0.00

0.04

0.06

0.00

−0.01

−0.03

−0.01

0.01

0.04

0.00

1

   

1.43

ER

−0.01

−0.04

0.03

0.01

0.17

−0.05

0.02

0.02

0.01

−0.04

0.23

0.07

−0.35

0.40

−0.03

−0.08

1

  

7.5

EG

0.00

0.00

0.03

0.03

0.12

−0.05

0.06

0.08

0.02

−0.06

0.18

0.04

−0.23

0.33

−0.02

0.18

0.70

1

 

2.39

CF

0.03

0.04

−0.02

0.00

−0.15

0.05

−0.03

−0.03

0.00

0.05

−0.21

−0.07

0.32

−0.40

0.03

−0.16

−0.90

−0.74

1

7.55

Table 8

Correlation matrix total leverage

 

TL

TL(L1)

TS

BR

NDTS

AgC

GROW

CP

PP

LIQ

TANG

CV

SIZE

INDTL

INMP

INF

ER

EG

CF

VIF

TL

1

                  

1.57

TL(L1)

0.90

1

                 

1.4

TS

0.01

0.00

1

                

1

BR

0.02

0.03

0.00

1

               

1

NDTS

−0.13

−0.10

0.01

0.00

1

              

1.7

AgC

−0.22

−0.22

0.02

−0.01

−0.10

1

             

1.12

GROW

0.05

0.02

0.00

0.03

−0.02

−0.03

1

            

1.02

CP

−0.25

−0.18

−0.01

0.00

0.02

0.08

0.09

1

           

1.16

PP

−0.01

0.00

0.00

0.00

−0.01

0.01

0.00

0.03

1

          

1

LIQ

−0.25

−0.22

−0.01

−0.01

−0.04

0.03

−0.01

0.04

0.00

1

         

1.08

TANG

−0.05

−0.05

0.01

0.00

0.60

−0.14

−0.01

−0.17

0.00

−0.08

1

        

1.87

CV

−0.11

−0.12

0.01

0.01

0.43

−0.12

−0.01

0.09

−0.01

−0.11

0.45

1

       

1.4

SIZW

0.42

0.40

0.00

0.01

0.05

−0.18

0.05

0.05

−0.01

−0.11

−0.03

0.06

1

      

1.39

INDTL

0.01

0.00

0.01

0.01

0.04

−0.02

−0.02

−0.06

0.00

−0.03

0.10

−0.01

−0.21

1

     

1.24

INMP

−0.01

−0.01

0.00

0.00

−0.01

−0.01

0.00

0.01

0.00

0.00

−0.01

−0.02

0.01

−0.05

1

    

1

INF

0.01

0.02

−0.02

0.01

−0.01

−0.01

0.02

0.06

0.00

0.02

−0.03

0.00

0.01

0.04

0.00

1

   

1.46

ER

−0.09

−0.14

0.03

−0.01

0.15

−0.07

0.00

−0.02

0.00

−0.02

0.23

0.06

−0.30

0.40

−0.03

−0.08

1

  

7.65

EG

−0.04

−0.06

0.03

0.00

0.11

−0.06

0.03

0.05

0.01

−0.02

0.17

0.04

−0.19

0.33

−0.02

0.18

0.70

1

 

2.37

CF

0.08

0.12

−0.02

0.01

−0.14

0.07

0.01

0.01

0.00

0.02

−0.21

−0.06

0.28

−0.41

0.02

−0.16

−0.90

−0.74

1

7.61

Adjustment rate of leverage

Tables 9, 10, and 11 show regression results for short-term, long-term, and total leverage, respectively. These results were obtained using dynamic panel data model. Table 9 shows that for short-term leverage (SL), lagged short-term leverage (SL (L1)) shows a positive and statistically significant coefficients (0.091, 0.220, 0.340) for growth, maturity, and decline stage. This shows that Chinese forms follow a target level of short term leverage across all the three life stages of a firm life cycle. However, adjustment rate (1-coefficent) is highest for growing firms and lowest for firms in the decline stage. This shows that during that stage, firms tend to speedily adjust their leverage.
Table 9

