Bank competition and leverage adjustments* Fuxiu Jiang School of Business Renmin University of China 59 Zhongguancun Street, Haidian District Beijing, China 100872 Email: [email protected] Zhan Jiang Shanghai Advanced Institute of Finance Shanghai Jiaotong University 211 West Huaihai Road, Datong Plaza Shanghai, China 200030 Email: [email protected] Tel: 86–135–85967060 Jicheng Huang School of Business Renmin University of China 59 Zhongguancun Street, Haidian District Beijing, China 100872 Email: [email protected] Kenneth A. Kim† School of Business Renmin University of China 59 Zhongguancun Street, Haidian District Beijing, China 100872 Email: [email protected]

December 2012

*

We thank Vidhan Goyal, Ning Zhu, Xiaodong Zhu, and seminar participants at Shanghai Advanced Institute of Finance (SAIF), and the University of International Business and Economics (Beijing) for discussions and comments on earlier versions of our paper. Yao Liu provided superb research assistance. The usual disclaimer applies. †

Corresponding author.

Bank competition and leverage adjustments

Abstract We propose and test a ―supply-side‖ factor, namely, bank competition, to see if it affects firms’ capital structure adjustment speed. When the banking environment is more competitive, under-levered firms should find it easier to obtain bank debt and move toward their target leverage faster. To empirically test our hypothesis, we study a market (i.e., China) where bank debt is the most important source of external finance. Bank competition in China also varies widely across time and provinces. Consistent with our hypothesis, we find when bank competition is high, under-levered firms move toward their target leverage faster. Further, we find that even small firms, non-state-owned firms, and firms in provinces with many other firms, are able to enjoy faster leverage adjustments when bank competition is high. Keywords: Bank competition; leverage adjustment; China JEL classifications: G32; G21; G10

1.

Introduction Recent research establishes firms have target capital structures, but they move toward

their targets slowly (e.g., Leary and Roberts (2005), Flannery and Rangan (2006), Huang and Ritter (2009), and Frank and Goyal (2009)).

More recently, therefore, the literature tries to

identify determinants of leverage adjustment speed.

As stated by Huang and Ritter (2009), this

is ―the most important issue in capital structure research today.‖

Several factors have been

proposed and found to be important determinants of leverage adjustment costs and speed, such as equity issuance costs (Altinkiliç and Hansen (2000)), transactions costs (Korajczyk and Levy (2003), Strebulaev (2007) and Shivdasani and Stefanescu (2010)), and cash flow realizations (Faulkender, Flannery, Hankins, and Smith (2012)).

In our paper, we propose and test a

―supply-side‖ factor to see if it affects leverage adjustment speed. Specifically, we examine the impact that bank competition has on firms’ leverage adjustment speed. While there are many theoretical and empirical studies on how credit and financial market environments (and bank competition specifically) affect economic development,1 there are very few studies on how the credit market environment might affect corporate capital structure. The reason is that we tend to believe only firm-specific factors determine financing decisions. However, as Titman (2002) points out, credit market conditions and supply-side factors can certainly determine firms’ capital structure choices, even though these impacts have been largely ignored by researchers for the past 20 years. 1

Titman (2002) provides anecdotal

This literature in this regard is vast. See literature reviews and literatures cited in Berger, Demsetz, and Strahan (1999), Beck, Demirgüç-Kunt, and Maksimovic (2004), Bonaccorsi di Patti and Dell’Ariccia (2004), and Cetorelli and Strahan (2006).

1

evidence that supply-side factors matter in corporate capital structure decisions (specifically, he discusses how the availability of junk bonds in the 1980s altered firms’ financing mix). Graham and Harvey (2001) provide survey evidence consistent with Titman’s (2002) point, as they find managers view supply conditions to be an important factor in their firms’ capital structure decisions. Recently, empirical evidence also supports Titman’s (2002) point (and also his prediction that researchers eventually will study the impact that supply conditions have on firms’ financing). Faulkender and Petersen (2006) find firms without credit ratings (i.e., firms that cannot access public debt) have lower leverage ratios. That is, debt market segmentation influences firms’ leverage ratios.

Leary (2009) studies two credit shocks during the 1960s in the U.S. and finds

when bank debt becomes readily available (unavailable) firms have higher (lower) bank debt ratios.

That is, bank loan supply conditions influence firms’ financing mix.

In our paper, we

focus on an integral feature of credit market conditions, namely, bank competition, to see if it influences firms’ leverage adjustment speeds. In the Modigliani and Miller world, there would perfect competition among banks to lend money to firms (i.e., capital supply is infinitely elastic with no frictions). However, the reality is that the level of bank competition that exists can vary widely, especially given that bank regulations and the existence of other barriers can vary widely across both time and markets. But how would a reduction in supply frictions (i.e., in our case, more bank competition) affect leverage adjustment speed?

On the one hand, the reasons seem straight-forward and highly

intuitive. When banks compete, it should, according to conventional industrial organization 2

theories, increase the supply and lower the cost of bank loans (Pagano (1993) and Guzman (2000)). To put it another way, when bank competition increases, the bargaining and monopoly power of banks decrease.2

Therefore, under competitive banking conditions, where the market

for bank debt is ―liquid,‖ firms should be able to access bank debt more quickly when they need or desire it. This implies when firms are below their target leverage ratios, they should be able to move toward their targets faster when the banking sector is competitive. Note, however, that for over-levered firms, bank competition may not impact their leverage adjustment speeds, as banks may not want firms to reduce their reliance on, and use of, bank debt.3 However, there are two compelling reasons why bank competition may not lead to faster adjustment speeds for under-levered firms. First, banks are regulated and thus are limited in their ability to (i) increase loan supply and to (ii) decrease interest rates. banks obviously do not have an unlimited supply of loans available.

With regard to (i),

For banks to increase their

loan supply, they must have sufficient deposits to draw from while also maintaining minimum capital adequacy ratio requirements and reserve requirements.

With regard to (ii), banks may

not have much room to lower interest rates. The minimum interest rate they can charge on loans is usually some fixed rate above a benchmark rate (e.g., overnight inter-bank lending rate). Further, in many countries, interest rates on loans are regulated.

2

When banks have bargaining or monopoly power, they can limit loan supply and set high interest rates. For example, see Hannan (1991a). 3 We recognize that if a firm has too much debt, then creditors have incentives to either encourage the borrower to maintain high equity levels or restructure their borrower’s bank debt, but it is not clear whether these activities are more or less likely to happen when bank competition is high.

3

Second, and perhaps more importantly, some scholars argue that bank competition may be bad for real economic activity.4

Specifically, banks without market power may not have

incentives to invest in small firms if long-term relationships are not assured (Mayer (1988) and Petersen and Rajan (1995)).

And, when banking is competitive, banks may be unable to

conduct careful screens of borrowers’ credit-worthiness and thus charge high interest rates (Marquez (2002)).

