from ‘borrowing for repaying’ to ‘repaying for...

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From ‘Borrowing for Repaying’ to ‘Repaying for Borrowing’:

Does the Way of Rollover for Short-term Loans Affect Bank Monitoring?

Haiming Liu*

School of Finance, Shandong University of Finance and Economics

Email: [email protected]

Yao-Min Chiang

Department of Finance, National Taiwan University

Email: [email protected]

Abstract

In 2007, China has implemented Guideline 2007, which changes banks’ way of loans rollover from

‘borrowing for repaying’ to ‘repaying for borrowing’. This paper investigates how bank monitoring is

affected when banks’ way of rollover changes by examining the effect of this regulatory change on

tunneling by controlling shareholders. The PSM-DID tests show that after implementation of

Guideline 2007, firms more heavily relying on short-term loans see a larger decline in tunneling by

controlling shareholders. And this effect is more pronounced for firms with more tunneling, larger

controlling right, lower EBIT, less tangible assets and less total assets. Furthermore, short-term loans

and leverage firms reliant more on short-term loans go down to a more extent. Finally, EBIT of firms

more heavily relying on short-term loans increases more while cash holding decreases. These results

suggest that changing banks’ way of rollover for short-term loans can also enlarge their monitoring

capacity and leads to better resources allocation even when the amount of short-term loans decreases.

Keywords: Short term loans, Regulation change, Refinancing, Tunneling effect, Monitoring effect.

January, 2018

* Corresponding author, Comments are welcome.

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1. Introduction

Monitoring is considered as one of the bank’s most distinctive and important activities (Freixas

and Rochet, 1997). As is shown by financial intermediation theory, commercial banks have incentives

and advantage to collect information and monitor borrowing firms compared with other forms of

lenders (Diamond, 1984; Ivashina et al., 2009; Ahn and Choi, 2009). Among monitoring devices for

banks, short-term loans are an important one. A short-term loan always matures and needs to be rolled

over before firms’ long-term project is finished. When rolling over loans, banks can negotiate with

firm managers and monitor (Myers, 1977). If firms do not repay their loans or listen to the suggestion

from banks, banks will refuse to roll over the loans. In other words, each rollover for short-term loans

provides an opportunity to monitor, making short-term debt an extremely powerful tool to monitor

management (Stulz, 2000; Diamond, 2004). And Diamond (2004) shows that in economies with weak

institutions, short-term loans can provide an credible threat to potential misbehaving borrowers.

What factors determine bank monitoring capacity from short-term loans? Previous research

confirms that increasing use of short-term loans, or shortening debt maturity can effectively enhances

bank monitoring capacity (Barclay and Smith, 1995; Datta et al., 2005; Gul and Goodwin, 2010).

Since each rollover provides an opportunity to monitor, as suggested by Stulz (2000) and Myers

(1977), increasing amount of short-term loans enhances banks’ negotiation power at time of debt

rollover.

But what will happen to monitoring capacity if banks change their way of rollover? Previous

research treat the way of rolling over loans as given and their implicit hypothesis is that short-term

loans can function as a monitoring device automatically. However, it is possible that although

short-term loans provide banks with opportunity to monitor their debtors, they do not use this power

well as they follower certain ways of rollover. For example, in China, a state-owned banking system

with weak institutions (Allen et al., 2005), when borrowing for repaying is widely used or even

encouraged, bank managers are more willing to extend excessive short-term loans. At time of rollover,

bank managers renew loans easily and rarely monitor, even for bad firms. Through collaborating with

bad firms, bank managers can cover the potential non-performing loans (NPLs hereafter for short) to

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avoid being concerned by supervisory agency and can also be rewarded for more loans extension or

empire building. This is just the case in China before 2007. So in certain institutions, banks exert little

monitoring on firms (Firth et al., 2008), and more loans can even leads to worse corporate governance

(Qian and Yeung, 2015). Under this circumstance, changing banks’ way of rolling over through

regulation can possibly make sense for releasing bank monitoring potential. This paper will investigate

how way of rollover for short-term loans affects bank monitoring capacity, or concretely, on tunneling

of controlling shareholders.

China’s background provides us with an ideal setting for investigating the above-mentioned issue.

Firstly, China has underwent a regulatory change about way of rollover for short-term loans. Before

2007, these exist two mode of rollover, borrowing for repaying and repaying for borrowing. The

former refers to that before previous loans mature, banks sign new loans contract with firms and

provide new loans for repaying previous loans; the latter refers to that after previous loans mature,

firms should firstly repay the loans, then banks review firms’ condition and decide whether to lend

new loans. Before 2007, borrowing for repaying is specially encouraged and widely used. As

mentioned before, in a state-owned banking system, bank managers are more willing to easily roll

over short-term loans to cover NPLs and get more reward for more loans origination, making

borrowing for repaying popular. In 2007, however, China Banking Regulatory Commission (CBRC)

brought ‘loan risk classification guideline’(Guideline 2007 hereafter for brevity) into effect. Clause 10

of Guideline 2007 almost prohibited the previously widely adopted practice of ‘borrowing for

repaying’ and only allowed ‘repaying for borrowing’ for short-term loans and thus changed banks’

way of rollover. As other regulatory changes, Guideline 2007 was initiated by authorities and hardly

predicted and affected by firms and commercial banks. So, this regulatory change constitutes an

natural experiment for changing way of rollover for short-term loans.

Secondly, as in other transitional economy, tunneling by majority shareholders is much severe

and is a major corporate governance problem in China. Jiang et al.(2010) find that billions of RMB is

transferred from listed firms to its controlling shareholders. Too much tunneling decreases firm

performance and their capacity for repaying. So, if the Regulation 2007 affects bank monitoring, it

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will impact tunneling at the first place. More importantly, in China, bank loans are highly positively

linked with, or even a main source of tunneling (Qian and Yeung, 2015). In practice, listed firms

obtain funds from bank loans and then these funds are transferred by controlling shareholders (Bailey

et al., 2011). Therefore, it needs more monitoring by creditors. And the impact of regulatory change on

bank monitoring can be more easily found in China by examining the effect of Guideline 2007 on

tunneling. So we specially focus on the perspective of tunneling to examine the issue.

Thirdly, as an typical monitoring instrument, short-term loans not only constrain managers’

opportunistic behavior, but also allows insiders or controlling shareholders less discretion (Fan et al.,

2012) and constrain shareholders’ expropriation activities (Díaz-Díaz et al., 2016). Controlling

shareholders with more private benefit always avoid using more short-term loans (Shyu and Lee,

2009). Diamond (2004) contends that in transitional economies with weak institution, short-term loans

can be an very powerful tools for monitoring. We can predict less expropriation from block

shareholders if bank monitoring from short-term loans increases.

Fourthly, different from USA and other developed countries, short-term loans are an important

source of financing in China and other transitional economies, making its monitoring role fairly

important. As is shown by Fan et al. (2012), in the USA, more than 80% of debt belong to long-term

debt, while in China (developing countries), more than 90% (60%) of debt is short-term. Among these

short-term debt in China, loans account for a significant part. Our sample shows that short-term loans

accounts for 35% of short-term debt and 30% of total debt.

We use PSM-DID methodology to estimates the effect of Regulation 2007 on controlling

shareholders’ expropriation. We take advantage of the fact that some sectors in the economy naturally

rely more on short-term loans than others do. We conduct a difference-in-differences test in which we

contrast firms in sectors with high versus low demand for short-term loans, before and after the

passage of Guideline 2007. Besides, to avoid the systematic difference between these two groups of

firms driving our results, we use propensity matching methods(PSM) to ensure well-suited

comparison.

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Our results show that after implementation of Guideline 2007, firms in sectors more intensive in

short-term loans witness less tunneling by block shareholders. This effect is more pronounced in firms

with more tunneling, larger controlling right, lower EBIT, less tangible assets and less total assets.

These results suggest that changing banks’ way of rollover also makes great senses for enhancing

monitoring from short-term loans. Furthermore, after 2007, firms in sectors more intensive in

short-term loans observe a larger decrease in short-term loans and total leverage, which means that

Guideline 2007 also has financing shock effect. Finally, EBIT of firms reliant more on short-term

loans increases to a more extent and cash holdings decrease although their investment is not affected,

which indicates that effect of better monitoring by Guideline 2007 outpaces the financing shock effect

and further confirms that this regulatory change improves bank monitoring role.

Our results indicate that changing way of rollover makes senses for bank monitoring. In China,

with a state-owned banking system and weak institutions as shown by Allen et al. (2005), there exists

severe agency problem between bank shareholders and bank managers. And bank managers can

collude with bad firms to renew short-term loans to get more reward and avoid supervisory

punishment by easily allowing to borrow for repaying. This constrain monitoring role played by

short-term loans. Through the way of prohibiting borrowing for repaying, the regulatory change forces

managers to recover previous debt before originating new loans. This rule prevents the collusion and

changes the contracting of rollover, enhancing banks’ information about firm repayment and their

prudent behavior at time of rollover. Therefore, this rule help release banks’ monitoring potential from

short-term loans.

Our work makes contributions to several strands of literature. Firstly, our work extend the

research on monitoring role of short-term loans or debt maturity. Previous empirical research predicts

that increasing short-term loans or shortening loans’ maturity enhances bank monitoring (Barclay and

Smith, 1995; Datta et al., 2005; Cheng and Milbradt, 2011). They takes banks’ way of rolling over

debt as given and implicitly hypothesize that short-term loans can function as a monitoring device

automatically. However, in certain institutions, agency problem and other forms of friction may

constrain monitoring role of short-term loans. Although each rollover offers banks an opportunity to

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monitor and negotiate (Stulz, 2000; Myers, 1977), banks may have varied capacity to seize this

opportunity due to different rollover regulations. By using 2007 regulatory change about rollover

policy as an exogenous shock, our paper confirms that banks’ monitoring capacity can also be

enhanced as they (are forced to) change their way of rollover. Moreover, we document that enhanced

monitoring can happen even if firms use less short-term loans or lengthen debt maturity, which further

supports that banks’ way of rollover is another important factor for their monitoring. This result is

somewhat surprising from the point of previous literature that emphasize a positive relation between

short-term loans and bank monitoring.

