Bank discrimination, holding bank ownership, and economic consequences: Evidence from China

Journal of Banking & Finance 36 (2012) 341–354

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Bank discrimination, holding bank ownership, and economic consequences: Evidence from China Zhengfei Lu a, Jigao Zhu b, Weining Zhang c,⇑ a

Guanghua School of Management, Peking University, Beijing, China Center for Finance and Accounting Research/Business School, University of International Business and Economics, Beijing, China c NUS Business School, National University of Singapore, Singapore b

a r t i c l e

i n f o

Article history: Received 8 October 2010 Accepted 22 July 2011 Available online 30 July 2011 JEL classification: G21 G28 G30 Keywords: Bank discrimination Holding bank ownership State-owned firms Political connections Bank loan

a b s t r a c t This paper finds that compared with Chinese state-owned firms, non-state-owned firms have a greater propensity to hold significant ownership in commercial banks. These results are consistent with the notion that because non-state-owned firms are more likely to suffer bank discrimination for political reasons, they tend to address their financing disadvantages by building economic bonds with banks. We also find that among non-state-owned firms, those that hold significant bank ownership have lower interest expenses, and are less likely to increase cash holdings but more likely to obtain short-term loans when the government monetary policy is tight. These results suggest that the firms building economic bonds with banks can enjoy benefits such as lower financial expenses and better lending terms during difficult times. Finally, we find that non-state-owned firms with significant bank ownership have better operating performance. Overall, we find that firms can reduce discrimination through holding bank ownership. Ó 2011 Elsevier B.V. All rights reserved.

1. Introduction A number of studies document bank discrimination against firms that do not enjoy political favoritism (see, e.g., La Porta et al., 2002; Brandt and Li, 2003; Khwaja and Mian, 2005; Faccio, 2006; Charumlind et al., 2006). These studies argue that banks give preferential treatment to politically connected firms because where governments pervasively control banks, bank managers have strong incentives for maintaining good relationships with governments. Meanwhile, firms with fewer political connections are playing an increasingly important role in national economies, especially transitional economies. It is interesting to consider how firms with fewer political connections circumvent such politically based bank discrimination and raise funding. Non-stateowned enterprises (hereafter cited as non-SOEs) and state-owned enterprises (hereafter cited as SOEs) in China provide a natural setting for investigating this issue. In China, non-SOEs have far fewer political connections than do SOEs because SOEs are ultimately controlled by the government. ⇑ Corresponding author. Tel.: +65 6601 1475; fax: +65 6773 6493. E-mail addresses: zfl[email protected] (Z. Lu), [email protected] (J. Zhu), [email protected] (W. Zhang). 0378-4266/$ - see front matter Ó 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.jbankfin.2011.07.012

On one hand, due to the nature of their ownership, non-SOEs are discriminated against in both equity financing and loan financing (e.g., Wang et al., 2008; Brandt and Li, 2003; Aharony et al., 2000; Su and Yang, 2009). On the other hand, non-SOEs have grown very rapidly in the past decade and need tremendous funding support. For example, in the manufacturing industries, the number of non-SOEs grew from 10,667 in 1998 to 245,850 in 2008. Simultaneously, the total profits of non-SOEs grew from 12.8% of the total profits of SOEs in 1998 to 91.6% of those profits in 2008. Although Allen et al. (2005) suggest that in China’s economy, non-SOEs raise funding through reputation and economic or noneconomic relationships with banks, no study has empirically investigated this conjecture. This paper fills the void by examining whether non-SOEs have a greater propensity than SOEs to build economic bonds with banks by holding ownership in those banks. Furthermore, we investigate whether such economic bonding can effectively eliminate bank discrimination against non-SOEs. In China, banks give preferential treatment to SOEs and discriminate against non-SOEs for various reasons. First, the government can require banks to issue ‘‘policy lending’’ to SOEs because the major banks are controlled by the government (Cull and Xu, 2003). Second, banks regard loans to SOEs as much safer because