That is, bank competition can lead to adverse selection, moral hazard, and

hold-up problems.

Given the contradictory views on how bank competition might affect firms’

abilities to access bank debt, it is not surprising that the empirical economic evidence in this regard is mixed.5,

6

Therefore, our study on the relation between bank competition and firms’ leverage adjustment speeds importantly contributes to two strands of the literature.

First, we propose

and empirically test whether a supply-side factor determines capital structure adjustment speed. Specifically, we test to see if high bank competition can increase firms’ leverage adjustment speeds.

Second, we contribute unique evidence to the continuing debate on whether or not

bank competition is good for economic development.

With regard to the latter contribution, in

our empirical tests we make sure to separately consider small firms.

According to scholars who

are critical of bank competition, they point out it is small firms in particular that suffer more 4

Cetorelli (2001) provides a review of the theoretical and empirical literatures on the costs and benefits of bank competition. 5 For example, empirical studies find low bank competition leads to high interest rates (Hannan (1991b)) and less new firm formations (Black and Strahan (2002) and Cetorelli and Strahan (2006)). However, other empirical studies find low bank competition leads to high probabilities of small firms obtaining bank debt (Petersen and Rajan (1995) and DeYoung, Goldberg, and White (1999). Some researchers therefore argue the optimal level of bank competition may be at an intermediate level (e.g., Dinç(2000) and Bonaccorsi di Patti and Dell’Ariccia (2004)). 6 The theory that market power is bad (good) for economic growth is known as the structure-performance (information-based) hypothesis. For example, see Beck, Demirgüç-Kunt, and Maksimovic (2004).

4

when bank competition is high. So, we test to see if high bank competition speeds up or slows down leverage adjustment speeds for small firms in particular. We use data from China to conduct our empirical tests. There are several reasons why China provides an ideal setting to test the effect of bank competition on firms’ leverage adjustment speeds.

First, bank debt is the primary source of firms’ external finance in China.

For example, in 2006, 85 percent of capital raised by listed-firms in China is bank debt (Federal Reserve Bank of San Francisco (2007)). Therefore, if bank competition affects firms’ leverage adjustment speeds, then we should clearly see it using Chinese data. Second, there is an obvious group of dominant banks in China known colloquially as the ―Big Four‖.7

The ―Big

Four‖ is a significant source of bank debt throughout our entire sample period, but their loan concentration declines significantly throughout the sample period. This means there is a high degree of time-series variation in bank competition in China.

Third, China is a large country

with 31 distinct provinces. The amount of power that the ―Big Four‖ has in each province varies. This means there is cross-sectional variation in bank competition, but within a single institutional environment.

Fourth, there is reason to believe that Chinese firms are more opaque

than U.S. firms and firms in other developed markets, as accounting and corporate governance standards in China may not assure a high level of transparency (e.g., Ball, Robin, and Wu (2000) and Firth, Fung, and Rui (2007)). Therefore, if small firms in particular suffer from high bank competition, then we should clearly see it using Chinese data. 7

Fifth, many banks in China are

See Allen, Qian, and Qian (2007) for an overview of China’s financial system. The ―Big Four‖ includes the Bank of China, Industrial and Commercial Bank of China, People’s Construction Bank of China, and Agricultural Bank of China.

5

mostly or partially state-owned. State-owned banks are known to pursue political objectives in their lending policies (LaPorta, Lopez-de-Silanes, and Shleifer (2002) and Sapienza (2004)). Therefore, bank competition may be particularly important for firms in China. will soon become the largest economy in the world.

Sixth, China

Therefore, it would be useful, in general,

to understand the role that China’s banking structure has on its firms’ financing. Finally, another benefit of studying China is that the country has state-owned enterprises (SOEs) and non-state-owned firms (non-SOEs). government.

For SOEs, the controlling shareholder is the

By looking at these two sets of firms separately, we can better understand the role

that government ownership and control plays on firm financing. As mentioned earlier, most banks in China are mostly or partially state-owned.

There is a strong sentiment that

state-owned banks discriminate in their lending to non-SOEs (e.g., see Brandt and Li (2003) and Martin (2012)). However, when there is high bank competition, then even non-SOEs may be able to access bank debt more easily. Therefore, we test to see if high bank competition increases leverage adjustment speeds for non-SOEs in particular. Using all listed-firms with available data from China’s two stock exchanges, during the period 1998-2009, we find the mean leverage adjustment speed for Chinese firms is very similar to U.S. firms.

Specifically, we find a mean active leverage adjustment speed of 29.6%.

That

is, on average, Chinese firms actively close 29.6% of the gap between their target leverage ratios and their previous year’s leverage ratios.

For U.S. firms, Faulkender, et al. (2012) find a mean

active leverage adjustment speed of 31.6%. We further separate our firms into those below

6

their target leverage ratios and those above their target ratios and find a much faster leverage adjustment speed for over-levered firms, consistent with Faulkender, et al (2012).8 More importantly, at least from our paper’s perspective, we find when there is higher bank competition the leverage adjustment speed is faster, but only for under-levered firms. That is, when bank competition is higher, then under-levered firms move toward their target leverage ratios faster.

This finding is impressive given that banks are limited in their ability to

increase loan supply and to decrease interest rates.

For over-levered firms, bank competition

has no effect on leverage adjustment speed, but this can be viewed as being as expected. Banks may not have incentives to help firms lessen their debt levels, and even if they do, the effect that bank competition has on this incentive is unclear.

For under-levered firms, a one standard

deviation increase in our bank competition variable increases the leverage adjustment speed by 15.4 percent. For small firms, the positive relation between bank competition and leverage adjustment speed is even stronger, but again only when the firm is under-levered. particular benefit from bank competition.

So, small firms in

Our findings offer unique support for the

conventional industrial organization theory that bank competition is good for economic development.

Bank competition allows small firms, which are customarily the sources and

drivers of country’s future economic growth, to obtain financing when it is beneficial to them.

8

They offer two possible explanations for the asymmetry: (i) there may be a greater benefit to over-levered firms to move toward their targets (i.e., financial distress costs may be high) or (ii) leverage adjustment costs might somehow be lower for over-levered firms.

7

For non-SOEs, the positive relation between bank competition and leverage adjustment speeds is also stronger, but again only when the firm is under-levered.

So, when bank

competition is high, then even non-SOEs can obtain bank debt easily to move to their target leverage ratios. Before concluding the paper, we conduct one final test. Our paper is a story about how supply-side conditions affect firms’ leverage adjustment speed.

When there are more banks

competing (i.e., when there are more suppliers of bank debt), then firms benefit. However, what if the demand for bank debt is high?

Given that bank debt supply is obviously not

infinitely elastic in the absence of perfect bank competition, if there is high demand for bank debt then this should slow down leverage adjustment speeds, as banks will have bargaining power over the many firms they can lend to.