Second, our work sheds light on literature about how to control shareholders’ tunneling. Previous

research emphasizes that judicial enforcement, as well as securities market regulation, can protect

minority shareholder from expropriation (La porta et al., 2000; Berkman et al., 2010), while Jiang et al.

(2010) find that high-quality auditing can help constrain shareholder tunneling. Our results show that

through proper design and enforcement of financial contracting impelled by banking reform, just as

enforcing a new way of rollover for short-term loans contract in our paper, can also constrain insiders’

tunneling. Our results thus coincide with Fan et al. (2012), which also find that proper financial

instrument can help reduce insiders’ discretion.

To note, our results have some implications for economies that are not limited to these with

state-owned banking system. For transitional economies, where short-term debt are dominant and

formal institutions are insufficient, there can exists agency problem between banks shareholders and

manager. Borrowing for repaying can be a way of obtaining private benefit if managers are rewarded

for empire building. And rollover restriction can enhance monitoring in such a context. Besides, our

work helps arouse more attention on the incentives and contracting of rollover for short-term debt, not

just the amount of short-term loans, for the other economies in the rest of the world.

2. Institutional Background, Related Literature and Hypothesis

2.1 Institutional Background

In 1992, Chinese government promised to establish a market-oriented economy with Chinese

characteristic. This process began to accelerate after China’s entry of World Trade Organization (WTO)

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in 2001. At that time, central government made many commitments for joining in WTO, including

gradually removing the restrictions on activities of foreign banks. Then more measures about domestic

banking reform were introduced to prepare for future fierce competition in banking industry after

formal liberalization. These measures include attracting foreign strategic investors as domestic banks’

shareholders, disposing of non-performing loans and imposing more stringent guideline on

commercial banks.

Before 2007, there exist two ways of rollover, borrowing for repaying and repaying for

borrowing. The former refers to that before previous loans mature, banks sign new loans contract with

firms and provide new loans for repaying previous loans; the latter refers to that after previous loans

mature, firms should firstly repay the loans and banks review firms’ condition and decide whether to

lend new loans. Before 2007, borrowing for repaying was not only allowed, but also widely used.

Firms could easily borrow new bank loans for repaying their due loans. According to the clause 9 of

“Provisional Regulations about Non-Performance Loan Identification”(bu liang dai kuan ren ding zan

xing ban fa) in 2000, abbreviated as Regulation 2000, loans that are renewed for repaying previous

loans, or borrowing for repaying, should be treated as normal debt by commercial banks as long as

four conditions are satisfied. These conditions include: firm is in normal operation; firm can repay the

interest; loan collateral or guarantee is valid; and loans belong to revolving loans. Regulation 2000

provides an almost unconditional debt rollover avenue for firms. At time of rollover, firms only

needed to repay interest of previous loans, not including principal. Borrowing for repaying is specially

encouraged by local government because at that time stock market in China was immature and firms’

normal current assets were largely financed by bank short-term loans. Besides, in practice, borrowing

for repaying was much more popular than repaying for borrowing as the former can help conceal

NPLs for bank managers. In a state-owned dominant banking system, where managers are rewarded

by size and quality of loans, bank managers have incentives to originate more short-term loans. At

time of rollover, bank managers can easily allow firms’ ‘borrowing for repaying’, even for

nearly-bankrupt firms and other bad firms. Through this way, managers can cover the NPLs to avoid

being punished by supervisory agency and get more reward for more loans extension.

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This rule was terminated as China gradually imposes more stringent regulation on banks.

Because borrowing for repaying was treated as a way of covering NPLs, financial statement of banks

underestimates the quantity of non-performing loans. In 2007, a newer edition, “Loan Risk

Classification Guideline” (dai kuan feng xian fen lei zhi yin) or Guideline 2007 devised by banks’

supervisory agency, China Banking Supervisory Commission (CBSC), began to take effect. The

purpose of Guideline 2007 is to guide banks to promote loans management and scientifically evaluate

loans quality. In Clause 10, it is stipulated that loans that are borrowed for repaying due loans or

‘borrowing for repaying’ should be classified as non-performance loans. And commercial banks with

more NPLs will face harsh supervision and have to draw a 2% loan loss provision. If banks do not

want to increase their NPLs, interest and principal of loans that are due must be fully withdrawn

before new loan contract is signed. That is, only repaying for borrowing is allowed, it almost becomes

the only mode of rollover after 2007. Before 2007, however, banks did not need to recover their

principal of loans that are due at time of rollover. They only required firms to repay interest. In other

words, before 2007, both borrowing for repaying and repaying for borrowing are allowed and the

former is more popular. Therefore, Guideline 2007 sets more restrictions on rollover for short-term

loans by changing banks’ way of rollover from ‘borrowing for repaying’ to ‘repaying for borrowing’.

CBSC also took some measures to prevent commercial banks from circumventing Clause 10. In

practice, when previous loans are nearly mature, lending banks can help firms get funds from other

institutions to repay these previous loans. After previous loans being payed back, lending banks

provide new loans for firms. Finally firms can repay funds from other institutions using new loans.

While this is a way of providing loans that are similar with borrowing for repaying, CBSC explicitly

prohibits lending banks acting as mediator between firms and other lending institutions. So the

Guideline 2007 has material impact on bank practice.1

1 Several reasons can account for the effectiveness of Guideline 2007. Firstly, CBSC, who devises Guideline 2007, is a

powerful supervisory agency, it can impose punishment or even revoke the financial licenses if banks do not listen to its

order. And it is also responsible for approving nomination of banks’ central and regional managers. Secondly, recent

years witness some criticism of Clause 10 in Guideline 2007 and also appeals for allowing borrowing for repaying.

Finally, after Guideline 2007 was into effect, a new form of finance, called bridge loans for borrowing for repaying,

comes out. Some private institutions lend funds, with very high interest rates, to firms before previous bank loans are

mature, and get repayment after firms get new funds from the same banks of previous loans.

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2.2 Related Literature: monitoring role of debt maturity

It is widely accepted that debt is an effective monitoring device to reduce the agency costs. Debt

can reduce resources under managers’ control and inhibit their opportunistic behavior, including

inefficient investment (Jensen, 1986; Stulz, 2000). Consistent with this conjecture, empirical research

documents that firm value and investment is negatively related with the level of debt, and this effect is

mainly pronounced in low-growth firms, which have higher probability of over-investment

(McConnell and Servaes,1995; Lang et al., 1996; Ahn et al., 2006). Firth et al. (2008) also confirm the

disciplinary role of debt in China, although this effect is weakened by a state-owned banking system.

Apart from debt level, some research suggests that debt maturity also matters for bank

monitoring. Myers (1977) argues that as debt maturity shortens, rollover and renegotiation of debt

contract will help mitigate under-investment problem for high-growth firms. Stulz (2000) contends

that use of short-term loans increases frequency of bank monitoring and constitutes a powerful tool to

monitor management. Rajan and Winton (1995) suggest that by allowing banks to demand repayment

more frequently on their information, short-term loans give banks greater flexibility to monitor firms.

Diamond (2004) finds that short-term debt lead to negative externalities among multiple lenders when

a single lender stop borrowing or going to the court, making other lenders demand their payment. This

will constitute a credible commitment to borrowing firms and force them to behave properly.

Empirical research also confirms the monitoring role of short-term debt. Johnson (2003) finds

that shorter debt maturity can attenuate the negative effect of growth opportunities on leverage, which

indicates that short-term loans can help reduce under-investment problem. Similarly, Aivazian et al.

(2005) document that shortening debt maturity can increase firm investment and this effect is only

concentrated in high-growth firms. This result indicates that short-term debt can help resolve

underinvestment problem. Datta et al. (2005) find that as managers are more aligned to shareholders,

they will use more short-term debt that facilitate monitoring. Stohs and Mauer (1996) postulate that

small firms, whose agency problem is more severe, use more short-term debt and thus roll over their

debt more frequently. Gul and Goodwin (2010) find that amount of short-term debt is negatively

related with audit fees, which indicates that short-term debt can bring in more monitoring and better

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governance. Brockman et al. (2010) confirm that short-term debt can mitigate the positive relationship

between manager incentives and firm risk. Chen et al. (2014) argue that block shareholder of small

family firms prefers opacity and thus enjoys private benefit of controlling at the cost of less arm’s

length financing. These firms are more dependent on monitored finance like short-term debt. Gomariz

and Ballesta (2014) find that short-term loans can help reduce over-investment and under-investment.

El Ghoul et al. (2016) confirms that short-term debt and high-quality audits are substitutes in

corporate governance. Because short-term debt, as an financial instrument, can monitor these insiders.

Firms located in area with worse investor protection will use more short-term debt. Fan et al. (2012)

confirm it by making a cross-country comparison. They find that in China with bad investor protection,

about 90% of debt belongs to short-term debt.

In all, previous research shows that the level of short-term debt or loans is an important device

for banks and other lenders to exert monitoring. Among these lenders, banks are an special lender with

advantage of monitoring and information (Freixas and Rochet, 1997). The monitoring role of

short-term loans from banks can make big sense. Since each rollover of short-term loans or

renegotiation process gives banks opportunity to monitor as shown by Stulz (2000), whether banks

can seize this opportunity can also affect their monitoring capacity. This issue, however, is not

explored by these research, because they take how banks roll over debt as given. China’s Guideline

2007 alters banking rollover practice. And this regulatory change provides us with an ideal setting to

investigate what happened to bank monitoring capacity if they change the way of rollover and thus the

extent to which they seize their opportunity to monitor for each rollover.