GMM estimation results for short-term leverage

 

Growth

Maturity

Decline

 

Coef.

z

P > z

Coef.

z

P > z

Coef.

z

P > z

Adjustment rate

0.905

  

0.780

  

0.64

  

SL(L1)

0.095

3.83

0.0000

0.220

6.88

0.000

0.340

4.24

0.000

TS

−0.004

−0.49

0.6210

−0.004

−1.12

0.262

0.018

1.95

0.052

BR

0.000

0.06

0.9550

0.000

2.66

0.008

0.000

−15

0.000

ZS

0.001

0.91

0.3610

0.000

−12.32

0.000

0.000

−0.37

0.714

NDTS

−0.763

−1.56

0.1180

−0.038

−0.17

0.867

−0.490

−2.76

0.006

AgC

−0.092

−0.6

0.5490

0.044

0.61

0.544

−0.577

−4.8

0.000

GROW

−0.031

−3.84

0.0000

−0.003

−0.65

0.515

−0.009

−1.84

0.066

CP

−0.187

−4.5

0.0000

−0.109

−4.11

0.000

−0.074

−2.29

0.022

PP

−0.011

−2.94

0.0030

−0.009

−2.18

0.029

−0.013

−4.49

0.000

LIQ

−0.083

−10.56

0.0000

−0.046

−8.98

0.000

−0.051

−7.47

0.000

TANG

−0.006

−0.34

0.7330

−0.025

−1.7

0.089

0.024

1.98

0.047

CV

−0.051

−2.63

0.0090

−0.034

−3.04

0.002

−0.018

−1.27

0.205

SIZE

0.003

0.31

0.7590

0.016

2.15

0.031

−0.005

−0.61

0.540

INDSL

0.506

5.77

0.0000

0.281

5.21

0.000

0.344

7.9

0.000

INMP

0.003

5.58

0.0000

0.002

4.36

0.000

0.113

7.01

0.000

INF

0.001

0.69

0.4880

0.002

2.92

0.004

0.000

0.57

0.569

ER

−0.019

−1.52

0.1280

0.010

1.5

0.134

−0.022

−3.25

0.001

EG

0.006

3.57

0.0000

−0.001

−0.94

0.345

−0.005

−4.43

0.000

CF

0.001

0.48

0.6280

−0.001

−0.87

0.385

−0.008

−6.46

0.000

_cons

0.199

0.68

0.4950

−0.231

−1.15

0.252

0.834

4.03

0.000

Table 10

GMM estimation results for long term leverage

 

Growth

Maturity

Decline

Coef.

z

P > z

Coef.

z

P > z

Coef.

z

P > z

Adjustment rate

0.750

  

0.435

  

0.541

  

LL(L1)