Further, high demand for debt decreases the

supply (for each firm) and increases the cost of bank debt.

However, if there is high bank

competition, then it should weaken each bank’s bargaining power. And, also with high bank competition, there may be enough supply of debt, at lower costs, to meet the high demand for bank loans.

We use the number of firms in each province to proxy for bank debt demand in

each province. We find when there are many firms in a province leverage adjustment speeds are slower, consistent with our expectation. However, for these provinces with many firms, we find that when bank competition is high, it leads to faster leverage adjustment speeds, which is also consistent with our prediction. Finally, before proceeding with the paper, we should mention two additional points. First, it is important to note that our hypothesis that bank competition is related to firms’ leverage 8

adjustment speed does not necessarily imply a positive relation between bank competition and firms’ leverage ratios.

For example, when bank competition is high, it does not directly cause

firms to have higher target leverage ratios or higher debt capacities. The only way higher bank competition leads to higher debt ratios is if firms’ demand for debt is being unmet before bank competition increases.

However, in unreported tests, we do not find a statistically significant

correlation between leverage ratios and bank competition.

Therefore, it does not appear there is

unmet demand for debt when bank competition is low.9 Second, while our paper does not directly test capital structure theories in China, our results are consistent with both the traditional trade-off theory and the pecking order theory of Myers (1984) and Myers and Majluf (1984). We find that under-levered (over-levered) Chinese firms subsequently increase (decrease) their leverage ratio, consistent with Fischer, Heinkel, and Zechner (1989), Leary and Roberts (2005), and Faulkender et al. (2012), suggesting that managers believe there is an optimum leverage ratio that trades off the benefits and costs of debt, consistent with the trade-off theory. We also find that under-levered small firms move toward their target leverage more slowly than other firms, suggesting that information asymmetry (i.e., transactions costs) that is inherent in small firms hinder them from obtaining external financing when they need it, consistent with pecking order theory.

9

In our discussions with corporate executives in China, they describe that their firms used to ―hoard‖ bank debt when bank debt was difficult to obtain. This is another possible reason why there is no observable positive relation between leverage levels and bank competition levels.

9

The rest of our paper proceeds as follows. The next section describes the banking environment in China.

Section 3 describes our data and empirical approach.

Section 4

presents and discusses results. The last section concludes. 2.

China’s banking system and structure10

2.1

Development of the banking system in China Prior to the mid-1980s, bank competition was virtually nonexistent and the banking

sector only consisted of China’s central bank, known as the People’s Bank of China (PBOC) and four fully state-owned banks known colloquially as the ―Big Four‖ including: Bank of China (BOC), China Construction Bank (CCB), Agriculture Bank of China (ABC), and Industrial and Commercial Bank of China (ICBC). The ―Big Four‖ banks each had a primary role in China’s planned economy.

For example, BOC financed importing and exporting, CCB financed

construction projects, and ABC provided loans for agriculture.

Then, starting in the late 1980s,

new commercial banks known as joint-stock banks, as they were only partially state-owned, began to spring up, such as Bank of Communications, China Merchants Bank, and CITIC Industrial Bank. These joint-stock banks dramatically changed China’s banking industry as they started competing with the ―Big Four‖ banks.

In 1996, Minsheng Bank became China’s

first mostly privately-owned bank. In 1994, the policy-banking divisions of the ―Big Four‖ banks were divested to create three new development banks: China Development Bank, Agricultural Development Bank of

10

Much of this discussion draws from Allen, Qian, and Qian (2005, 2008), Martin (2012), and Walter and Howie (2012).

10

China, and the Export-Import Bank of China.

In 1995, the ―Commercial Bank Law‖ was

formally enacted to allow the ―Big Four‖ banks to become more market-oriented from both legal and business perspectives.

Specifically, the ―Big Four‖ banks were no longer restricted to

specific activities, businesses, or industries.

At the same time, other banks were allowed to

finance activities that had previously been reserved for the ―Big Four.‖

Since 1996, regional

commercial banks (e.g., Bank of Beijing, Bank of Shanghai, Bank of Nanjing, etc.) started appearing and quickly began playing an active role for local economies.

These banks primarily

served their provinces, and most are joint-stock banks. After the East Asian financial crisis in 1997, the Chinese government became wary of how systemic risks in banking sectors can contribute to financial crises, so more banking reforms took place to move the sector into a more market-based system.

Since China’s entrance into the

World Trade Organization (WTO) in 2001, the banking sector progressed further by becoming more global.

A series of reforms took place in 2003 so that fully state-owned banks could

become joint-stock banks by issuing initial public offerings (IPOs), a process known as equitization. Since 2005, fully state-owned commercial banks started conducting IPOs in the global marketplace.11

The main objective behind these IPOs was not simply to raise capital, but

to introduce better governance and incentive systems so that these banks could become more market-oriented.12

At the end of 2006, the government started to remove many restrictions it

11

Today, all of the ―Big Four‖ banks are now joint-stock banks. China Construction Bank issued its IPO in Hong Kong in 2005 and in Shanghai in 2007. Industrial and Commercial Bank of China and Bank of China issued their IPOs in Hong Kong and Shanghai in 2006. Agricultural Bank of China issued its IPO in Hong Kong and Shanghai in 2010. However, all of the ―Big Four‖ are still mostly owned by the government (Martin (2012)). 12 For example, see Berger, Hasan, and Zhou (2009) and Chang, Hu, Chou, and Sun (2012).

11

had imposed on foreign banks, allowing them to compete on a more even playing field with local Chinese banks.13

In 2007, the government allowed the Postal Savings Bank of China, which

was established in 1986 as a savings banks only, to begin lending explicitly to small and medium sized enterprises. During the last year of our study sample period, 2009, the banking system in China comprises of the central bank, the ―Big Four‖ commercial banks, three development banks, the postal savings bank, 13 national joint-stock commercial banks, 143 regional commercial banks, 95 foreign banks, 11 privately-owned rural banks, and 3,056 rural credit cooperatives (these are similar to credit unions in the U.S.). From 1998 to 2009, which represents our study sample period, total deposits of all financial institutions in China grew from 9.57 trillion yuan to 59.77 trillion yuan, which is more than a 500% increase, and the total loan balance grew from 8.65 trillion yuan to 39.97 trillion yuan, which is almost a 400% increase (source: China Statistical Yearbooks).

The total loan

balance in 2009 is 1.17 times China’s GDP during the same year, which shows the importance of the banking system to China’s economy.

As of 2012, each of the ―Big Four‖ commercial banks

is among the world’s 15 largest banks by total assets.14 2.2

Bank regulations in China China’s central bank, known as the People’s Bank of China (PBOC) regulates China’s

banking system, in addition to formulating and implementing China’s monetary policy.