2.3 Hypothesis Development

2.3.1 The impact of Guideline 2007 on tunneling

In China, a typical transitional economy with weak institutions and state-owned dominant

ownership of banking system (Allen et al., 2005), monitoring capacity of banks can be weakened and

distorted (Firth et al., 2008). Similarly, in such an economy, there exist severe agency problem and

other frictions in commercial banks, and monitoring role of short-term loans can also be weakened,

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especially when borrowing for repaying is allowed. As we document before, when borrowing for

repaying is allowed before 2007, it receives great popularity. Bank managers, including branch

managers, have incentives to originate more short-term loans and roll over them easily in a rewarding

system based on loans size. At time of rollover, these managers can possibly collude with firms.

Managers are willing to easily allow firms’ borrowing for repaying and transfers blood to sustain the

operation of bad firms in order to cover the NPLs. They do not have incentives to monitor. Through

this way, more loans extension and less face value of NPLs on financial statement enable managers to

get more reward and receive less supervision by CBSC. Therefore, under China’s background, with

agency problems and other friction in commercial banks, borrowing for repaying disables monitoring

function arising from short-term loans and makes short-term loans essentially not different from

long-term loans.

The inability of short-term loans to monitor has changed after the introduction of Clause 10 in

Guideline 2007. Guideline 2007, initiated by powerful supervisory agency CBRC, forces banks to roll

over loans almost only through ‘repaying for borrowing’ instead of ‘borrowing for repaying’. This

disables bank managers’ incentives to cover the NPLs through borrowing for repaying and enhances

banks’ monitoring role at time of rollover. The reasons are as follows:

Firstly, Guideline 2007 makes banks managers collect information and review firm conditions

more carefully at time of rollover, thus enhancing banks’ monitoring capacity. After 2007, the gate of

covering NPLs by borrowing for repaying is shut down and NPLs are not easily concealed. If bank

managers act at their will and extend new loans at time of rollover regardless of firms’ repayment

condition, or even collude with firms as before, more NPLs will come out on financial statement. In

contrast, before 2007 banks are not worried about this for the easiness of concealing NPLs. And these

managers will be focused by CBSC and get warning or punishment for more NPLs. Therefore, after

NPLs can not be covered easily, bank managers will review firms’ condition more prudently and

negotiate with firms about their investment plans at time of rollover. If firms’ condition is not good or

firms do not listen to the suggestion of banks, bank managers, in anticipation of more face value of

NPLs, will refuse to provide new loans after previous loans are withdrawn. In other words, after 2007,

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banks can seize opportunities of monitoring at each rollover of short-term loans as suggested by Stulz

(2000), and Guideline 2007 really helps release bank monitoring potential. Under more monitoring,

large shareholder of borrowing firms will decrease their misbehavior or tunneling, in order to fulfill

banks’ requirement and increase their repayment capacity.

Secondly, Guideline 2007, which only allows repaying for borrowing, can help reveal firms’

repayment problem and other problem, and thus passively enhance banks’ information about firms’

repayment ability. After 2007, when firms are forced to repay interests and principal instead of

interests only, firms with repayment problems are more likely to be detected.2 Banks with more

information can monitor firms much easier. Controlling shareholders of firms, who anticipate more

repayment information revelation, will decrease their misbehavior or tunneling, for tunneling can lead

to lower repayment capacity (Johnson et al., 2000) and can lead to refusal of rollover after 2007.

Thirdly, short-term loans initiated by multiple banks to a single firm can act as commitment

device that forces firms or insiders to behave properly ex ante after 2007. In practice, there exists more

than one banks providing loans to a single firm. As Diamond (2004) shows, run by one lender can

exert negative externalities on other lenders and leads to their losses, so every lender works hard to

survey information of firms’ repayment problem and try to run firstly before firm defaults. And this

constitutes a commitment device of short-term loans monitoring to limit insiders’ misbehavior. After

2007, firms are more likely to expose their repayment problem and banks managers collect

information more carefully. This increase probability of runs by banks and also the credibility of

commitment device of short-term loans, thus enhancing monitoring role of short-term loans.

In all, the implementation of Guideline 2007 enhances banks’ monitoring capacity from

short-term loans and thus constrains tunneling of block shareholders. Because Guideline 2007 aims at

short-term loans, we predict that the effect of Guideline 2007 will be larger for firms reliant more on

short-term loans. Thus these firms will see a larger decline of tunneling after 2007, compared with

firms less reliant on short-term loans.

Hypothesis 1: For firms more reliant on short-term loans, short-term loans will go down to more

2 In practice, firms can borrow bridging funds from other banks and private institutions. However, it is hard to find a

bank willing to lend bridging funds and the interest rate of private institutions is fairly high.

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extent after 2007 compared with those less reliant.

2.3.2 The impact of Guideline 2007 on leverage

Next we will discuss how Guideline 2007 affects firm capital structure. There exist two opposite

effects of this regulatory change on firm leverage, direct effect and indirect effect.

The direct effect refers to that Clause 10 of Guideline 2007 is firstly a financing shock to firms. It

prohibits the easy rollover of short-term loans, thus limiting firms’ lending of short-term loans. While

firms can substitute short-term loans with other source of debt, market frictions can limit firms’

financing substitution (Leary, 2009). This will be more obvious in a transitional economy with weak

institutions. For example, collateral requirement and longer approval process will limit firms to

borrow long-term loans, and immature bond market in China will limit the substitution using bonds.

Therefore, under this financing shock, firms’ short-term loans ratio and leverage will go down,

especially for firms more dependent on short-term loans.

The indirect effect comes from the fact that Guideline 2007 brings about better corporate

governance, so banks should be more willing to provide short-term loans to firms. Under this

governance shock, short-term loans ratio and leverage should increase.

We postulate that as for the impact on leverage, the direct effect is larger than the indirect effect.

Guideline 2007 is firstly a financing-related regulation, and governance shock from this regulation is

more indirect and less important in determining capital structure. Moreover, although better

governance after 2007 will attract more lenders, one should keep in mind that Guideline 2007 helps

crowd out excessive debt arising from bank managers’ collusion with firms when borrowing for

repaying is allowed. Therefore, Guideline 2007 makes firm leverage do down, especially for firms

reliant more on short-term loans.

Hypothesis 2: For firms more reliant on short-term loans, leverage will decrease to more extent

after 2007 compared with those less reliant.

3. Sample description and methodology

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3.1. Sample description

Our sample data are obtained from the Chinese Stock and Market Accounting Research (CSMAR)

database for all firms listed on the Shanghai and Shenzhen stock exchanges. Initially, after excluding

observations in financial industry, we collect 9298 firm-year observations from 2003 to 2010. Then we

keep samples of top quartile and low quartile sorted by short-term loans to total assets ratio, and get

4675 observations. Finally, we exclude these samples that fail to match using PSM methodology and

get 2957 firm-year observations.

3.2 Methodology

We employ Propensity Score Matching (PSM) and Difference-in-Difference (DID)

methodologies to estimate the effect of implementation of Guideline 2007.

The DID methodology initially compares the effect of an event on treatment firms affected by

this event with control firms that are not affected. By using this double differences, we can eliminate

impact of common shocks other than our interested event. Because the legal reform happens at the

country level and applies to all firms, initially we do not have any natural treatment and control groups

for our DID analysis. However, because Guideline 2007 has material impact on rollover of short-term

loans, it should be particularly impact firms intensive in the use of short-term loans. We just take

advantage of this fact and construct our treatment group and control group. Concretely, we compute

the mean value of pretreatment (from 2003 to 2006) cross-sectional short-term loans dependence for

each firm, where short-term loans dependence is defined as short-term loans to total assets ratio. Then

we divide our sample firms into quartiles (top 25%, middle 50%, and low 25%) by their pretreatment

average short-term loans dependence. We define highest quartile as treatment group and lowest

quartile as control group.

Before employing DID methodology to estimate the effect of Guideline 2007, we should ensure

that trends of the outcome variable (tunneling) for both groups should be similar prior to the reform.

However, the parallel trends hypothesis can possibly be not satisfied automatically. In practice, firms

with certain characteristics, lower growth opportunity or bad governance, can obtain more funds from

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bank loans in China’s institution and at the same time block shareholders are more prone to transfer

funds in these firms (Bailey et al., 2011). Also, there exist some difference about these characteristics

between treatment group and control group. In other words, firms in treatment group and control

group are not assigned randomly, and variables that determine likelihood of being treated can also

affect the amount and trend of outcome variable (tunneling). This constitutes the main source of

non-parallel trend. We use PSM methodology proposed by Abadie and Imbens (2006) to solve this

issue. Concretely, we try to find the unit in control group that is closest in terms of these covariates

that determines probability of being treated with each unit in treatment group before 2007 reform.

After dropping samples with poor overlap, we can ensure that control group and treatment group are

similar in the covariates that determine being treated and thus shareholders’ expropriation before

Guideline 2007 was introduced. These two groups can be viewed as randomly assigned and should

share similar trend in outcome variables after PSM. Apart from this, the PSM methodology can also

avoid some alternative explanations. We will testify these issue latter.

3.3. Model Specifications

To evaluate the effect of Guideline 2007 on tunneling, we estimate the following regression

specification in accordance with Campello and Larrain (2016) using firm-year panel data:

ijtjtjijtitittiijt XTreatPostTreatPostTunnel 3211 (1)

Where i indexes firm, j indexes industry and t indexes time. Dependent variable is Tunnel,

computed as other receivables divided by total assets. αi and γt are firm and year fixed effect. ϕj

represents industry fixed effect. γt×ϕj represents year-industry fixed effect. Post is an indicator variable

that takes on value of 1 in years that Guideline 2007 is in place (2007, 2008, 2009 and 2010) and 0

otherwise (years before 2007). Treat is an indicator variable that takes on value of 1 if firm belongs to

treatment group (high short-term loans dependence) and 0 if firm belongs to control group (low

short-term loans dependence). Coefficient on interaction term Post×Treat or β3 is the DID effect,

which is of our interest. X is control variables, including logarithm of total assets (Asset), logarithm of

number of years since the firm was listed on the stock market plus 1 (Age), cash flow from selling

16

products and supplying labors divided by total assets (Cashflow), earnings before interests and tax to

total assets ratio (EBIT), fixed assets to total assets ratio (Tangibility), percentage of shares held by

largest shareholder (First), number of directors on board (Director), ratio of independent directors on

board to total directors (Independent), growth rate of sales (Growth), cash reserves to total assets ratio

(Cash) and whether is SOE (SOE) where SOE is 1 if firms belong to state-owned firms. ε is error term,

with standard errors clustered at firm level.