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the government bails out SOEs if they have financial problems (Wang et al., 2008). Third, government officials have incentives to assist SOEs in attaining funding because successful SOEs create more resources for the government, enhancing the political capital of government officials and increasing their opportunities for promotion (Kornai, 1993; Qian, 1994; Li and Zhou, 2005; Wang et al., 2008). Finally, bank managers have a strong incentive to maintain good relationships with government officials and hence are more willing to grant loans to SOEs than to non-SOEs (Brandt and Li, 2003). Thus, non-SOEs face discrimination when obtaining bank loans, which are the most important funding source in China (Allen et al., 2005, 2008). In addition, they also face discrimination in seeking financing from the equity market (Wang et al., 2008; Su and Yang, 2009). Therefore, non-SOEs are much more likely to experience financial constraints or financial distress than SOEs. Such financing disadvantages have motivated non-SOEs to attempt to eliminate bank discrimination. Once a company holds significant ownership in a bank, it will be regarded as an insider party and can influence the bank manager to issue ‘‘insider loans’’ or practice ‘‘related lending’’ (see, e.g., Laeven, 2001; Charumlind et al., 2006; La Porta et al., 2003; Cull et al., 2006). In China, firms holding more than 5% bank ownership are very likely to assign representatives to the board of the bank and can thus influence the decision process for lending. Thus, holding a significant bank ownership stake can be an alternative way for non-SOEs to eliminate bank discrimination. Therefore, we argue that non-SOEs have a greater propensity than SOEs to hold significant ownership in banks. Furthermore, if such economic bonds can effectively eliminate bank discrimination, then among non-SOEs, those holding significant ownership in banks enjoy more direct credit benefits in terms of financial expenses and lending policy. Although China is not the only setting in which we can investigate firm responses to bank discrimination and its economic consequences, the evidence from China is generalizable, and the Chinese setting presents some advantages over that of developed countries. First, as prior studies note (see, e.g., La Porta et al., 2002; Dinc, 2005; Claessens et al., 2008), politically based bank discrimination is more pervasive in emerging markets. It is meaningful to investigate such issues in China, one of the most important emerging markets, and the implications of this study are likely to be generalizable to other emerging markets. Second, in many countries, the political connections of firms can change with changes in government administration. In contrast, in China, the political connections between non-SOEs and SOEs are very stable, and bank discrimination policies are persistent. Third, the capital markets are more mature in developed countries. Firms generally have more channels to access financial resources. So, the financial constraints faced by firms with fewer political connections are not as pronounced in those environments as they are in China. For these reasons, studying non-SOEs and SOEs in China provides more evidence of how firms respond to bank discrimination. Using a sample of Chinese listed firms from 2006 to 2008, we obtain the following results. First, consistent with our predictions, non-SOEs are more likely to hold significant bank ownership. Second, among the non-SOEs, those holding significant bank ownership have lower financial expenses. Third, the non-SOEs holding significant bank ownership are less likely to increase their cash holdings when the monetary policy of the central bank (the People’s Bank of China) becomes tighter, but they can obtain shortterm bank loans more easily when the bank’s monetary policy is tight. These results suggest that firms holding significant bank ownership benefit from reduced loan interest rates and from an increased likelihood of receiving loans when the macroeconomic policy is unfavorable. In additional analyses, we find that among non-SOEs, those holding significant bank ownership have fewer