13 14

The

For example, foreign banks were previously tightly restricted with regard to where and to whom they could lend. http://www.gfmag.com/tools/best-banks/11986-worlds-50-biggest-banks-2012.html#axzz2DTjM9Zqw

12

China Banking Regulatory Commission (CBRC) oversees banks to ensure they adhere to PBOC’s banking regulations.

Similar to the U.S. Federal Reserve, the PBOC sets banks’

reserve requirements, sets interest rates for inter-bank lending, and controls the money supply. However, the PBOC has two regulatory tools that the U.S. Federal Reserve does not have: (1) it can set benchmark rates on bank deposits and bank loans and (2) it can set credit limits to banks. Both have direct relevance to our study. allowed to offer interest rates within a band.

First, under PBOC regulations, banks are only This means banks may have limited ability to

adjust interest rates up or down in the face of bank competition.

Second, the PBOC only allows

each bank’s credit to grow by a fixed fraction each year. For example, the PBOC set a target credit growth rate of 13-14% for 2011.

Another way the PBOC can control commercial bank

lending is through its reserve requirement. controls the money supply.

In fact, this is the primary way that the PBOC

Given PBOC’s control over credit growth and reserve requirements,

it means banks may have limited ability to increase loans in the face of bank competition. However, despite China’s bank regulations, bank competition may still impact overall bank loan supply and cost.

Note that China initiated major banking reforms throughout the

1990s to make banking competitive and market-oriented, suggesting that banks must be afforded some latitude to compete on lending rates. And, high banking competition implies not only more loan supply from each bank but also from more banks.

Finally, even though China sets

curbs on credit growth at each bank, it appears this curb is merely a target rather than a strict restriction.

For example, we mentioned earlier that the PBOC set a target credit growth rate of

13-14% for each bank in 2011, but the average increase in banks’ total loans outstanding still 13

grew by 15.7% in 2011, suggesting the PBOC’s control over credit growth rates is loose (Martin (2012)).

How are Chinese banks able to stretch their lending amounts despite reserve

requirements?

There is evidence that suggests Chinese banks increase their loan supply by

competing for, and thereby increasing, deposits (Yi and Zhao (2001)). 2.3

Importance of bank financing in China At the end of 2009, there are 1,718 listed companies in China’s two stock

exchanges—Shanghai and Shenzhen.

The total market capitalization is 24.39 trillion yuan,

which is 71.6% of China’s 2009 GDP.

Although the size of stock markets, which were

established in 1990, has been growing fast each year, equity financing still lags far behind debt financing (Tong (2005) and Fan, Rui, and Zhao (2008)).

Allen, Qian, and Qian (2005, 2008)

also report that debt financing plays a dominant role for corporate financing in China. According to an estimate by the Federal Reserve Bank of San Francisco (2007), the corporate bond market in China provided only 1.4% of the total capital raised by corporations in China in 2006, whereas commercial banks provided 85%.

According to China Securities and Futures

Statistical Yearbook (2010), the total amount of outstanding corporate bonds was 2.44 trillion yuan in 2009, including 457.18 billion short-term bonds and 872.44 billion mid-term bonds, which only represents 6.1% compared to total bank loans in 2009.

In other words, throughout

our entire sample period, 1998-2009, bank debt is by far the primary source of external financing in China. 2.4 Bank competition in China

14

From 1998 to 2009, the market share of total deposits in the ―Big Four‖ banks declined from 74.27% to 50.91%. That is, the market share of non-Big Four banks doubled (i.e., from 25.67% to 49.09%).

During the same time period, the market share of total loans by the ―Big

Four‖ banks declined from 72.21% to 43.67% (estimated from the Almanac of China's Finance and Banking). Clearly, the banking industry in China has gradually become quite competitive. There are also significant differences in bank concentration across China’s 31 different provinces.

In 2009, the ratio of ―Big Four‖ banks’ total assets in each province to the entire

banking sector’s total assets in each province ranges from 41.3% in Liaoning to 96.7% in Xizang. The mean (median) is 50.7% (49.5%), and the standard deviation is 9.7% (source: Report on Regional Financial Market (2009)). This by-province variation in bank concentration, together with the time-series variation, provides us with sufficient statistical power, and an ideal setting, to test the effect that bank competition has on firms’ leverage adjustment speeds. 3.

Data and empirical methodology

3.1

Data Our sample includes all Chinese listed firms from both the Shanghai and Shenzhen Stock

Exchanges from 1998 to 2009.

The sample begins in 1998 because important financial

statements variables (e.g., short term debt and depreciation) only become available during this time. Financial statements and stock returns data come from the China Stock Market and Accounting Research (CSMAR) database. many different provinces.

Of course, many Chinese firms have locations in

Following literature (e.g., Coval and Moskowitz (1999), Seasholes

and Zhu (2010), and Pirinsky and Wang (2006)), we identify a firm’s location by where its 15

headquarters are located.

As usual, we exclude financial firms from our sample.

And,

because we examine year-to-year changes in leverage, we also delete firms with fewer than two consecutive years of data. Our final study sample consists of 12,463 firm-year observations. 3.2

Variable definitions and basic regression specification Our methodological approach follows Faulkender, et al. (2012).

Normally, the literature

(e.g., Flannery and Rangan (2006), Byoun (2008), Lemmon, Roberts, and Zender (2008), and Huang and Ritter (2009)) estimates leverage adjustment speed with the following regression model:

Levi ,t  Levi ,t 1 

Di ,t Ai ,t



Di ,t 1 Ai ,t 1

  ( Lev *i ,t  Levi ,t 1 )   i ,t

(1)

where Di ,t is firm i’s outstanding debt at time t, Ai ,t is the firm’s book assets at time t, Lev i ,t is the leverage ratio (long-term debt plus short-term debt to total assets) at time t, Lev i ,t 1 is lagged leverage, and Lev * i ,t is the estimated target leverage ratio given firm characteristics at t-1. The lambda ( ) value is known as the firm’s speed of adjustment toward its target leverage ratio. However, the recent literature (e.g., Faulkender, et al. (2012)) argues that lambda in equation (1) can change without any ―active‖ capital structure adjustment when firm’s post their income to its equity account.

As argued by Faulkender et al., any test designed to measure

firms’ adjustments to leverage targets should focus on firms’ ―active‖ adjustments, where they access capital markets in some way or form and ―pay‖ the associated transactions costs. Therefore, following Faulkender, et al., we revise model (1) to be as follows:

16

Levi ,t  Lev p i ,t 1   ( Lev *i ,t  Lev p i ,t 1 )   i ,t where Lev p i ,t 1 

Di ,t 1 Ai ,t 1  NI i ,t

(2)

and NI i ,t is equal to net income during year t.

leverage at t would be Lev p i ,t 1 if the firm does not access capital markets.