Following Vig (2013), we also include the industry-year fixed effect to control for the

industry-specific time-varying shocks because these shocks can be possibly correlated with short-term

loans dependence and bias our estimations. Besides, to avoid other potential endougeneity, we lag all

control variable for 1 period.

To further investigate the impact of Guideline 2007, we estimate its effect on firm leverage and

financing structure by employing the following regression:

ijtjtjitittiijt ijtXTreatPostTreatPostLeverage

'3211 (2)

Where the dependent variable is total debt to total assets ratio in the year t+1 (Leverage). The

right side of model 2 is the same with model 1 except control variables. In model 2, we control these

covariates that determine firms’ use of short-term loans, including logarithm of total assets (Asset),

total debt to total assets ratio (Leverage), logarithm of number of years since the firm was listed on the

stock market plus 1 (Age), earnings before interests and tax to total assets ratio (EBIT), other

receivables divided by total assets (Tunneling), fixed assets to total assets ratio (Tangibility), growth

rate of sales (Growth), cash reserves to total assets ratio (Cash) and whether is SOE (SOE) where SOE

is 1 if firms belong to state-owned firms.

Before making the DID estimation, we employ PSM methodology to ensure the similarity of

likelihood of being treated for treatment group and control group and thus the parallel trend of

outcome variable. The regression equation is as follows:

ijtijtXTreat '

0)Pr( (3)

Where the dependent variable is the dummy variable Treat, which is 1 if firms belong to

treatment group where firms are most reliant on short-term loans and 0 if firms belong to control

17

group. The control variables are the same as those in model 2 which are also independent variables for

the level of short-term loans dependence.

After matching these variables among these two groups, we drop off these observations failed to

match for pre-treatment period. Accordingly, we exclude firms in post-treatment period that do not

successfully match for all four years in pretreatment period.

3.4 Summary Statistics

Table 1 presents descriptive statistics for the variables described above. The mean (median) firm

in the sample has a SLoan of 19.89% (16.30%). The mean (median) other receivables ratio (Tunneling)

across all firm-years equals 4.929% (1.985%) . The mean value of leverage is 0.607 and its standard

deviation is 0.253. The average firm’s EBIT is 3.884% of total assets, as indicated by EBIT. The

above-mentioned variables have large standard deviation compared with their mean values. The big

discrepancy of the main dependent variables for our sample provide convenience for our empirical

research.

[Insert table 1 about here]

4. Empirical Results

4.1 Time trend of tunneling

Before getting into analyzing the effect of Guideline 2007 on tunneling, we firstly analyze the

time trend of tunneling to initially testify the parallel trend. Figure 1 separately plots the time series of

other receivables to assets ratio for both the high short-term loans reliance group (treatment group)

and low short-term loans reliance group (control group). We subtract value in 2007 of each group from

time series value for each year in each group. It can be seen from figure 1 that tunneling for treatment

group and control group move roughly together before the implementation of Guideline 2007. It can

be inferred from figure 1 that the two sets of firms follow similar trend in terms of tunneling before

regulatory change. This means that the crucial assumption of no pre-existing differences is met. After

2007, however, the two sets of firms exhibit a diverging trend in tunneling. Firms in treatment group

18

see a larger decline after 2007, compared with control group. This is consistent with our hypothesis

that rollover restriction leads to better monitoring and less tunneling, especially for firms in treatment

group.

[Insert figure 1 about here]

In next section, we will examine the time trend for tunneling using regression model to further

confirm parallel trend hypothesis.

4.2 Parallel trend and dynamic effect of Guideline 2007 on Tunneling

DID methodology assumes that, if the reform did not happen, the change of outcome variable

would be the same for both treatment group and control group. Apart from using figure to see the

parallel trend, we further introduce the following regression to examine whether outcome variable of

these two groups shares the similar pattern before the implementation of Guideline 2007.

ijtjtj

ijtitittiijt XTreatDTreatPostTunneling

3

3

*211

(4)

where the specification is the similar with equation (1) except the interaction term. We just

replace interaction term Post×Treat in equation (1) with interaction terms of τ-th year dummy Dτ with

treatment group dummy, where Dτ equals to 1 in the τ-th year after the reform and D-τ equals to 1 in

the τ-th year before the reform. We exclude the year 2003 (τ=-4), and therefore contrast the trends of

treatment group and control group in each year relative to that in 2003. And the coefficients β* is of

our interests. If parallel trend holds, the coefficients β* before treatment period (2004, 2005 and 2006

where τ equals to -3, -2, -1, respectively) should be insignificant. Column 1 in table 2 shows the result.

The β* estimates show no effects in the three year before the guideline was passed. And this means

that our sample meet the requirement of parallel trend.


We can also infer the dynamic effect of Guideline 2007. After the reform, the effect on tunneling

materialize rather quickly and keeps stable. The dynamic effect also provides evidence that the

introduction of Guideline 2007 functions in a sensible way.

19

4.3 The effect on Tunneling of controlling shareholders

This section explores the impact of Guideline 2007, which changes banks’ way of rollover, on

tunneling of controlling shareholders to testify its effect on bank monitoring.

Columns 2-4 in Table 2 report the corresponding results. Columns 2 and 3 show estimations with

and without industry-year fixed effect. Coefficients on Treat×Post are negative and significant at 1%

level. Compared with firms less reliant on short-term loans, those heavily dependent on short-term

loans financing see a larger decline of tunneling of controlling shareholders. This is consistent with

hypothesis 1. As Clause 10 of Guideline 2007 shifts banks’ rollover policy from ‘borrowing for

repaying’ to ‘repaying for borrowing’, bank monitoring capacity from shorter maturity loans is

enhanced and controlling shareholders in firms reliant heavily on short-term loans have to cut down

their expropriation to more extent. Changing way of rollover also matters for releasing banks’

monitoring potential.

The absolute value of coefficient of Treat×Post declines when we add industry-year fixed effect,

which may indicate that some time-varying industry factors indeed affect our results. Therefore, we

will include this fixed effect in all following estimations.

A possible concern is that firms in treatment group just increase their size to a larger extent after

2007 and thus makes the tunneling ratio decline more. Meanwhile, Guideline 2007 does not impact

block shareholder tunneling in a different way. To rule out this explanation, we replace the dependent

variable in model 1 with total amount of tunneling, LnTunnel, computed as the logarithm of other

receivables plus 1. Coefficient on Treat×Post is still negative and significant, which means that

compared with firms less dependent on short-term loans, amount of tunneling in firms heavily relying

on short-term loans decline more quickly by 37%, which indicates that the reform really impact the

tunneling.

4.4 Cross-sectional heterogeneity effect or DIDID effect on tunneling

In this section, we will examine whether there exists heterogeneous treatment effects in order to

20

further support our hypothesis. If Guideline 2007 really matters for shareholder tunneling, firms with

more expropriation problem will be impacted more. Besides, we hypothesize that Guideline 2007

functions through increasing bank managers’ incentive to collect information and review firms’

condition more carefully due to more difficulty in concealing NPLs. So this regulation affects firms

with less transparency. Firms with potential repayment problem will be rejected with a higher

probability after 2007. So this regulation will impact more on firms with lower repayment capacity.

Besides, because some firms have less other financing channels, they will be more likely to act at

banks’ will and be more affected by Guideline 2007.

Previous research has found that firms with larger controlling shareholders are expropriated more

(Jiang et al., 2010). Also, firms with lower EBIT and higher tunneling has lower repayment capacity,

these with less tangible assets are less transparent (Barth et al., 2001) and smaller firms have less

alternative financing ways (Leary, 2009). We can expect that the effect of Guideline 2007 on tunneling

will should be more pronounced in firms with more tunneling, firms with more controlling right by

controlling shareholders, firms with lower EBIT, firms with less fixed assets and smaller firms.

To examine this DIDID effect, we interact above-mentioned variables (their pretreatment mean

value) with treatment dummy Treat, regulatory change dummy Post and their interaction term,

respectively. We estimate the following specification:

ijtjtjitijtiitit

iiitiittiijt

TreatXZTreatPostTreatPost

ZTreatZPostZTreatPostTunnel

76

543211 (5)

As in model 1, the dependent variable is Tunnel, and we include treatment dummy Treat,

regulatory change dummy Post, their interaction variable and control variables X, which is the same as

in model 1. We also control firm fixed effect, year fixed effect, industry-year fixed effect as before.

We further introduce continuous variables Z, which proxy for the characteristics of interests, including

tunneling, controlling right, EBIT, fixed assets ratio and firm size. We compute Z as mean value of

corresponding variables in pre-treatment years (2003, 2004, 2005, 2006) for each firm except

controlling right, whose Z value is the average value in 2004, 2005 and 2006 because this variable has

many missing values in 2003. We then interact Z with treatment dummy Treat, regulatory change

21

dummy Post and their interaction term, respectively. The coefficient β7 is of our interest, which

capture DIDID effect. Apart from this, in DIDID specifications, we include interaction terms of time

dummy for every year with treatment dummy (Vig, 2013), which is an advantage of DIDID compared

with DID.


We report our results in table 3. The triple interaction terms are of our interests. In column 1,

coefficient on Treat×Post×Tunnel is negative and significant, meaning that compared with firms in

treatment group with less expropriation problem, firms in treatment group with more expropriation

problem will see a larger decline of controlling shareholder tunneling. Furthermore, the coefficient of

Treat×Post×Control in column 2 is negative and significant, indicating that for firms that more

affected by Guideline 2007, these with more controlling rights by controlling shareholders are more

affected and are accompanied by larger decline in tunneling.These results further support our

hypothesis that Guideline 2007 really helps constrain shareholder tunneling. Because more controlling

right are linked with higher tunneling as is shown by Jiang et al. (2010) and tunneling can lower firm

repayment capacity, banks will be more likely to reject their loans application. Shareholders in these

firms have to decrease tunneling to a larger extent.