0.250

13.2

0.000

0.565

18.35

0.000

0.459

34.73

0.000

TS

−0.054

−9.4

0.000

0.000

−0.08

0.938

−0.008

−1.61

0.106

BR

−0.001

−1.57

0.117

0.000

−1.59

0.112

−0.001

−0.9

0.368

ZS

0.001

3.37

0.001

0.000

−0.27

0.789

−0.001

−4.72

0.000

NDTS

0.664

2.84

0.005

−1.103

−4.5

0.000

−0.845

−5.49

0.000

AgC

−0.037

−0.77

0.440

−0.020

−0.24

0.810

−0.043

−0.59

0.552

GROW

−0.007

−1.67

0.095

0.020

3.99

0.000

0.045

8.95

0.000

CP

−0.025

−1.2

0.231

−0.094

−4.97

0.000

−0.089

−4.37

0.000

PP

−0.003

−1.12

0.265

0.000

−3.92

0.000

−0.001

−0.31

0.755

LIQ

0.014

2.44

0.015

0.029

4.63

0.000

0.018

8.52

0.000

TANG

−0.023

−2.25

0.024

0.013

1.21

0.225

−0.018

−2.67

0.008

CV

0.049

3.67

0.000

0.055

5.79

0.000

0.046

6.23

0.000

SIZE

0.028

5.46

0.000

−0.012

−1.61

0.107

0.012

2.42

0.016

INDLL

0.353

5.19

0.000

0.478

6.77

0.000

0.467

16.03

0.000

INMP

−0.001

−1.96

0.050

0.000

1.23

0.217

0.009

0.7

0.482

INF

−0.001

−0.75

0.456

−0.001

−1.25

0.210

−0.001

−3.04

0.002

ER

0.042

5.02

0.000

−0.002

−0.32

0.751

0.015

3.43

0.001

EG

0.004

2.75

0.006

−0.002

−1.91

0.057

−0.003

−5.6

0.000

CF

0.006

3.8

0.000

−0.001

−0.68

0.497

−0.004

−4.06

0.000

_cons

−1.254

−7.27

0.000

0.290

1.39

0.164

−0.166

−1.24

0.215

Table 11

GMM estimation results for total leverage

 

Growth

Maturity

Decline

Coef.

z

P > z

Coef.

z

P > z

Coef.

Z

P > z

Adjustment rate

0.594

  

0.286

  

0.268

  

TL(L1)

0.406

8.99

0.000

0.714

19.08

0.000

0.732

22.95

0.000

TS

−0.032

−4.18

0.000

0.001

5.03

0.000

−0.002

−0.45

0.651

BS

−0.001

−0.59

0.555

0.000

−1.89

0.059

0.000

−9.27

0.000

ZS

0.000

1.34

0.180

0.000

−0.4

0.692

0.001

6.32

0.000

NDTS

−1.157

−3.71

0.000

−2.074

−7.17

0.000

−0.160

−0.53

0.598

AgC

−0.125

−1.49

0.135

0.104

1.08

0.278

−0.315

−3.62

0.000

GROW

0.005

3

0.003

0.004

1.34

0.182

0.088

7.25

0.000

CP

−0.145

−6.53

0.000

−0.183

−7.9

0.000

−0.247

−7.68

0.000

PP

−0.007

−1.51

0.132

−0.001

−5.19

0.000

−0.002

−0.51

0.607

LIQ

−0.102

−18.19

0.000

−0.002

−3

0.003

−0.007

−12.49

0.000

TANG

−0.003

−0.2

0.838

−0.016

−1.19

0.233

−0.046

−3.5

0.000

CV

−0.033

−2.71

0.007

0.022

1.94

0.052

0.032

3.12

0.002

SIZE

0.031

4.01

0.000

0.062

7.6

0.000

0.028

3.57

0.000

INDLL

0.062

6.99

0.000

0.018

2.01

0.044

0.000

0.05

0.959

INMP

0.001

1.4

0.160

0.002

7.56

0.000

−0.010

−0.56

0.578

INF

0.001

1.83

0.068

0.002

3.68

0.000

0.002

2.28

0.023

ER

0.036

3.75

0.000

0.054

8.57

0.000

0.024

3.22

0.001

EG

−0.001

−0.78

0.436

−0.002

−2.18

0.030

0.000

0.1

0.919

CF

0.003

1.77

0.078

0.004

3.73

0.000

0.002

1.58

0.115

_cons

−0.577

−2.42

0.016

−1.750

−8.61

0.000

−0.737

−3.41

0.001

For long-term leverage (LL), Table 10 shows that coefficients for lagged leverage (LLL1) are not only positive but also statistically significant. This shows that firms in China follow a target level of long-term leverage. Adjustment rate (1-coefficient) is highest for firm in growing stage (0.752). The only difference is that long-term leverage (LL) unlike short term leverage (SL) reposts lowest adjustment rate for firms in maturity stage. This shows that firms adjust their long-term leverage more speedily during growth and decline stage.

For total leverage, Table 11 reports that all the three lagged leverage (TL L1) are positive as well as strongly significant. This shows that Chinese firms follow a target level of all three proxies of leverage (short term leverage, long term leverage, and total leverage). Total leverage (TL) reports the highest adjustment rate for growing firms (0.596) and lowest for firms in declining stage (0.26). This again confirms that firms in their growing stage tend to speedily adjust to their target level of leverage.