Note that

The left-hand side

of (2) is the firm’s ―active‖ adjustment toward its target leverage ratio. The literature (e.g., Flannery and Rangan (2006), Byoun (2008), Lemmon, et al. (2008), and Huang and Ritter (2009)), estimates target leverage, Lev * i ,t , as follows:

Lev *i ,t  X i ,t 1

(3)

where  is a coefficient vector and X i ,t 1 is a vector of firm-specific characteristics hypothesized to be related to leverage, along with firm fixed effects and year fixed effects. Once the coefficients are estimated, they can be used to ―fit‖ each firm’s target leverage ratio. The choice and definitions of our firm-specific characteristics are the same as those in Faulkender, et al. (2012): EBIT_TA = (Income before extraordinary items + Interest expense + Income taxes)/Total assets, MB = (Book liabilities plus market value of equity)/ Total assets,15 DEP_TA = Fixed asset depreciation/Total assets, LnTA = ln(Total assets deflated by the consumer price index to 2004 yuan value), 15 In China, a complication arises when measuring market values of equity because many stocks are non-tradeable. Therefore, for these non-tradeable shares, we apply the book value of equity when calculating their market value. Specifically, for each firm, we multiply its amount of tradable shares by the market price at the end of the previous year and we multiply its amount of non-tradable shares by the book value of equity to obtain the total market value of equity. For robustness checks, we follow Bai, Liu, Lu, Song, and Zhang (2004) and we apply a 20%, 30%, and 0% (which assumes no discount, where non-tradeable shares are assumed to equal the market price) illiquidity discount when calculating the market value of these non-tradeable shares. Our results are robust to alternative measures of MB.

17

FA_TA = Fixed assets/ Total assets, Industry_median Lev = Median leverage for the firm’s one- or two-digit industry.16 Some studies (e.g., Flannery and Rangan (2006), Lemmon, Roberts, and Zender (2008), and Huang and Ritter (2009)) estimate target leverage concurrently with the speed of adjustment toward target, where equation (3) can be directly plugged into equation (1) or (2), leaving just one regression model to estimate as follows: Levi ,t  X i ,t 1  (1   ) Levi ,t 1   i ,t

(4)

However, because we are interested in seeing how bank competition affects leverage adjustment speed, we run model (4) to estimate target leverage first and then use it to estimate model (2). Estimating model (4) first allows us to include firm fixed effects and address estimation issues normally found in dynamic panel models. Based on the recommendation of Flannery and Hankins (2012), we estimate model (4) using Blundell and Bond’s (1998) GMM  estimation method to compute Lev*i ,t . Then, we use this computed target leverage ratio to

estimate equation (2) using OLS with bootstrapped standard errors given the generated regressor (Pagan (1984)).

By using equation (2) as our baseline model, it allows us later to interact

deviations from target leverage with a bank competition variable and also other variables of interest to see how these variables affect leverage adjustment speeds (see Faulkender, et al. (2012) and Öztekin and Flannery (2012) for similar leverage adjustment models with interaction terms).

16

We use finer two-digit industry classifications for the manufacturing industry because more than half of our firms come from this industry. For other industries, we use one-digit industry classifications. Industry classifications are based on Chinese Security Supervision Committee (CSSC) industry classifications.

18

Our measure of bank competition comes from the National Economic Research Institute’s (2012) Index of Marketization of China’s Provinces 2011 Report.17

They estimate a

bank competition index, BC_Index, in each province j and in year t as follows:

BC _ Index j ,t 

Share j ,t  Sharemin, 2001 Sharemax, 2001  Sharemin, 2001

 10

(5)

where Share j ,t is the percentage market share of total deposits in banks other than the ―Big Four‖ banks, Postal Savings Bank of China, and the three policy banks, in province j and in year t.

Sharemax, 2001 and Sharemin, 2001 are the maximum and minimum Share values among all

provinces in year 2001 and they are used to create a standardized index value from 0 to 10 in 2001. Therefore, BC_Index measures the degree of bank competition in a specific province and in a specific year relative to the degree of bank competition that exists in 2001.18 Note that our bank competition index is essentially the inverse of the concentration ratio of notable banks to all banks, which is a standard way of measuring bank competition in the literature.19

We listed the eight banks excluded from the Share measure above. The reason

these particular eight banks are excluded is because they historically and currently are viewed as the ―main‖ state-owned banks even though all of the non-policy banks are now joint-stock banks.20

Unfortunately, we are unable to restructure the BC_Index variable to only exclude the

17

National Economic Research Institute (NERI) is a non-government, non-profit research think tank in China. According to its website, NERI conducts independent and objective in-depth research on major issues concerning China’s economic development. 18 The BC_Index is also included as an independent variable in equation (3) to control for its potential main effect on leverage. 19 For example, see Journal of Money, Credit, and Banking’s special issue on bank competition and concentration published in 2004. Many of the papers in the special issue use a similar measure of bank competition as ours. 20 However, as previously mentioned, all ―Big Four‖ banks are still more than 50% owned by the government. The Postal Savings Bank is also mostly owned by the government, while the three development banks are wholly owned by the government.

19

―Big Four‖ banks, but we think the way it is measured is suitable for our study.

Because we are

interested in lending to firms by commercial banks, we would not want to include loans made by policy banks anyway (their lending is for policy/development projects on behalf of the government).21

That is, development banks do not compete with either the ―Big Four‖ or with

any other commercial banks. Also, and just as important, note that by excluding the market share of Postal Savings Bank, which primarily lends to small and medium sized enterprises, the empirical results are biased against finding small firms benefit from bank competition. Table 1 shows summary statistics of target leverage, deviation from target leverage, change in the deviation from target leverage, the BC_Index, as well as firm characteristic variables used in the target leverage calculation.

Table 1 reports that the BC_Index ranges from

-0.48 to 12.41, with a mean of 6.975 and a standard deviation of 2.656.

Note that the standard

deviation is large relative to the mean, indicating significant variation in the BC_Index across years and provinces. [Insert Table 1 here] 4.

Results

4.1

Baseline regression results Table 2 reports results from estimating equation (2).

The independent variable, Active

dev, is simply Lev * i ,t  Lev p i ,t 1 . The coefficient on this variable is the leverage adjustment speed. The first column of results in Table 2 reports an annual active leverage adjustment speed of 29.1%. 21

This speed is very similar to 31.6% active leverage adjustment speed

Martin (2012) states that all lending by policy banks is only to serve their economic roles.

20

documented for U.S. firms (see Faulkender, et al. (2012)).

We follow Faulkender, et al. (2012)

and report results separately for firms below (second column) and above (third column) their target leverage ratios.

The speed of adjustment for these two sets of firms is strikingly different

(25.5% for under-levered firms vs. 48.0% for over-levered firms), and similar to what Faulkender, et al. finds (they find 29.8% for under-levered firms vs. 56.4% for over-levered firms).