In a similar vein, coefficient on Treat×Post×EBIT is positively significant. EBIT can also

represents firms’ future repayment capacity. The introduction of Guideline 2007 help banks detect

repayment problem more easily and focus more on firm repayment condition, so loans applications of

firms with lower EBIT can more likely to be rejected. And shareholders in these firms will decrease

their expropriation to enhance firms’ repayment capacity.

In column 4, coefficient on Treat×Post×Tangibility is positive and significant. Within the

treatment group that are affected by Guideline 2007, controlling shareholders in firms with less

tangible assets are more likely to cut down their tunneling. Since less tangible assets is linked with

higher level of firm opacity, Guideline 2007 can function more for these firms as it can enhance bank

managers’ incentive to collect information and monitor.

Finally, coefficient on Treat×Post×Asset in column 5 is positively significant. Smaller firms

22

often has less alternative ways of financing. As Guideline 2007 makes debt rollover for short-term

loans difficult and banks enhance their monitoring, these smaller firms will be more likely to act to

please bank managers.

4.5 The impact of Guideline 2007 on corporate leverage and debt structure

In this section, we will investigate the effect of Guideline 2007 on firm leverage by employing

regression 1. Column 1 in table 4 reports the result. Coefficient on interaction term Treat×Post is

negative (-4.331) and significant at 1% level, which indicates that firms reliant more on short-term

loans have to cut down their reliance on debt financing after the implementation of Guideline 2007.

This is consistent with hypothesis 2. Guideline 2007 brings about two opposite effects, financing

shock effect and governance shock effect. Although better governance brought in by Guideline 2007

can enlarge firms’ financing capacity, the financing shock effect dominates in determining capital

structure, leading to lower leverage for firms more reliant on short-term loans.

Apart from investigating the effect on total debt, we also survey the impact on debt structure.

Because Guideline 2007 aims at short-term loans rollover, it should firstly impact financing from

short-term loans. Columns 2 report results for short-term loans. Coefficient on interaction term Treat

×Post is negative and significant at 1% level, which means that compared with control group which

rely less on short-term loans as a source of financing, firms heavily relying on short-term loans will

cut down their lending of short-term loans to a more extent. this is consistent with hypothesis 2. The

impact is also economically significant. Firms heavily relying on short-term loans have to decrease

use of short-term loans more by 12.43%. This provides a direct evidence that Clause 10 of Guideline

2007 really works as a source of financing shock.


Since firms have to decrease the use of short-term loans financing after the implementation of

Guideline 2007, do they use more long-term loans? Columns 3 and 4 in table 2 show the results for

long-term loans and total bank loans, respectively. The dependent variables are long-term loans to

total assets ratio LLoan and total loans to total assets ratio TLoan. Coefficient on interaction term in

23

column 3 is positive (3.185) and significant at 1% level, which indicates that although firms more

dependent on short-term loans shrink their lending of short-term loans after 2007, they will increase

their lending of long-term loans by 3.185% to alleviate this negative effect. Column 4 reports the

result for total loans ratio. Coefficient on Treat×Post is -9.017, which is roughly the sum of

coefficients in columns 2 and 3, and is also significant. Firms in treatment group have to decrease

9.017% more of total bank loans. This indicates that although firms can substitute short-term loans

with long-term loans financing, the negative of Guideline 2007 on loans financing can not be fully

offset.

As bank loans financing decreases after 2007, does firms can offset this negative effect using

debt other than bank loans, including bonds and trade credit? Column 5 reports the result for other

debt, defined as total debt minus bank loans, divided by total assets. Coefficient on interaction term

Treat×Post is positive (5.350) and significant at 1% level, indicating that firms in treatment group can

increase their source of financing from other forms of debt after 2007 by 5.350%. Although firms in

treatment group have to cut down bank loans financing for a larger extent, they can offset this negative

effect by substituting bank loans with other forms of debt. However, because these treatment firms cut

down their bank loans by 9%, these firms can not fully offset the negative effect of Guideline 2007 on

debt financing, and thus their leverage will get down to a larger extent.

In all, Guideline 2007 can impact firm leverage firstly as a source of financing shock. Although

firms can offset the negative effect of Guideline 2007 on short-term loans by using more long-term

loans and non-loans debt, they can not prevent the decline of debt arising from financing shocks.

Market friction, including market segmentation, immature bond market and weak law enforcement,

can preclude substitution and thus leads to lower level of total debt.

4.6 The effect of Guideline 2007 on cash holdings, investment and profitability

Having established that Guideline 2007 enhances bank monitoring, we further analyze how this

reform impact other outcomes? We examine issues about cash holding, investment and profitability,

respectively. This will help us further confirm the effect of Guideline 2007 on bank monitoring. We

24

employ the DID specification similar with equation 1 except control variables. In this section, we

control Asset, Age, First, Director, Independent, Cashflow, Grow, Tangibility and SOE.

Column 1 in table 5 reports the result for cash holdings, where the independent variable is cash

holdings to assets ratio. There exists two opposite effects of Guideline 2007, including financing

shock effect and governance shock effect. On the one hand, firms in treatment group have to cut down

short-term loans and total loans more due to rollover restriction. In other words, this reform can lead

to higher liquidity risk due to more difficulty in rollover (Cheng and Milbradt, 2011). So these firms

have to hoard more cash to mitigate the adverse impact in its liquidity management process (Vig, 2013;

Harford et al., 2014), compared with firms in control group. On the other hand, however, because

Guideline 2007 enhances bank monitoring and decrease tunneling by shareholders in treatment group

to a larger extent, these firms have more resources to use freely and thus decrease their demand for

hoarding cash. The interaction term in column 1 is negative and significant, indicating the second

effect (governance shock effect) dominates. This further confirm hypothesis and indicates that

Guideline 2007 enhances bank monitoring and spares more resources for firms by constraining

shareholder expropriation, so firms more dependent on short-term loans only need to conserve less

cash.


Column 2 reports the result for investment. The dependent variable is defined as investment

expenditures on new project divided by total assets, and multiply by 100. In a similar vein, there exists

two opposite effects. Guideline 2007 can decrease debt financing and constrains controlling

shareholders’ resources diversion at the same time. Coefficient on interaction term is insignificant.

This means that although the reform decreases treatment firms’ financing to a more extent, it also help

protect firm resources from being tunneled. So, the investment in treatment group is not affected.

A potential concern is that our dependent variables are ratio variable and it is the change of total

asset, not cash holding or investments, drives our results. So we substitute dependent variables with

LnCash, the logarithm of cash reserves plus 1, and LnInvest, the logarithm of investment expenditures

on new project plus 1. The direction and significance of interaction terms do not change. This can

25

alleviate the above-mentioned concern.

Finally, we examine how the reform affects firm profitability. Column 5 reports the results where

dependent variable is EBIT. Coefficient on Treat×Post is positive and significant. Compared with

firms less reliant on short-term loans, firms dependent more on short-term loans enjoy an larger

increase in profitability. This is further consistent with the notion that Guideline 2007 leads to better

bank monitoring. Although Guideline 2007 limit firms’ debt financing and may decrease firm

performance, it brings about better corporate governance, which dominates in determining profitability.

It also implies a higher resources allocation efficiency after 2007.

4.7 Alternative explanations

Up to now we find that Guideline 2007 brings about lower level of controlling shareholder

expropriation, and attribute these results as Guideline 2007 leads to better bank monitoring. And

short-term loans and total debt financing decrease at the same time. We should caution that there exists

some alternative explanations.

4.7.1 Other corporate governance shocks

Our results can also be driven by other corporate governance shocks. Our results may arise from

the fact that firms in treatment group firstly have better corporate governance for reasons other than

the implementation of Guideline 2007. And then these firms have better access to stock and bond

market financing and cut down their reliance on bank loans financing. For example, during 2006-2007,

China has implemented split share structure reform, which promote corporate governance as well as

stock market financing. If so, we should observe that treatment group can get more fund from the

market and less from commercial bank. Also, these firms should have more access to total funds as a

whole from financial market and commercial banks due to better governance. In contrast, if our results

are driven solely by Guideline 2007, we should see a decline of total funds as a whole from different

sources of financing since Guideline 2007 firstly negatively impact firm’s financing and this financing

shock effect dominates over governance effect in determining firm financing.


26

Therefore, we estimate the effect of Guideline 2007 on firms’ sources of financing. We replace

the dependent variable in model 2 with Loan Issue, Equity Issue, Bond Issue and Total Issue, which

represent fund INflow from bank loans, equity issuance, bond issuance and these three channels,

respectively. Loan issue is computed as proceeds from borrowings divided by total assets. Similarly,

Equity issue and Bond issue are computed as proceeds from equity issuance divided by total assets

and proceeds from bond issuance divided by total assets. Total issue is the sum of Loan issue, Equity

issue and Bond issue. Table 6 reports the results. Coefficient on interaction term in column 1 is -10.89

and significant, which indicates that firms in treatment group have 10.89% less fund from bank loans

compared with control group. Interaction term in column 2 is 0.725 and significant at 10%, indicating

that firms in treatment group only slightly increase their fund from equity financing by 0.72%,

compared with control group. Coefficient on interaction term in column 3 is insignificant, which

means that bond issuance for treatment group and control group is affected indifferently. Finally, we

testify how firms’ total financing is affected. Interaction term in table 4 is negative and significant,

which means that total financing for firms in treatment group decrease more by 10.36% compared

with these in control group. This is inconsistent with other governance shocks explanation. Under that

explanation, corporate governance for firms in treatment group improve more and these firms should

have more funds inflow compared with firms in control group. And it is the Guideline 2007 that drives

our results. After borrowing for repaying is prohibited, firms reliant on short-term borrowing have less

bank loans financing and have to resort to other financing, such as equity issuance. However, because

there exists market frictions, firms can not fully compensate bank loans with bond and equity

issuance.