long-term loans and are less likely to change their long-term loan holdings. We conjecture that because firms with bank relationships are more able to obtain short-term loans, which are less costly than long-term loans, they are less likely to obtain long-term loans and thus have less volatile and lower levels of long-term loan holdings. Finally, we find that as compared to non-SOEs without significant bank ownership, non-SOEs with significant bank ownership exhibit better returns on assets and greater sales. These results suggest that holding bank ownership is associated with better operating performance, possibly because reducing financial constraints improves a firm’s operational efficiency. Our paper makes several contributions to the extant literature in finance and economics. First, although prior studies (see, e.g., La Porta et al., 2002; Brandt and Li, 2003; Khwaja and Mian, 2005; Faccio, 2006; Charumlind et al., 2006) extensively document the existence of bank discrimination against firms with fewer political connections, no study has explicitly investigated the responses of these firms to bank discrimination. Our study investigates this issue and provides evidence that firms that encounter bank discrimination for political reasons are likely to improve their relationships with banks by investing in bank ownership. Our results also suggest that building economic bonds with a bank can effectively reduce discrimination by the bank. This idea is consistent with the notion that if a firm is at a disadvantage due to its political traits, then it will try to eliminate this disadvantage via economic strategy. Second, this paper extends our understanding of how firms avoid financial constraints in a transitional economy. Prior studies have extensively discussed the relationship between finance, law, and economic growth (see, e.g., Allen et al., 2005, 2008; Ge and Qiu, 2007; Yao and Yueh, 2009; Zhou, 2009). Specifically, Allen et al. (2005) argue that ‘‘China is an important counter example to the findings in the law, institutions, finance, and growth literature: neither its legal nor financial system is well developed, yet it has one of the fastest growing economies.’’ They believe that there is an alternative mechanism that private sectors use to raise funding. In this paper, we explore one concrete alternative mechanism. Legal and other institutions do not provide fair, equal access to financial resources for non-SOEs, but individual non-SOEs may respond to this disadvantage by building economic bonds. Third, this paper extends the findings on related lending. There are two major forms of ownership relationships between banks and firms: banks hold ownership of firms and firms hold ownership of banks. Both types of ownership relationships can lead to related lending. As far as we know, most extant studies on the subject of related lending focus on those situations in which banks hold ownership of firms, as with Keiretsu in Japan, chaebols in Korea, and controlling banks in China.1 In both Japan and Korea, the controlling banks appropriate most of the benefits of these relationships (e.g., Weinstein and Yafeh, 1998; Bae et al., 2002a, 2002b). In China, such lending is more likely to lead to inefficient investment and thus impair firm value, and banks play a less effective monitoring role (Lin et al., 2009; Luo et al., 2011). Therefore, the related lending in the form of banks holding a firm’s ownership generally does not benefit firms. Thus, unlike previous studies focusing on the economic consequences of this type of related lending, our study provides the first evidence that related lending can also benefit a firm 1 For example, Weinstein and Yafeh (1995, 1998), and Pinkowitz and Williamson (2001) find that in Japan, main banks are more likely to lend loans to firms that are members of their financial groups (Keiretsu). Bae et al. (2002a, 2002b) document that firms belonging to Korean business groups (chaebols) benefit from lending by related controlling banks. Using a sample from China, Lin et al. (2009) find that relationships between banks and controlling firms can facilitate related lending. Luo et al. (2011) find that when banks hold firm ownership shares in emerging markets such as China, they play a less effective monitoring role because they are more concerned with the security of their loans or aim to obtain better terms for those loans.

Z. Lu et al. / Journal of Banking & Finance 36 (2012) 341–354

if a firm holds ownership of a bank. These results suggest that who controls the ownership critically influences the effects of related lending on firm value and that related lending is an important channel through which the controlling party obtains benefits. In addition, although Kummer et al. (1989) and La Porta et al. (2003) also investigate the existence of related lending in the form of firm holding bank ownership, neither of these studies investigates why firms have an incentive to achieve bank ownership. Our study is the first to explore one important reason for holding bank ownership: protection from bank discrimination due to a lack of political favoritism. Finally, this study is also the first to investigate how bank ownership affects a firm’s cash holding decisions, borrowing behavior and operating performance. Lin et al. (2009) show that companies with banks as leading shareholders have relatively poorer operating performance and that the inefficient investments due to bank ownership are responsible for their disappointing performance. We investigate the reverse: how does a firm’s holding ownership in a bank affect the firm’s performance? Our results supplement the literature regarding financing decisions and operating performance. We organize the rest of the paper as follows. Section 2 discusses the institutional background on bank ownership and presents the hypotheses. Section 3 presents the data sample. Section 4 presents the empirical results, and Section 5 concludes the paper.