Faulkender, et al. suggest two possible explanations for the asymmetry: (i) the benefits

may be greater for over-levered firms to reach their targets (i.e., their financial distress costs may be high) or (ii) the leverage adjustment costs for over-levered firms may somehow be lower. [Insert Table 2 here] 4.2

Effect of bank competition on leverage adjustment speed To estimate the effect that bank competition has on leverage adjustment speeds, we

follow Faulkender, et al. (2012) and generalize our model by specifying that the ith firm’s adjustment speed at time t depends on a variable of interest (in our case, that variable is bank competition) as follows:

Levi ,t  Lev p i ,t 1  ( 0   1 BC _ Indexi ,t )( Lev *i ,t  Lev p i ,t 1 )   i ,t

(6)

where  1 is the coefficient on the interaction between active deviation from target leverage and our bank competition index variable.

The coefficient  1 captures the influence of bank

competition on the speed of leverage adjustment.

When  1 is positive, bank competition

speeds up the adjustment, and when it is negative, bank competition slows down the adjustment. Table 3 presents results from estimating equation (6). We again report results for all sample

21

firms and separately for over-levered and under-levered firms.

The results are robust to lagged

bank competition, i.e., BC _ Indexi ,t 1 , and so they are not presented for brevity. [Insert Table 3 here] The first column of results in Table 3 shows bank competition increases leverage adjustment speeds. This finding supports the hypothesis that a supply-side factor, namely, bank competition, can affect firms’ leverage adjustment speeds.

This finding also supports

conventional industrial organization theories that bank competition increases loan supply and decreases loan cost. Even though this finding is consistent with our expectation, we still feel it is impressive given that banks are regulated (especially in China) in their loan supply and costs. The results in the second and third columns reveal that high bank competition only speeds up leverage adjustment for under-levered firms.

This result can be viewed as being as expected.

When firms are under-levered, they can move toward their target leverage ratios more quickly when banks compete to lend to them. When firms are over-levered, there is no clear reason why banks would compete to help firms reduce their leverage. The faster leverage adjustment speed for under-levered firms when bank competition is high is not only statistically significant, it is also economically significant.

When the BC_index

increases by one, it increases under-levered firms’ leverage adjustment speeds by a relative 5.8% (i.e., the coefficient on the interaction term is 0.011 compared to the 0.190 coefficient on Active dev).

A one standard deviation increase in the BC_index increases under-levered firms’

adjustment speeds by 15.4%.

22

4.3

Effect of bank competition on leverage adjustment speeds for small firms To see if small firms in particular benefit from high bank competition, we include the

following three-way interaction term in our regression model: BC_Index × D_small × Active dev. We must be careful in specifying the dummy variable, D_small, to indicate small firms because, on average, non-SOEs are smaller than SOEs.

In our sample, SOEs (non-SOEs) have a mean

total asset value of 4.94 billion yuan (2.06 billion yuan).

Therefore, to make sure any small

firm findings are not being driven simply by a non-SOE effect, we specify the D_small dummy in the following way: For the sample of SOEs-only (non-SOEs-only), D _ small is equal to one when a firm’s total assets is smaller than the median total assets of all SOEs (non-SOEs) in the same year and same industry, otherwise it is equal to zero. This way, SOEs and non-SOEs are equally represented in the small firm category.

We estimate the following model:

Levi ,t  Lev p i ,t 1  ( 0   1 BC _ Indexi ,t   2 D _ smalli ,t   3 BC _ Indexi ,t  D _ smalli ,t )  ( Lev *i ,t  Lev p i ,t 1 )   i ,t

(7)

If the coefficient  3 is positive, then it indicates that small firms experience faster adjustment speeds when bank competition is high. Table 4 reports the results from estimating equation (7). [Insert Table 4 here] The first column of results in Table 4 shows small firms have faster leverage adjustment speeds when bank competition is high. From the second and third columns, we see it is under-levered firms that drive these results, consistent with our earlier findings.

The increase in

adjustment speed for under-levered small firms is statistically significant and also economically significantly.

When the BC_index increases by one, it increases small firms’ leverage

23

adjustment speed by a relative 5.0%.

A one standard deviation increase in the BC_index

increases under-levered small firms’ adjustment speeds by 12.8%. Note also that small firms’ adjustment speeds are much faster than other firms’ adjustment speeds (i.e., the coefficient on the three-way interaction is 0.011 compared to the 0.005 coefficient on the interaction between BC_Index and Active dev).

That is, small firms benefit more than large firms when bank

competition is high. When bank competition is low, large firms can probably still access bank debt relatively easily compared to small firms. Table 4 shows two additional interesting results.

In the second column, we also see that

small firms in general have slower leverage adjustments than other firms.

That is, the

interaction term between D_small and Active dev is negative and statistically significantly. The result is not surprising.

Under-levered small firms may be expected to have a harder time

obtaining debt compared to under-levered large firms, ceteris paribus. However, when bank competition is high, these under-levered small firms are able to enjoy faster leverage adjustment speeds.

In the third column, we see that among over-levered firms, small firms in particular

move to their target leverage faster. This result suggests small firms (compared to large firms) are particularly keen on reducing their debt ratios when they are over-levered, as small firms usually have higher financial distress costs. 4.4

Effect of bank competition on leverage adjustment speeds for non-SOEs A firm is identified as an SOE if its largest shareholder is the government.

To see if

non-SOEs benefit from high bank competition, we include the following three-way interaction term in our regression model: BC_Index × D_nonSOE × Active dev, where D_nonSOE is equal 24

to one (zero) when the firm is a non-SOE (SOE).

Specifically, we estimate the following

model:

Levi ,t  Lev p i ,t 1  ( 0   1 BC _ Indexi ,t   2 D _ nonSOE i ,t   3 BC _ Indexi ,t  D _ nonSOE i ,t )  ( Lev *i ,t  Lev p i ,t 1 )   i ,t

(8)

If the coefficient  3 is positive, then it indicates that non-SOEs experience faster adjustment speeds when bank competition is high. Table 5 reports the results from estimating equation (8). [Insert Table 5 here] The first column of results in Table 5 shows non-SOEs have faster leverage adjustment speeds when bank competition is high. From the second and third columns, we see it is under-levered firms that drive these results, consistent with our earlier findings. The increase in adjustment speed for under-levered non-SOEs is statistically significant and also economically significantly.

When the BC_index increases by one, it increases non-SOEs’ leverage

adjustment speed by a relative 4.5%.

A one standard deviation increase in the BC_index

increases under-levered non-SOEs’ adjustment speeds by 11.9%. Note also that non-SOEs’ adjustment speeds are more than twice as fast as other firms’ adjustment speeds (i.e., the coefficient on the three-way interaction is 0.009 compared to the 0.007 coefficient on the interaction between BC_Index and Active dev).

That is, non-SOEs benefit more than SOEs

when bank competition is high. When bank competition is low, SOEs can probably still access bank debt relatively easily (after all, many banks are partially state-owned) compared to non-SOEs.