Thus, all three proxies of leverage regression results showed that firms in growing phase speedily fill the gap between actual and target leverage. The reason is that during growing stage, firms have highest investment opportunities and in order to avail these opportunities, there are vigorous adjustment of leverage. For declining stage, adjustment speeds are lowest because during this stage investment opportunities are less and firms do not need to quickly adjust their leverage positions. These adjustment speeds are in line with studies conducted by Getzmann et al. (2014), and Ahsan et al. (2016b). These studies found adjustment speed between 20 and 60% for Asian countries.

Determinants of leverage

This section provides explanation for relationship between leverage and explanatory variables and adjustment of leverage due to these relationships. Table 9 reports a positive and statistically significant coefficient for tax shield (TS) during mature stage. This shows that during decline stages, non-financial firms in China increase short-term debt to gain more tax advantage during mature stage. However, Table 10 shows that tax shield shows a negative relationship with long-term leverage (LL) during decline stage. This might be due to the reason that short-term and long-term debt are negatively correlated (refer to correlation matrices), and thus, it can be inferred that during decline stages firms in China tends to raise less long-term leverage due to tax benefits they get from short term leverage. Table 9 shows that for higher earnings, volatility or business risk shows no association with short term leverage. However, Table 10 for long-term leverage (LL) shows that during growth and maturity, the relationship between business risk (BR) and long-term leverage is negative, and even at maturity stage, it has a negative significant relationship. This shows that as firms enter into maturity, firms realize its long-term profitability and this slows down adjustment in leverage. In decline stage, this relationship for long term leverage (LL Table 10) is positive and thus firm again try to speedily adjust their leverage. For bankruptcy risk (ZS), short-term leverage (SL; Table 9) shows a positive and significant coefficient explaining the disciplinary role of bankruptcy risk in short-term leverage adjustment for growth stage. Table 10 shows that for long-term leverage (LL; Table 10), bankruptcy risk (ZS) shows a positive and significant coefficient during growth which becomes negative and significant during decline stage. This shows that during growth, firms speedily adjust their adjustment rate due to bankruptcy risk in an uncertain environment and this adjustment decreases during decline stage.

For total leverage (TL; Table 11), the bankruptcy (ZS) relationship lacks statistical significance; however, during decline stage, it reports a positive and significant relationship which confirms that leverage is speedily adjusted, and as total leverage include greater proportion of short term leverage, thus, it can be inferred that adjustment involves mainly the short term leverage adjustment during the decline stage for total leverage. Non-debt tax shield (NDTS) shows strong negative relationship with all three proxies of leverage (SL, LL, TL) during maturity and decline stages. This shows that firms in China have greater NDTS advantage as compared to tax shield during maturity and decline. This greater NDTS advantage coupled with less investment opportunities compel firms to raise more debts and thus report a lower adjustment rate during mature and decline stages. Agency costs (AgC) lack statistical significance for all three stages. However, for short-term and total leverage (SL-TL), this relationship is negative and significant in decline stage. This shows that during decline stage, there are not much investment opportunities that can create agency conflicts and firms do not raise more debt to increase its disciplinary role during decline stage. Negative association of growth opportunities (GROW) with short-term leverage (SL; Table 9) indicates that firms do not use short-term loan to finance their long-term investment opportunities, and hence, it has a negative effect on firms leverage’s adjustment rate for short-term leverage. On the other hand, growth opportunities (GROW) show positive and significant relationship with both short-term and total leverage (Tables 10 and 11) in almost all three stages of firm’s life cycle. This indicates that to finance long-term investment opportunities, firms tend to raise long-term leverage and this increases adjustment rate for leverage especially during maturity stage.