2. Institutional background and hypotheses development 2.1. Political connection and sources of discrimination Political connections result in significant benefits to firms. Faccio (2006) suggests that political connections can add firm value with respect to market returns. Faccio et al. (2006) argue that government is more likely to bail out politically connected firms than similar unconnected firms. Among the benefits that result from political connections, the preferential access to financial resources, primarily bank lending, is the most important one. La Porta et al. (2002) find that government ownership of banks is large and pervasive around the world and is more common in countries with interventionist governments. This suggests that governments have the ability to intervene in bank lending in many countries. Dinc (2005) also shows that there is substantial political influence on bank lending in emerging markets. Using data from Indonesia, Leuz and Oberholzer-Gee (2006) suggest that, compared to those with fewer political connections, firms with strong political connections are less likely to be listed in the foreign security market because they are more likely to receive sufficient funding from the domestic capital market. Using Brazilian data, Claessens et al. (2008) find that firms with political connections established through campaign contributions have preferential access to bank financing. Johnson and Mitton (2003) suggest that Malaysian capital controls provide support for politically connected firms. Khwaja and Mian (2005) find that in Pakistan, firms with political connections borrow 45% more from banks than do other firms. Using Italian data, Sapienza (2004) finds that the stronger the political party in the area where the firm is borrowing, the lower the interest rates. The evidence above shows pervasive bank discrimination against firms with fewer political connections around the world and especially in emerging economies. This type of bank discrimination is also very common in China. Based on the nature of the ultimate controlling shareholder, a Chinese firm can be classified as either an SOE or a non-SOE. SOEs have much tighter political connections than non-SOEs for the following reasons: (1) an SOE’s ultimate controlling shareholder is the local or central

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government; (2) a significant portion of SOE profits becomes part of the government’s fiscal income, making the performance of SOEs directly related to government’s performance; (3) SOEs create a significant portion of the jobs that are available nationally, so healthy SOE development is important in maintaining a peaceful society; and (4) government officials can directly or indirectly intervene in the operation, investment, and financing decisions of SOEs, so SOE performance can create significant political capital for government officials. For these reasons, SOEs have more political connections than do non-SOEs. Such political connections yield advantages for SOEs in terms of operations, investment, and financing. For example, non-SOEs face quite a few barriers when entering profitable industries such as telecommunications, energy, and medical services. SOEs receive priority treatment when becoming involved in governmental investment projects such as the construction of railroads and highways. For political reasons, non-SOEs suffer bank discrimination when seeking financing. First, the government can require banks to engage in ‘‘policy lending’’ for the benefit of SOEs. Cull and Xu (2003) indicate that for a long time, SOEs have been able to procure bank loans easily regardless of firm performance. Second, banks regard loans to SOEs as much safer because SOEs enjoy more protection from the government. The government bails out SOEs if they are experiencing financial problems because worker layoffs resulting from SOE bankruptcy can lead to civil unrest (Wang et al., 2008). Third, government officials have an incentive to assist SOEs with funding because successful SOEs provide the government with more resources, enhancing the political capital of government officials and increasing their chances of receiving promotions (Kornai, 1993; Qian, 1994; Li and Zhou, 2005; Wang et al., 2008). Finally, bank managers have significant incentives to maintain good relationships with government officials through loans to SOEs. Government officials can influence the selection of bank managers, especially at state-owned banks. In addition, bank managers also enjoy other private benefits. For example, government officials can use their political power to help arrange jobs for relatives of bank managers or facilitate entry into the party (Brandt and Li, 2003). Although China has had an established stock market for eighteen years, bank loans are still the major financial source for Chinese firms (Allen et al., 2005). In addition, Wang et al. (2008) argue that SOEs are given preferential access to the stock market, and Su and Yang (2009) find empirical evidence that non-SOEs are also discriminated against in seeking seasoned equity offerings. Given such financial disadvantages, non-SOEs are more likely to experience financial distress. SOEs and non-SOEs in China provide us with a natural setting for investigating the issues of political connections and bank discrimination. 2.2. Related lending and holding ownership in banks In addition to their political connections, bank insiders also enjoy the ability to encourage banks to issue loans with favorable terms. Such lending is called ‘‘related lending’’ or ‘‘insider lending’’. Laeven (2001) develops a theoretical model to show that because large bank shareholders can fire bank managers, such shareholders can use their power to ensure that bank managers engage in related lending. La Porta et al. (2003) argue that in many countries, banks tend to lend to firms related to them. Charumlind et al. (2006) find that firms with bank connections have greater access to long-term debt and need less collateral. Using data from Mexico, Maurer and Haber (2007) argue that banks tend to engage in related lending because the information asymmetry is low between the bank and the firms, which are also shareholders of the bank. In China, holding an ownership stake of more than 5% in a bank can entitle the holding firm to assign representatives to the bank’s board of directors. These representatives can influence the