25

4.5

Effect of bank competition on leverage adjustment speeds when there are many firms Finally, we test to see if high bank competition is helpful when demand for bank debt is

also high. Our proxy to measure bank debt demand is the number of firms in each province. We assume more firms in a province means there is more debt demand in that province. To conduct our test, we include the following three-way interaction term in our regression model: BC_Index × D_more × Active dev, where D_more is equal to one when a firm is located in a province where its number of listed-firms is more than the median number of listed-firms in all provinces in the same year, otherwise it is equal to zero.

Specifically, we estimate the

following model:

Levi ,t  Lev p i ,t 1  ( 0   1 BC _ Indexi ,t   2 D _ morei ,t   3 BC _ Indexi ,t  D _ morei ,t )  ( Lev *i ,t  Lev p i ,t 1 )   i ,t

(9)

If the coefficient  3 is positive, then it indicates that firms in high-demand provinces experience faster adjustment speeds when bank competition is high. Table 6 reports the results from estimating equation (9). [Insert Table 6 here] The first column of results in Table 6 shows firms in provinces with many other firm suffer from slower leverage adjustment speeds (i.e., the interaction term between D_more and Active dev is negative and statistically significant). This result supports the contention that when the demand for bank debt is high, banks have more bargaining power over firms and less available debt (for each firm) at potentially higher costs.

However, the coefficient on three-way

interaction term is positive and statistically significant, indicating that for those provinces with

26

many firms, when bank competition is also high, it leads to faster leverage adjustment speeds. From the second and third columns, we see it is under-levered firms that experience faster leverage adjustment speeds when bank competition is high, consistent with our earlier findings. The increase in adjustment speed for under-levered firms in provinces with many other firms is statistically significant and also economically significantly.

Specifically, when the BC_index

increases by one, it increases these firms’ leverage adjustment speed by a relative 6.4%.

A one

standard deviation increase in the BC_index increases thse under-levered firms’ adjustment speeds by 17.0%. 5.

Conclusion Traditionally, we tend to believe only firm-specific factors are important in firms’

financing decisions, but anecdotal evidence, survey findings, and recent empirical evidence suggest credit market conditions and supply-side factors also affect corporate capital structure. In our paper, we focus on an integral feature of credit market conditions, namely, bank competition, to see if it influences firms’ leverage adjustment speeds.

When banks compete, it

should, according to conventional industrial organization theories, increase the supply and lower the cost of bank loans. This implies firms should be able to obtain bank debt quickly and cheaply when they need or desire it. Therefore, if firms are below their target leverage ratios, they should be able to move toward their targets faster when the banking sector is competitive. To test our hypothesis, we use data from China.

There are several reasons why China

provides an ideal setting to test the effect of bank competition on firms’ leverage adjustment speeds.

For example, bank debt is the primary source of firms’ external finance in China. 27

There is also an obvious group of dominant banks in China known colloquially as the ―Big Four‖.

And, China also has 31 provinces with different degrees of bank competition. Using all listed-firms with available data from China’s two stock exchanges, during the

period 1998-2009, we find leverage adjustment speeds in China are very similar to what has been documented for U.S. firms.

More importantly, we find higher bank competition is associated

with faster leverage adjustment speeds, but only for under-levered firms. That is, when bank competition is higher, then under-levered firms move toward their target leverage ratios faster, consistent with our hypothesis. Small firms in particular might have a hard time accessing bank debt.

In additional tests,

we find this to be true, i.e., small firms suffer slower leverage adjustment speeds when they are under-levered, however, when bank competition is high we find that small firms enjoy faster leverage adjustment speeds.

In China, non-SOEs might also have a hard time accessing bank

debt, especially given that many banks in China are mostly or partially state-owned.

In

additional tests, we find when bank competition is high, non-SOEs enjoy faster leverage adjustment speeds.

Finally, firms in provinces with many other firms may have a hard time

accessing bank debt.

In these provinces, banks have bargaining power over the many firms

demanding bank debt, and also the supply of debt might be lower (for each firm) and the cost of debt might be higher.

In additional tests, we find firms in provinces with many other firms

suffer slower leverage adjustment speeds, consistent with our contention, however, when bank competition is high we find that the leverage adjustment speed improves for these firms.

28

Some scholars argue that bank competition is bad for economic development. competition can lead to adverse selection, moral hazard, and hold-up problems.

Bank

Our findings

offer unique support for the view that bank competition is good for the economy. We find bank competition allows small firms, which are customarily the sources and drivers of country’s future economic growth, to be able to obtain bank financing relatively quickly when it is beneficial to them. Overall, therefore, we importantly contribute to two strands of the literature. We extend recent literature that cites credit market conditions as an important factor in firms’ financing and test whether it is also important for firms’ leverage adjustment speeds.

Second, by finding that

high bank competition increases leverage adjustment speeds, we offer unique evidence to the debate on whether bank competition is good or bad.

29

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Table 1 Descriptive statistics Table 1 provides summary statistics for all variables used in our study. The sample contains all nonfinancial listed-firms in China with complete data from CSMAR, for the time period 1998–2009. Panel A reports statistics on all firms and also on under-levered (below target leverage) and over-levered (above target leverage) firms. Over-levered and under-levered statistics are not reported in Panel B because the variables used to estimate leverage targets are estimated on the full sample. Target leverage ratio, Lev target, is estimated using the methodology presented in Section 3.2. Active dev is the target leverage ratio less the adjusted lagged leverage ratio, which is defined as the previous period’s total debt divided by the sum of the previous period’s book assets plus net income for the current period. ΔActive dev is the leverage ratio less the adjusted lagged leverage ratio. BC_index is a measure of bank competition, calculated as the total deposit share of banks except the ―Big Four‖ state-owned banks, Postal Savings Bank of China, and three policy banks, normalized by BC_index values in 2001. Lev is total debt (long-term debt plus short-term debt) divided by book value of assets. EBIT_TA is income before extraordinary items plus interest expense plus income taxes divided by total assets. MB is the sum of book liabilities and the market value of equity divided by total assets. DEP_TA is fixed asset depreciation divided by total assets. LnTA is the natural log of total assets deflated by the consumer price index to 2004 yuan value. FA_TA is fixed assets divided by total assets. Industry_median Lev is the annual median leverage ratio for two-digit industries (for the manufacturing sector) or one-digit industries (for all other industries), based on the Chinese Security Supervision Committee (CSSC) industry code classifications.