4.7.2 Does Guideline 2007 only have financing shock effect and not impact monitoring?

The second possible alternative explanation is that Guideline 2007 only restricts short-term loans

financing, so firms reliant on short-term loans have to cut down their reliance on bank loans. And in

China, bank loans are an important source of funds for tunneling (Bailey et al., 2011). Block

shareholders in these treatment group firms have less fund to tunnel and decrease expropriation. In

27

other words, level of tunneling goes down passively for the decrease of short-term loans, not a result

of enhanced bank monitoring. There only exist financing shock effect and no governance shock effect

from Guideline 2007.


The implicit hypothesis of this explanation is that more short-term loans lead to more tunneling.

Luckily, our constrained sample, or PSM sample, can help rule out this explanation. We return to our

baseline regression and replace the interaction term with short-term loans ratio SLoan to see whether

bank loans lead to more tunneling in our sample. The coefficients of SLoan are of our interests.

Columns 1-3 in table 6 report the results for whole samples, samples before treatment and samples

after treatment. Coefficients on SLoan are insignificant, which means short-term loans can not lead to

more tunneling in our after-matching sample, and we can rule out this explanation.

4.7.3 Effect of new Property law

Another explanation comes from new property law. In 2007, China has imposed a new property

law and clarifies which intangible assets, like accounts receivables, can be used to guarantee

long-term loans while intangible assets are rarely used as collateral before 2007. Because treatment

group with more short-term loans dependence always have more intangible assets, these firms can rely

more on their intangible assets and get more long-term loans after 2007. At the same time, these firms

can rely less on short-term loans after 2007. However, bank loans of treatment firms should be more if

this explanation holds. The enlarging capacity of bank financing for treatment group under this

explanation contradict with the declining amount of total loans for these firms.


Furthermore, we construct a new treatment dummy Treat1. We compute the average value of

pretreatment fixed assets to total assets ratio for each firms that belongs to our final sample, and

Treat1 is 1 if firms’ average value of fixed assets ratio is higher than median of these values and 0 if

average value is in lower than the median. Then we replace Treat1 with Treat in model 1. Coefficient

of Treat1×Post in column 3 is insignificant, which means that firms with more intangible assets will

28

not shift their demand from short-term loans to long-term loans, which is inconsistent with the

alternative explanation. Moreover, we further include both Treat1×Post and Treat×Post into our

model. Coefficient on Treat1×Post is insignificant, and that of Treat×Post is still significant and its

magnitude is not affected. Therefore, the property law explanation can not drive our results.

5. Conclusions

This paper analyzes how the way of rollover for short-term loans affects bank monitoring by

investigating the effect of Guideline 2007 on shareholder tunneling. Results show that after 2007,

firms reliant more on short-term loans are expropriated less by controlling shareholders. From

heterogeneous effect, the impact of Guideline 2007 on tunneling is more pronounced in firms with

more tunneling, more controlling right, lower EBIT, less tangibility and lower size. Furthermore, after

the reform, firms dependent more on short-term loans have to cut down their short-term loans and

leverage to a more extent. Finally, Guideline 2007 makes firms more dependent on short-term loans

conserve less cash while have better performance.

This paper uncovers another important factor determining monitoring role of short-term loans.

While previous research find that increasing amount of short-term loans or shortening debt maturity

can enhance bank monitoring, we find that changing way of rollover of short-term loans can also

increase bank monitoring capacity and release their governance potential. This increases the evidence

of monitoring role of short-term loans.

Our results also have some implications for banking industry reform. Abundant of research

investigate this issue (Datta et al.,1995; Bertrand et al.,2007; Tsai et al.,2014). In transitional

economies with weak institutions, bank loans are a main source of financing and commercial banks

play an important part in corporate governance. However, agency problem and other friction in

banking industry can distort monitoring role of banks (Firth et al., 2008; Qian and Yeung, 2015).

Through changing banks’ loans origination behavior and loans contracting forcibly, banking reform

propelled by regulatory agency can improve bank monitoring in corporate governance. Therefore, our

29

work indicates that in transitional economies, reformer should focus on which factors, cultivated in

their institutions, inhibit banks from monitoring and help release bank monitoring potential through

proper reform about bank practice. Our works also indicates that we should not only emphasize

amount of financial instruments (short-term loans) itself, but also focus on core factors of determining

the monitoring role of these instruments or mechanism behind these instruments.

30

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33

Figure 1 Trend of Tunneling for Treatment Group and Control Group

The figure plots time trend of average other receivable ratio for treatment group and control group in each year, where

we subtract value in 2007 from the average of average value for two groups in each year. We divide firms into quartiles

based on pretreatment value of short-term loans ratio and plot the time series for highest short-term loans ratio quartile

and lowest short-term loans ratio quartile. Before plotting, we drop samples using matching method.

34

Table 1 Summary Statistics for Main Variables

This table reports summary statistics for main variables. Short-term loans refers to short-term loans to total assets ratio,

Tunneling refers to other receivables divided by total assets. Post is an indicator variable that takes on value of 1 in

years that Guideline 2007 is in place (2007, 2008, 2009 and 2010) and 0 otherwise (years before 2007). Treat is an

indicator variable that takes on value of 1 if firm belongs to treatment group (high short-term loans dependence) and 0

if firm belongs to control group (low short-term loans dependence). We also report the results for logarithm of total

assets (Asset), total debt to total assets ratio (Leverage), logarithm of number of years since the firm was listed on the

stock market plus 1 (Age), earnings before interests and tax to total assets ratio (EBIT), fixed assets to total assets ratio

(Tangibility), growth rate of sales (Growth), cash reserves to total assets ratio (Cash), whether is SOE (SOE) where

SOE is 1 if firms belong to state-owned firms, percentage of shares held by largest shareholder (First), number of

directors on board (Director) and ratio of independent directors on board to total directors (Independent).

Variables N Mean SD Median 25 quantile 75 quantile

Short term loan 2,957 19.894 17.126 16.302 4.115 32.895

Tunneling 2,957 4.929 7.143 1.985 0.748 5.670

Post 2,957 0.493 0.500 0.000 0.000 1.000

Treat 2,957 0.567 0.496 1.000 0.000 1.000

Asset 2,957 21.468 1.255 21.403 20.701 22.208

Leverage 2,957 0.607 0.253 0.587 0.464 0.699

Age 2,957 2.293 0.404 2.398 2.079 2.565

EBIT 2,957 3.884 8.271 4.439 2.251 7.322

Tangibility 2,957 0.361 0.229 0.329 0.178 0.532

Growth 2,957 0.285 0.864 0.152 -0.025 0.352

Cash 2,957 0.143 0.113 0.116 0.064 0.193

SOE 2,957 0.677 0.468 1.000 0.000 1.000

Cash flow 2,957 0.046 0.086 0.046 0.002 0.095

First 2,957 37.108 16.235 33.811 24.700 49.770

Board 2,917 9.288 2.066 9.000 8.000 10.000

Independent 2,917 0.354 0.054 0.333 0.333 0.375

35

Table 2 The Effect of Guideline 2007 on Tunneling

The table reports the effect of Guideline 2007 on tunneling. Dependents variables are Tunnel in columns 1-3 and

LnTunnel in column 4, computed as other receivables divided by total assets and logarithm of other receivables plus 1,

respectively. Post is an indicator variable that takes on value of 1 in years that Guideline 2007 is in place (2007, 2008,

2009 and 2010) and 0 otherwise (years before 2007). Treat is an indicator variable that takes on value of 1 if firm

belongs to treatment group (high short-term loans dependence) and 0 if firm belongs to control group (low short-term

loans dependence). D(Year=t) is a dummy variable that takes the value of 1 as year is t and 0 otherwise. Control

variables include logarithm of total assets (Asset), logarithm of number of years since the firm was listed on the stock

market plus 1 (Age), cash flow from selling products and supplying labors divided by total assets (Cashflow), earnings

before interests and tax to total assets ratio (EBIT), fixed assets to total assets ratio (Tangibility), percentage of shares

held by largest shareholder (First), number of directors on board (Director), ratio of independent directors on board to

total directors (Independent), growth rate of sales (Growth), cash reserves to total assets ratio (Cash) and whether is

SOE (SOE) where SOE is 1 if firms belong to state-owned firms. We control firm fixed effect, year fixed effect,

industry fixed effect. We also control industry-year fixed effect in column 1, 3 and 4 (Vig, 2013). All independent

variables and control variables are lagged for 1 year. Standard errors clustered at firm level.

Models

(1) (2) (3) (4)

Variables Tunnel Tunnel Tunnel LnTunnel

Treat×Post -2.168*** -1.890*** -0.370***

(-4.23) (-3.65) (-3.01)

Treat×D(Year=2004) 0.921

(1.44)

Treat×D(Year=2005) -0.377

(-0.40)

Treat×D(Year=2006) -0.975

(-1.21)

Treat×D(Year=2007) -2.051**

(-2.33)

Treat×D(Year=2008) -1.748*

(-1.91)

Treat×D(Year=2009) -2.183**

(-2.29)

Treat×D(Year=2010) -2.042**

(-2.31)

Asset 1.437*** 1.201*** 1.470*** 1.208***

(4.58) (4.23) (4.72) (7.86)

Age 2.162 2.547* 2.250* 0.611**

(1.59) (1.84) (1.68) (2.03)

Cashflow -3.231* -2.515 -3.319* 0.0704

(-1.89) (-1.50) (-1.93) (0.12)

EBIT -0.0944*** -0.0941*** -0.0947*** -0.00860

(-4.39) (-4.39) (-4.42) (-1.32)

Tangibility -5.213*** -5.258*** -5.244*** -0.149

(-4.44) (-4.41) (-4.46) (-0.45)

First -0.0435** -0.0508** -0.0423** -0.00608

(-2.39) (-2.56) (-2.31) (-1.53)

Director -1.296 -1.208 -1.324 -0.0801

(-1.24) (-1.20) (-1.28) (-0.31)