Tables 9, 10, and 11 show that both current profit (CP) and past profit (PP) show negative and statistically significant association with short-term (SL), long-term (LL), and total leverage (TL) across all three life cycle stages. This is in line with pecking order theory (POT) that firms first uses internal funds to finance investment projects and then they opt for external financing. This negative relationship slows adjustment of leverage across all three stages of a firm’s life cycle. We find mixed relationship for tangibility (TANG) and collateral value (CV).

However, during decline stage, collateral value shows a positive and significant relationship for long-term (LL) and total leverage (TL). This shows that high collateral value during decline stage gives firm better credit rating for bank financing and thus it speeds up leverage adjustment.

Firm’s size (SIZE) shows a significant and positive association with leverage during all stages. This indicates bigger firms enjoy reputation and have better credit rating in line with trade-off theory. Thus, firm’s size speeds up leverage adjustment across all three stages of firm’s life cycle.

Industry leverage (INDSL, INDLL, and INDTL) shows positive and significant relationship with firm’s leverage indicating that firm follows industry in its leverage decisions across all three stages of a firm’s life cycle. However, industry profitability (INMP) shows both positive and negative relationship. For long-term leverage (Ll), industry profitability shows a negative relationship during growing stage, which is in line with pecking order theory (POT). This shows that as industry becomes more profitable so as the firm, firms tend to use internal funds to finance their investment opportunities and do not raise external funds.

Inflation (INF) shows a positive and significant relationship with short-term (SL) and total leverage (TL) across all three stages of life cycle. This shows that due to the effects of inflation, firms speedily adjust their book leverages and thus inflation speeds up adjustment rate across all three stages. Exchange rate (ER) shows positive and significant coefficients for long-term (LL) and total leverage (TL). This relationship holds for all three stages of life cycle. This shows firms value exchange rate in their leverage adjustment decisions. As most of Chinese firms are export oriented, thus exchange rate has a role in high adjustment rate of Chinese firms. Both economic growth (EG) and capital formation (CF) show mixed results. In growth stage, they report negative while in maturity stage they show positive coefficients. Thus, during mature stages, firms are able to reap the benefits of economic growth and speedily adjust their leverage (Ahsan et al. 2016b).

In the end, Table 12 provides results of post estimation tests for GMM estimation. In all cases, the number of groups are higher than the number of instruments. The p values for both Sragan and Arelleano Bond test are insignificant which confirms the estimation through generalized method of moments.
Table 12

Sargan and Abond test results

 

Number of groups

No. of instruments

Sargan Test

Abond Test

Short-term leverage

 Growth

218

95

0.163

0.3685

 Maturity

651

95

0.2321

0.2784

 Decline

168

95

0.4733

0.7261

Long-term leverage

 Growth

204

95

0.3057

 

 Maturity

600

95

0.033

0.095

 Decline

150

95

0.3328

0.4291

Total leverage

 Growth

252

95

0.1517

0.1027

 Maturity

699

95

0.3105

0.8352

 Decline

190

95

0.4264

0.231

Table 13

ANOVA

Variable

Stages

F Statistics

Growth

Maturity

Decline

F value

Prob > F

SL

0.17

0.17

0.15

35.23

0.000

LL

0.1

0.1

0.1

43.12

0.000

TL

0.58

0.63

0.53

65.34

0.000

TS

0.07

0.15

0.15

27.21

0.000

ZS

3.06

6.93

3.47

26.13

0.000

BR

-0.06

-0.4

2.16

27.12

0.000

NDTS

0.03

0.03

0.03

0.87

0.429

AgC

0.05

0.06

0.05

23.15

0.000

GROW

0.18

0.81

0.13

29.36

0.000

CP

0.04

0.06

0.05

21.47

0.000

PP

0.52

0.47

0.48

22

0.000

LIQ

1.34

1.39

1.72

33.19

0.000

TANG

0.26

0.24

0.24

27.15

0.000

CV

0.53

0.55

0.57

0.17

0.368

SIZE

22.05

21.89

22.18

24.12

0.000

AGE

2.67

2.87

3.02

13.14

0.000

INDSL

0.17

0.17

0.17

0.19

0.247

INDLL

0.1

0.1

0.1

0.14

0.637

INDTL

0.59

0.6

0.68

12.14

0.000

INMP

0.05

0.04

0.02

0.28

0.234

INF

2.83

2.68

2.43

19.58

0.000

ER

7.07

7.17

7.27

11.12

0.000

EG

9.93

9.9

9.9

0.53

0.186

CF

45

44.65

44.25

24.15

0.000

ANOVA analysis and robust estimation

In order to add robustness to our findings, we conducted ANOVA analysis and estimated our equation with a more novel method of dynamic estimation (Blundell and Bond system estimation). Table 13 corresponds to robustness analysis.