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decision-making process for lending and thus benefit the holding firm.2 In summary, building economic bonds with a bank and specifically holding an ownership stake in a bank can help a firm obtain related lending from the bank. Therefore, we predict that to eliminate bank discrimination, non-SOEs in China are more likely to choose to hold ownership in banks. 2.3. Regulations on holding bank ownership in China Although Chinese government has restrictively regulated the bank industry for a long time, since the last decade, it began to allow most legal entities to invest in banks. When a firm has more than 5% ownership of a bank, the regulation regards the firm and the bank as ‘‘related parties’’, and the firm will be able to obtain a ‘‘related loan’’ from the bank. The China Banking Regulatory Commission (CBRC) has established the following important regulations for ‘‘related loans’’: (1) a bank is required to establish a ‘‘Related Loan Committee’’ to supervise and report related lending; (2) collateral is required for ‘‘related loans’’; (3) the ratio of the credit line to the directly related firm cannot exceed 10% of the bank’s equity balance; and the ratio of the credit line to the corporate group controlled by the related firm cannot exceed 15% of the bank’s equity balance. Even though the CBRC imposes restrictions on related lending, holding a 5% ownership of a bank can still help to eliminate financial constraints because related firms can obtain 10% of the bank’s equity as a line of credit. However, because the regulations require these firms to be profitable in the three years prior to their investment in the bank ownership, we argue that the purpose of bank ownership is to avoid future financial constraints or distress rather than to resolve current financial issues. 2.4. Hypotheses development As we discussed above, banks discriminate against non-SOEs for political reasons, thus non-SOEs have a strong propensity to build economic bonds with banks through holding bank ownership.3 Therefore, we hypothesize the following: H1. As compared to SOEs, non-SOEs have a greater propensity to hold significant bank ownership. However, there are other reasons for firms to desire partial bank ownership besides the goal of reducing bank discrimination. First, financial services and commercial banks are profitable, so firms with excessive funding have an incentive to invest in this profitable industry. Second, the Chinese government restrictively regulates financial services industry, so holding bank ownership is an appropriate way for firms to strategically enter this industry. Although other motivations for bank ownership cannot be ruled out, we can still seek to verify that related lending is an incentive for investing in an ownership stake in a bank. Consistent with H1, we predict that if related lending is important for non-SOEs, then we should see them experience a direct benefit of related lending: interest reduction (La Porta et al., 2003; Charumlind et al., 2006; Maurer and Haber, 2007). Therefore, we hypothesize the following: 2 In the sample used in our paper, approximately 80% of firms holding more than 5% bank ownership stakes directly assign representatives to the relevant bank’s board of directors. 3 Although it is possible for banks to also choose certain industrial firms as investors, it remains the case that compared to SOEs, non-SOEs have greater incentives to build relationships with banks. Banks are more likely to select SOEs as strategic investors because SOEs have greater economic and political power, which can bolster a bank’s business. If we eventually observe that non-SOEs are more likely to hold bank ownership, then the results will suggest that SOEs are not willing to hold bank ownership, whereas non-SOEs have strong incentives to do so even if they are not the priority investor candidates. This concept is consistent with our argument.