Independent -5.793* -5.570* -5.855* -0.542

(-1.87) (-1.83) (-1.90) (-0.65)

36

Growth -0.226 -0.262* -0.239 0.0187

(-1.46) (-1.76) (-1.55) (0.57)

Cash -4.555*** -4.179** -4.360** -0.626

(-2.61) (-2.45) (-2.49) (-1.13)

SOE -0.491 -0.297 -0.455 -0.0947

(-0.62) (-0.36) (-0.58) (-0.56)

Constant -7.538 -12.78* -8.402 -6.664**

(-0.98) (-1.85) (-1.11) (-2.09)

Firm fixed effect yes yes yes yes

Year fixed effect yes yes yes yes

Industry-Year

fixed effect yes no yes yes

R2 0.294 0.213 0.292 0.341

N 2,896 2,896 2,896 2,896

37

Table 3 Results for DIDID Effect

The table reports the DIDID effect of Guideline 2007 on tunneling. Dependents variable is Tunnel, computed as other

receivables divided by total assets. Post is an indicator variable that takes on value of 1 in years that Guideline 2007 is

in place (2007, 2008, 2009 and 2010) and 0 otherwise (years before 2007). Treat is an indicator variable that takes on

value of 1 if firm belongs to treatment group (high short-term loans dependence) and 0 if firm belongs to control group

(low short-term loans dependence). The third terms of triple interaction terms are mean value of corresponding

variables in pre-treatment years (2003, 2004, 2005, 2006) for each firm except controlling right (Control), which we

compute as average value in 2004, 2005 and 2006, because this variable has many missing values in 2003.Control

variables include logarithm of total assets (Asset), logarithm of number of years since the firm was listed on the stock

market plus 1 (Age), cash flow from selling products and supplying labors divided by total assets (Cashflow), earnings

before interests and tax to total assets ratio (EBIT), fixed assets to total assets ratio (Tangibility), percentage of shares

held by largest shareholder (First), number of directors on board (Director), ratio of independent directors on board to

total directors (Independent), growth rate of sales (Growth), cash reserves to total assets ratio (Cash) and whether is

SOE (SOE) where SOE is 1 if firms belong to state-owned firms. We control firm fixed effect, year fixed effect,

industry fixed effect. We also control industry-year fixed effect. In DIDID specifications, we include interaction terms

of time dummy for every year with treatment dummy (Year Dummies×Treat), which is an advantage of DIDID

compared with DID (Vig, 2013). All independent variables and control variables are lagged for 1 year. Standard errors

clustered at firm level.

Models

(1) (2) (3) (4) (5)

Variables Tunnel Tunnel Tunnel Tunnel Tunnel

Treat×Post×Tunnel -0.704***

(-12.90)

Treat×Post×Control -0.0998**

(-2.49)

Treat×Post×Tangibility 9.054***

(4.21)

Treat×Post×EBIT 43.60***

(5.01)

Treat×Post×Asset 1.422**

(2.18)

Asset 0.582** 1.492*** 1.474*** 0.722** 1.342***

(2.15) (4.80) (4.76) (2.29) (4.27)

Age 0.463 3.386** 2.286 2.015 2.085

(0.44) (2.51) (1.65) (1.60) (1.56)

Cashflow -2.378 -2.959* -2.953* -3.791** -3.266*

(-1.54) (-1.81) (-1.77) (-2.22) (-1.90)

EBIT -0.0796*** -0.103*** -0.0982*** -0.0664*** -0.0913***

(-3.82) (-4.51) (-4.64) (-3.00) (-4.22)

Tangibility -2.572** -5.083*** -3.418*** -4.827*** -5.351***

(-2.50) (-4.28) (-3.05) (-4.22) (-4.57)

First -0.0300** -0.0544*** -0.0472*** -0.0271 -0.0393**

(-2.15) (-2.85) (-2.69) (-1.61) (-2.18)

Director -0.219 -1.185 -1.131 -1.090 -1.035

(-0.27) (-1.12) (-1.09) (-1.07) (-1.01)

Independent -2.012 -5.476* -5.331* -4.287 -5.162*

(-0.86) (-1.72) (-1.77) (-1.50) (-1.72)

Growth -0.0881 -0.256* -0.215 -0.121 -0.187

(-0.60) (-1.71) (-1.37) (-0.80) (-1.23)

Cash -4.371*** -4.254** -4.521*** -4.644*** -4.614***

38

(-2.61) (-2.47) (-2.63) (-2.65) (-2.60)

SOE -0.452 -0.495 -0.374 -0.542 -0.522

(-0.74) (-0.64) (-0.49) (-0.72) (-0.68)

Constant 3.092 -12.43 -10.24 3.887 -6.357

(0.51) (-1.62) (-1.40) (0.53) (-0.84)

Firm fixed effect yes yes yes yes yes

Year fixed effect yes yes yes yes yes

Industry-Year

fixed effect yes yes yes yes yes

Year Dummies×Treat yes yes yes yes yes

R2 0.405 0.299 0.307 0.320 0.301

N 2,896 2,775 2,896 2,896 2,896

39

Table 4 The Impact of Guideline 2007 on Bank Loans and Total debt

This table reports the effect of Guideline 2007 on bank loans and total debt. The dependent variables include total debt

to total assets ratio (Leverage), short-term loans to total assets ratio (SLoan), long-term loans to total assets ratio

(LLoan), short-term loans plus long-term loans to total assets ratio (TLoan) and total debt minus bank loans to total

assets ratio (Other Debt). Post is an indicator variable that takes on value of 1 in years that Guideline 2007 is in place

(2007, 2008, 2009 and 2010) and 0 otherwise (years before 2007). Treat is an indicator variable that takes on value of 1

if firm belongs to treatment group (high short-term loans dependence) and 0 if firm belongs to control group (low

short-term loans dependence). Coefficient on interaction term Post×Treat is the DID effect, which is of our interest.

Control variables include logarithm of total assets (Asset), total debt to total assets ratio (Leverage), logarithm of

number of years since the firm was listed on the stock market plus 1 (Age), earnings before interests and tax to total

assets ratio (EBIT), other receivables divided by total assets (Tunnel), fixed assets to total assets ratio (Tangibility),

growth rate of sales (Growth), cash reserves to total assets ratio (Cash) and whether is SOE (SOE) where SOE is 1 if

firms belong to state-owned firms. We control firm fixed effect, year fixed year and industry fixed year. We also control

industry-year fixed effect (Vig, 2013). All independent variables and control variables are lagged for 1 year. Standard

errors clustered at firm level.

Models

(1) (2) (3) (4) (5)

Variables Leverage SLoan LLoan TLoan Other Debt

Treat×Post -4.331*** -12.43*** 3.185*** -9.017*** 5.350***

(-4.70) (-12.50) (3.84) (-8.02) (5.37)

Asset 3.501*** 3.303*** 2.092*** 5.394*** -0.758

(5.33) (6.57) (5.24) (8.83) (-1.18)

Leverage 27.21*** 6.346*** 3.936*** 10.99*** 18.78***

(12.83) (2.72) (3.33) (4.52) (9.41)

Age -0.262 -2.116 -1.703 -4.221 1.329

(-0.10) (-0.73) (-0.92) (-1.32) (0.46)

EBIT -0.127*** -0.187*** 0.0266 -0.145** -0.0663**

(-4.40) (-3.84) (1.21) (-2.52) (-1.98)

Tunnel -0.0533 0.0886 -0.167*** -0.0685 0.0652

(-1.08) (1.59) (-4.83) (-1.13) (1.21)

Tangibility -7.176*** 5.009** -1.053 3.493 -7.160***

(-3.27) (2.06) (-0.57) (1.30) (-3.29)

Growth -0.655*** -0.750*** -0.108 -0.925*** -0.00358

(-3.03) (-3.33) (-0.66) (-3.80) (-0.01)

Cash -14.22*** 0.629 -4.366** -4.914 -11.49***

(-4.04) (0.21) (-2.02) (-1.45) (-3.41)

SOE -1.076 -0.665 -0.347 -0.987 0.549

(-0.98) (-0.40) (-0.45) (-0.55) (0.39)

Constant -23.49 -44.96*** -37.30*** -81.49*** 34.30**

(-1.56) (-3.38) (-4.04) (-5.47) (2.22)



Industry-Year


R2 0.337 0.362 0.162 0.299 0.315

N 2935 2934 2934 2935 2934

40

Table 5 The Effect on Cash Holdings, Investment and EBIT

The table reports the effect of Guideline 2007 on cash holdings, investment and EBIT. Dependents variables include

cash holdings to total assets ratio (Cash), investment expenditure to total assets ratio (Invest), logarithm of cash

holdings plus 1 (LnCash), logarithm of investment expenditure plus 1 (LnInvest) and earnings before interests and tax

to total assets ratio (EBIT). Post is an indicator variable that takes on value of 1 in years that Guideline 2007 is in place

(2007, 2008, 2009 and 2010) and 0 otherwise (years before 2007). Treat is an indicator variable that takes on value of 1

if firm belongs to treatment group (high short-term loans dependence) and 0 if firm belongs to control group (low

short-term loans dependence). Control variables include logarithm of total assets (Asset), logarithm of number of years

since the firm was listed on the stock market plus 1 (Age), cash flow from selling products and supplying labors

divided by total assets (Cashflow), earnings before interests and tax to total assets ratio (EBIT), fixed assets to total

assets ratio (Tangibility), percentage of shares held by largest shareholder (First), number of directors on board

(Director), ratio of independent directors on board to total directors (Independent), growth rate of sales (Growth), cash

reserves to total assets ratio (Cash) and whether is SOE (SOE) where SOE is 1 if firms belong to state-owned firms. We

control firm fixed effect, year fixed effect, industry fixed effect. We also control industry-year fixed effect in column 1,

3 and 4 (Vig, 2013). All independent variables and control variables are lagged for 1 year. Standard errors clustered at

firm level. In column 3 and 4, there exists 1 and 28 missing sample due to the negative value of cash holdings and

investment expenditure. In column 5, there exists 388 missing samples for EBIT.