Analysis of variance suggests that null hypothesis for most of the variables is rejected which suggest that means of different groups (across life cycle) are not equal. For one firm-specific variable non-debt tax shield (NDTS), the null hypothesis of difference of means cannot be rejected. For industry level for short-term and long-term leverage, there exists no significance difference of means.

Further, the Blundell and Bond GMM estimation is in conformance with our previous estimation of dynamic adjustment speed except for the short-term leverage. Both long-term and total leverage (Tables 14 and 15) reports results in consistency with our previous estimation.
Table 14

Blundell and Bond GMM estimation for long-term leverage

 

Growth

Maturity

Decline

Coef.

Z

P > z

Coef.

z

P > z

Coef.

z

P > z

Adjustment rate

0.735

  

0.420

  

0.536

  

LL(L1)

0.265

5.31

0.000

0.580

12.560

0.000

0.464

6.33

0.000

TS

−0.062

−8.09

0.000

0.000

−0.630

0.527

−0.022

−5.26

0.000

BR

0.005

4.44

0.000

0.000

−0.310

0.753

−0.001

−2.16

0.031

ZS

−0.002

−3.49

0.000

0.000

−0.860

0.389

−0.001

−8.3

0.000

NDTS

0.522

3.04

0.002

−0.753

−2.830

0.005

−0.679

−4.18

0.000

AgC

0.016

0.18

0.860

0.026

0.320

0.750

−0.099

−1.32

0.187

GROW

−0.007

−2.21

0.027

0.027

4.300

0.000

0.010

2.29

0.022

CP

−0.066

−5.42

0.000

−0.103

−4.630

0.000

−0.102

−6.05

0.000

PP

0.000

−6.79

0.000

0.000

0.740

0.459

0.000

1.95

0.051

LIQ

0.020

6.67

0.000

0.027

4.310

0.000

0.014

8.83

0.000

TANG

−0.012

−3.72

0.000

0.003

0.230

0.817

−0.016

−2.66

0.008

CV

0.016

1.53

0.126

0.056

6.270

0.000

0.047

10.65

0.000

SIZE

0.024

5.36

0.000

−0.010

−1.400

0.161

0.042

8.44

0.000

INDLL

0.338

4.12

0.000

0.431

5.270

0.000

0.419

11.42

0.000

INMP

−0.004

1.96

0.043

0.000

1.140

0.270

−0.006

0.34

0.148

INF

−0.001

0.28

0.486

−0.002

0.240

0.586

0.000

0.48

0.476

ER

0.041

13

0.000

−0.005

−0.810

0.420

0.023

5.97

0.000

EG

0.003

3.83

0.000

0.001

0.990

0.324

0.000

−0.57

0.571

CF

0.001

3.22

0.001

−0.001

−0.820

0.413

−0.001

−1.69

0.091

_cons

−0.898

−6.75

0.000

0.214

0.970

0.332

−0.999

−6.84

0.000

Table 15

Blundell and Bond GMM estimation for total leverage

 

Growth

Maturity

Decline

Coef.

z

P > z

Coef.

z

P > z

Coef.

z

P > z

Adjustment rate

0.604

  

0.287

  

0.278

  

TL(L1)