Under Mean Median Std. Dev. levered Panel A: Leverage target, deviation from target, and bank competition index Lev target 0.321 0.311 0.213 0.398 Active dev 0.047 0.038 0.178 0.148 ΔActive dev 0.011 0.008 0.095 0.036 BC_index 6.975 7.120 2.656 6.678 Panel B: Firm characteristics used in target leverage calculation Lev 0.274 0.263 0.175 EBIT_TA 0.047 0.054 0.087 MB 1.360 1.139 0.755 DEP_TA 0.024 0.020 0.016 LnTA 21.139 21.032 1.017 FA_TA 0.299 0.272 0.179 Industry_median Lev 0.268 0.261 0.063

35

Over levered 0.208 -0.101 -0.027 7.411

Table 2 Baseline leverage adjustment speeds Table 2 presents OLS regression results where the dependent variable is firm’s change in active book leverage, ΔActive lev, and the independent variable is firm’s active deviation from its target leverage ratio, Active dev. Both variables are defined in Table 1. Column 2 (column 3) represent firm-years with leverage below (above) target leverage. Standard errors are bootstrapped to account for generated regressors (1000 replications). p-Values are reported in parentheses. ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively.

Levi,t-Levpi,t-1=γ(Lev*i,t-Levpi,t-1)+εi,t

Active dev N Adj. R2

ΔActive Lev Under lev. 0.255*** (0.000) 7,412 0.179

All 0.291*** (0.000) 12,463 0.300

36

Over lev. 0.480*** (0.000) 5,051 0.316

Table 3 Bank competition and leverage adjustment speed Table 3 presents OLS regression results where the dependent variable is firm’s change in active book leverage, ΔActive lev, and the independent variables are (i) firm’s active deviation from its target leverage ratio, Active dev, and (ii) an interaction term between BC_Index and Active dev. All variables are defined in Table 1. Column 2 (column 3) represent firm-years with leverage below (above) target leverage. Standard errors are bootstrapped to account for generated regressors (1000 replications). p-Values are reported in parentheses. ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively.

Levi,t-Levpi,t-1=(γ0+γ1BC_Index)×(Lev*i,t-Levpi,t-1)+εi,t

Active dev BC_Index ×Active dev N Adj. R2

ΔActive lev Under lev. 0.190*** (0.000) 0.011*** (0.000) 7,412 0.183

All 0.215*** (0.000) 0.012*** (0.000) 12,463 0.303

37

Over lev. 0.452*** (0.000) 0.004 (0.432) 5,051 0.316

Table 4 Bank competition and leverage adjustment speed for small firms Table 4 presents OLS regression results where the dependent variable is firm’s change in active book leverage, ΔActive lev, and the independent variables are (i) firm’s active deviation from its target leverage ratio, Active dev, (ii) an interaction term between BC_Index and Active dev, (iii) an interaction term between D_small and Active dev, and (iv) a three-way interaction term between BC_Index, D_small, and Active dev. D_small is specified in the following way: for the sample of SOEs-only (non-SOEs-only), D_small is equal to one when a firm’s total assets is smaller than the median total assets of all SOEs (non-SOEs) in the same year and same industry, otherwise it is equal to zero. This way, small firms are equally represented by SOEs and non-SOEs. All other variables are defined in Table 1. Column 2 (column 3) represent firm-years with leverage below (above) target leverage. Standard errors are bootstrapped to account for generated regressors (1000 replications). p-Values are reported in parentheses. ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively.

Levi,t-Levpi,t-1=(γ0+γ1BC_Index+γ2D_small+γ3BC_Index×D_small) ×(Lev*i,t-Levpi,t-1)+εi,t

Active dev BC_Index ×Active dev D_small ×Active dev BC_Index ×D_small ×Active dev N Adj. R2

ALL 0.224*** (0.000) 0.005** (0.039) -0.020 (0.529) 0.011** (0.014) 12,463 0.307

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ΔActive lev Under lev. 0.228*** (0.000) 0.005* (0.094) -0.072** (0.018) 0.011** (0.021) 7,412 0.184

Over lev. 0.314*** (0.000) 0.010 (0.155) 0.159** (0.031) -0.006 (0.514) 5,051 0.322

Table 5 Bank competition and leverage adjustment speed for non-SOEs Table 5 presents OLS regression results where the dependent variable is firm’s change in active book leverage, ΔActive lev, and the independent variables are (i) firm’s active deviation from its target leverage ratio, Active dev, (ii) an interaction term between BC_Index and Active dev, (iii) an interaction term between D_nonSOE and Active dev, and (iv) a three-way interaction term between BC_Index, D_non-SOE, and Active dev. D_nonSOE is equal to one (zero) when the firm is a non-SOE (SOE). All other variables are defined in Table 1. Column 2 (column 3) represent firm-years with leverage below (above) target leverage. Standard errors are bootstrapped to account for generated regressors (1000 replications). p-Values are reported in parentheses. ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively.

Levi,t-Levpi,t-1=(γ0+γ1BC_Index+γ2D_nonSOE+γ 3BC_Index×D_nonSOE) ×(Lev*i,t-Levpi,t-1)+εi,t

Active dev BC_Index ×Active dev D_nonSOE ×Active dev BC_Index ×D_nonSOE ×Active dev N Adj. R2

All 0.225*** (0.000) 0.006** (0.025) 0.000 (0.997) 0.009* (0.088) 12,463 0.307

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ΔActive lev Under lev. 0.201*** (0.000) 0.007** (0.014) -0.023 (0.514) 0.009* (0.074) 7,412 0.185

Over lev. 0.432*** (0.000) 0.002 (0.707) 0.073 (0.390) -0.001 (0.918) 5,051 0.318

Table 6 Bank competition and leverage adjustment speed when there are many firms Table 6 presents OLS regression results where the dependent variable is firm’s change in active book leverage, ΔActive lev, and the independent variables are (i) firm’s active deviation from its target leverage ratio, Active dev, (ii) an interaction term between BC_Index and Active dev, (iii) an interaction term between D_more and Active dev, and (iv) a three-way interaction term between BC_Index, D_more, and Active dev. D_more is equal to one when a

firm is located in a province where its number of listed-firms is more than the median number of firms in all provinces in the same year, otherwise it is equal to zero. All other variables are defined in Table 1. Column 2 (column 3) represent firm-years with leverage below (above) target leverage. Standard errors are bootstrapped to account for generated regressors (1000 replications). p-Values are reported in parentheses. ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively.

Levi,t-Levpi,t-1=(γ0+γ1BC_Index+γ2D_more+γ3BC_Index×D_more) ×(Lev*i,t-Levpi,t-1)+εi,t

Active dev BC_Index ×Active dev D_more ×Active dev BC_Index ×D_more ×Active dev N Adj. R2

ALL 0.296*** (0.000) 0.003 (0.523) -0.152*** (0.000) 0.016** (0.011) 12,463 0.307

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ΔActive lev Under lev. 0.266*** (0.000) 0.000 (0.925) -0.130*** (0.000) 0.017*** (0.003) 7,412 0.186

Over lev. 0.531*** (0.000) 0.001 (0.932) -0.179** (0.034) 0.013 (0.288) 5,051 0.320