Models

(1) (2) (3) (4) (5)

Variables Cash Invest Lncash LnInvest EBIT

Treat×Post -2.003** 0.408 -0.193** 0.131 1.776***

(-2.06) (0.93) (-2.54) (0.98) (2.62)

Asset -3.516*** -0.477** 0.495*** 0.819*** -2.858***

(-4.95) (-2.34) (6.89) (7.10) (-5.32)

Age 1.093 -3.144** 0.418* -0.0394 1.507

(0.46) (-2.38) (1.66) (-0.12) (0.74)

First 0.0588** 0.0267** 0.0104*** 0.0104** 0.128***

(2.18) (1.97) (4.30) (2.36) (5.13)

Director 2.565 1.672* 0.648*** 0.530** 2.993*

(1.61) (1.82) (3.76) (1.97) (1.85)

Independent 0.550 6.821*** 0.884 1.470* 5.379

(0.13) (2.94) (1.60) (1.89) (1.04)

Cashflow 12.57*** 2.659** 1.514*** 1.192*** 9.704***

(4.83) (2.55) (4.90) (2.68) (3.58)

Grow -0.0303 0.0146 0.00909 0.0214 0.922***

(-0.13) (0.16) (0.38) (0.60) (4.66)

Tangibility -8.715*** -3.417*** -0.473** -0.288 3.409*

(-4.56) (-3.27) (-2.39) (-0.85) (1.90)

SOE 1.606 -0.818 -0.0914 -0.497** -3.157***

(1.58) (-1.38) (-0.64) (-2.52) (-2.69)

Constant 75.81*** 17.09*** 5.631*** -0.798 43.43***

(4.96) (3.02) (3.35) (-0.30) (3.69)



Industry-Year


R2 0.185 0.119 0.334 0.206 0.229

N 3,271 3,271 3,270 3,243 2,893

42

Table 6 Results on Firm Financing

This table reports the effect of Guideline 2007 on firms’ source of financing. The dependent variables include Loan

issue, Equity issue, Bond issue and Total issue. Loan issue is computed as proceeds from borrowings divided by total

assets. Similarly, Equity issue and Bond issue are computed as proceeds from equity issuance divided by total assets

and proceeds from bond issuance divided by total assets. Total issue is the sum of Loan issue, Equity issue and Bond

issue.. Post is an indicator variable that takes on value of 1 in years that Guideline 2007 is in place (2007, 2008, 2009

and 2010) and 0 otherwise (years before 2007). Treat is an indicator variable that takes on value of 1 if firm belongs to

treatment group (high short-term loans dependence) and 0 if firm belongs to control group (low short-term loans

dependence). Coefficient on interaction term Post×Treat is the DID effect, which is of our interest. Control variables

include logarithm of total assets (Asset), total debt to total assets ratio (Leverage), logarithm of number of years since

the firm was listed on the stock market plus 1 (Age), earnings before interests and tax to total assets ratio (EBIT), other

receivables divided by total assets (Tunnel), fixed assets to total assets ratio (Tangibility), growth rate of sales (Growth),

cash reserves to total assets ratio (Cash) and whether is SOE (SOE) where SOE is 1 if firms belong to state-owned

firms. The measure unit of EBIT is 10%. We control firm fixed effect, year fixed year and industry fixed year. We also

control industry-year fixed effect (Vig, 2013). All independent variables and control variables are lagged for 1 year.

Standard errors clustered at firm level.

Models

(1) (2) (3) (4)

Variables Loan issue Equity issue Bond issue Total issue

Treat×Post -10.89*** 0.725* -0.184 -10.36***

(-7.46) (1.90) (-1.38) (-6.87)

Asset 2.755*** 0.0692 0.199*** 3.036***

(3.29) (0.34) (2.66) (3.44)

Leverage 2.420 0.0589 -0.287* 2.273

(0.88) (0.08) (-1.81) (0.78)

Age -7.525* -0.655 0.624 -7.546*

(-1.71) (-0.68) (1.38) (-1.70)

EBIT 0.185 0.387*** 0.00631 0.567

(0.37) (3.04) (0.21) (1.11)

Tunnel -0.274*** -0.0235 -0.000246 -0.299***

(-3.92) (-1.15) (-0.06) (-4.12)

Tangibility -3.695 0.0563 0.695*** -2.993

(-1.26) (0.06) (2.65) (-0.96)

Grow -0.0946 0.143 -0.0653*** -0.0158

(-0.28) (1.06) (-2.83) (-0.04)

Cash -2.992 -3.484** -0.260 -6.796

(-0.66) (-2.17) (-0.70) (-1.41)

SOE -0.749 -0.250 -0.0695 -1.083

(-0.44) (-0.44) (-0.55) (-0.60)

Constant -23.96 -1.342 -5.487*** -31.03

(-1.21) (-0.30) (-3.21) (-1.53)

Firm fixed effect yes yes yes yes

Year fixed effect yes yes yes yes

Industry-Year

fixed effect yes yes yes yes

R2 0.163 0.099 0.164 0.143

N 2,934 2,933 2,934 2,933

43

Table 7 The Effect of Short-term Loans on Tunneling

The table reports the effect of short-term loans on tunneling. Dependents variable is Tunnel, computed as other

receivables divided by total assets. Columns 1-3 report the results for whole samples, samples before treatment and

samples after treatment. Sloan is short-term loans to total assets ratio. Control variables include logarithm of total assets

(Asset), logarithm of number of years since the firm was listed on the stock market plus 1 (Age), cash flow from selling

products and supplying labors divided by total assets (Cashflow), earnings before interests and tax to total assets ratio

(EBIT), fixed assets to total assets ratio (Tangibility), percentage of shares held by largest shareholder (First), number

of directors on board (Director), ratio of independent directors on board to total directors (Independent), growth rate of

sales (Growth), cash reserves to total assets ratio (Cash) and whether is SOE (SOE) where SOE is 1 if firms belong to

state-owned firms. We control firm fixed effect, year fixed effect, industry fixed effect. We also control industry-year

fixed effect (Vig, 2013). All independent variables and control variables are lagged for 1 year. Standard errors clustered

at firm level.

Models

(1) (2) (3)

Variables Tunnel Tunnel Tunnel

SLoan 0.0272 -0.0205 0.00206

(1.61) (-0.51) (0.18)

Asset 1.657*** 2.951*** 0.503*

(5.34) (3.36) (1.77)

Age 2.487* 3.512* 0.0686

(1.85) (1.96) (0.02)

Cashflow -3.241* -0.976 2.017

(-1.88) (-0.29) (1.53)

EBIT -0.0926*** -0.0952** -0.0551**

(-4.34) (-2.24) (-2.13)

Tangibility -5.526*** -2.615 -0.215

(-4.59) (-1.22) (-0.16)

First -0.0403** -0.0210 -0.0106

(-2.20) (-0.86) (-0.60)

Director -1.161 1.124 -1.934*

(-1.14) (0.79) (-1.70)

Independent -5.503* -1.550 -2.414

(-1.77) (-0.34) (-0.80)

Growth -0.245 -0.220 0.0362

(-1.58) (-0.56) (0.28)

Cash -3.632** -5.493 -2.285

(-2.06) (-1.29) (-1.37)

SOE -0.471 -0.563 -0.163

(-0.60) (-0.51) (-0.19)

Constant -13.87* -59.97*** 2.785

(-1.87) (-3.27) (0.26)

Firm fixed effect yes yes yes

Year fixed effect yes yes yes

Industry-Year

fixed effect yes yes yes

R2 0.288 0.180 0.130

N 2,896 1,479 1,417

44

Table 8 Confounding Effects of New Property Law

This table reports the effect of new property law on short-term loans financing. The dependent variables is short-term

loans to total assets ratio (SLoan). Post is an indicator variable that takes on value of 1 in years that Guideline 2007 is

in place (2007, 2008, 2009 and 2010) and 0 otherwise (years before 2007). Treat is an indicator variable that takes on

value of 1 if firm belongs to treatment group (high short-term loans dependence) and 0 if firm belongs to control group

(low short-term loans dependence).We compute the average value of pretreatment fixed assets to total assets ratio for

each firms that belongs to our final sample, and Treat1 is 1 if firms’ average value of fixed assets ratio is higher than

median of these values and 0 if average value is in lower than the median. Control variables include logarithm of total

assets (Asset), total debt to total assets ratio (Leverage), logarithm of number of years since the firm was listed on the

stock market plus 1 (Age), earnings before interests and tax to total assets ratio (EBIT), other receivables divided by

total assets (Tunnel), fixed assets to total assets ratio (Tangibility), growth rate of sales (Growth), cash reserves to total

assets ratio (Cash) and whether is SOE (SOE) where SOE is 1 if firms belong to state-owned firms. We control firm

fixed effect, year fixed year and industry fixed year. We also control industry-year fixed effect (Vig, 2013). All

independent variables and control variables are lagged for 1 year. Standard errors clustered at firm level.

Models

(1) (2)

Variables SLoan SLoan

Treat×Post -12.40***

(-12.46)

Treat1×Post 1.434 0.574

(1.07) (0.48)

Asset 4.796*** 3.332***

(8.74) (6.65)

Leverage 5.787** 6.349***

(2.36) (2.72)

Age -0.801 -2.195

(-0.23) (-0.75)

EBIT -0.210*** -0.187***

(-4.24) (-3.84)

Tunnel 0.148** 0.0882

(2.43) (1.58)

Tangibility 6.544** 5.447**

(2.33) (2.15)

Grow -0.934*** -0.747***

(-3.92) (-3.32)

Cash 5.479 0.699

(1.63) (0.23)

SOE -0.951 -0.669

(-0.53) (-0.40)

Constant -79.56*** -45.68***

(-5.38) (-3.45)

Firm fixed effect yes yes

Year fixed effect yes yes

Industry-Year

fixed effect yes yes

R2 0.294 0.362

N 2,934 2,934

from ‘borrowing for repaying’ to ‘repaying for...

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