0.397

6.730

0.000

0.713

10.870

0.000

0.722

7.630

0.000

TS

−0.687

−0.890

0.372

−0.060

4.870

0.000

−0.490

−2.520

0.012

BR

−0.218

−1.300

0.194

0.026

0.900

0.368

−0.160

−1.850

0.064

ZS

−0.195

−1.850

0.064

−0.001

−1.130

0.258

0.066

4.660

0.000

NDTS

1.250

−4.590

0.000

0.730

−5.080

0.000

1.120

−1.820

0.069

AgC

0.128

1.920

0.055

0.005

0.060

0.949

0.007

−1.820

0.069

GROW

0.005

2.750

0.006

0.007

5.010

0.000

0.003

3.820

0.000

CP

−0.173

−1.530

0.125

−0.147

−7.730

0.000

−0.213

−4.220

0.000

PP

−0.014

−2.460

0.014

−0.008

−3.190

0.001

−0.005

−2.100

0.035

LIQ

−0.102

−23.920

0.000

−0.101

−2.080

0.037

−0.716

−6.190

0.000

TANG

0.233

0.180

0.857

−3.449

−2.260

0.024

−2.999

−2.580

0.010

CV

−0.433

−6.340

0.000

−0.510

2.090

0.036

−0.689

2.780

0.005

SIZE

0.023

5.500

0.000

0.031

3.670

0.000

0.036

6.880

0.000

INDLL

0.062

3.250

0.000

0.058

4.210

0.000

0.038

3.140

0.000

INMP

0.004

1.370

0.287

0.002

0.376

0.145

0.002

0.852

0.129

INF

0.020

2.140

0.000

0.031

3.540

0.000

0.050

4.120

0.000

ER

0.038

3.870

0.000

0.039

4.780

0.000

0.035

3.160

0.002

EG

0.311

2.270

0.023

0.417

3.340

0.001

−0.039

−0.300

0.767

CF

0.147

1.910

0.056

0.222

3.600

0.000

0.071

1.070

0.284

_cons

−0.663

−3.100

0.002

−1.340

−4.040

0.000

−0.830

−5.880

0.000

For long-term leverage, Table 14 reports adjustment rates of 0.73, 0.42, and 0.53 for growth, maturity, and decline stages, respectively. The trend in dynamic adjustment rate for long-term leverage according to the system estimation (Blundell and Bond) is in accordance with our argument that adjustment of financial leverage is higher during the growth stage; it reduces toward maturity and then increases during the decline stage.

Similarly, Blundell and Bond estimation for total leverage (Table 15) shows that financial leverage in Chinese firms follows a high-low-high pattern across the three stages of firm life cycle. Our ANOVA analysis and system GMM estimation add robustness to our results.

Conclusions

This study tries to investigate how firms adjust their leverage policy across the firm’s life cycle. For this purpose, the study uses an extensive set of data of 860 A-listed Chinese non-financial firms over a 19-year period (1996–2014). The study uses Arellano-Bover/Blundell-Bond dynamic panel data model to estimate adjustment rate of leverage and its determinants in three different life stages of Chinese firms. We find that adjustment rate of leverage varies for different life stages. In accordance with the trade-off theory of capital structure, this study reports a low-high-low pattern of leverage across growth, maturity, and decline stage of firms’ life, respectively. For total leverage, dynamic panel data reports highest adjustment rate for growing firm, followed by mature firms, and then firms in declining stage of their life. Both short-term and long-term leverage report similar pattern of leverage’s adjustment rate across the three life cycles. The firm life cycle measure in this study is based on a multivariate technique using firm’s age, sales growth, and dividend payout ratio.

The study finds that profitability is one of the integral determinants of leverage adjustment in China in line with pecking order theory. All determinants had implications for long-term and total leverage.

The study provides useful insight in young and unique market setting of Chinese financial markets and prevalent of bank loans in Chinese market. The study will help policy makers to increase financing options in debt abundant financial markets like China.

Declarations

Authors’ contributions

This study is conducted by AR, MW, and HY, to investigate the dynamics of leverage policy across firm’s life cycle in China using innovative GMM technique. All the authors equally participated to conduct and write this research paper. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors’ Affiliations

(1)
School of Accounting, Dongbei University of Finance and Economics

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Copyright

© The Author(s). 2016