H2. As compared to non-SOEs that do not hold significant bank ownership, non-SOEs that do hold significant bank ownership pay lower interest expenses on bank loans of the same amount. Bates et al. (2009) and Han and Qiu (2007) argue that as compared to firms without financial constraints, firms with financial constraints are more likely to hold precautionary cash when they anticipate a potential cash shortage in the near future. We argue that when the monetary policy of the central bank becomes stricter, the potential cash shortage in the near future becomes more pronounced. Hence, the non-SOEs with economic bonds with banks are more likely to avoid precautionary cash holding when the monetary policy tightens, and they are likely to obtain shortterm loans more easily than do other non-SOEs after these policy changes. We therefore hypothesize the following: H3a. As compared to non-SOEs that do not hold significant bank ownership, non-SOEs that hold significant bank ownership are less likely to increase their cash holdings when the monetary policy of the central bank becomes stricter. H3b. As compared to non-SOEs that do not hold significant bank ownership, non-SOEs that hold significant bank ownership are more likely to increase the amount of their short-term loans soon after the monetary policy of the central bank becomes stricter. 3. Data sample and descriptive statistics The WindDB (Wind Financial Database) provides bank shareholder data for the period from 2006 to 2008, so we make this our sample period. We obtain financial data from the China Stock Market Trading Database (CSMAR), which is a leading integrated financial data service provider in China. For the sample firms with available financial data, we obtain the controlling shareholder data from the Center of China Economic Research Services Database (CCER), at which point we classify firms as SOEs if their largest shareholder is the state and as non-SOEs if their largest shareholder is an entity other than the state. Because the regulations regard firms with at least 5% ownership as related parties, we choose 5% ownership of a single bank as the threshold for significant economic bonds between a firm and a bank.4 We focus only on non-financial firms when we identify the firms holding significant bank ownership. Panel A of Table 1 reports the industry distribution of the final sample used to test the hypotheses. The sample is comprised of 4001 firm-year observations, including 111 firm-years (48 for non-SOEs and 63 for SOEs) in which the firms had at least 5% bank ownership. Except for the mining, timber and furniture, communication and cultural industries, all industries contained firms with significant ownership stakes in banks.5 Hence, holding significant ownership in China appears very popular in our sample, which is therefore consistent with the findings of prior studies.6 4 According to the ‘‘Information Disclosure by Commercial Banks’’ regulations, since 2007, banks have been required to disclose which shareholders hold 5%+ ownership shares because these are regarded as large shareholders that can influence bank policymaking and operation decisions. 5 These three industries do not contain firms holding significant bank ownership shares, possibly because these industries have very restrictive barriers for non-SOE entry and SOEs do not have an incentive to hold significant bank ownership shares. 6 In our analyses, we focus on the firms listed in the Chinese stock market. It might be argued that these listed firms are less likely to suffer financial constraints and thus that even non-SOEs in this class are not motivated to build economic relationships with banks. However, we argue the major financing source for these non-SOE firms remains banks, as argued in Allen et al. (2005, 2008). Without sufficient support from banks, these non-SOEs are still likely to experience financial constraints. In our sample, the average financial leverage of non-SOEs is 64.70%, whereas that of SOEs is 54.48%. In addition, if the non-SOEs are not financially constrained when they are listed, this will bias against finding the results is consistent with the hypotheses.

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Table 1 Descriptive statistics. The sample period is from 2006 to 2008. The sample includes 4001 firm-years of A Share listed in the Shanghai Stock Exchange (SHSE) and Shenzhen Stock Exchange (SZSE), where 111 firm-years hold significant ownership of banks. We classify industry using ‘‘Guidelines on Industry Classification of Listed Companies’’ issued by the China Securities Regulatory Commission (CSRC). Industry

Sample firms

Firms holding more than 5% of a bank’s total ownership

Number of firm-years Panel A: Industry distribution of sample firm-years Agriculture, forestry, livestock farming, fishery (A) Mining (B) Food and beverage (C0) Textile, clothes, and fur (C1) Timber and furniture (C2) Paper making and printing (C3) Petroleum, chemistry, rubber, and plastic (C4) Electronic (C5) Metal and non-metal (C6) Machinery, equipment, and instrument (C7) Medicine and biological products (C8) Other manufacturing (C9) Electric power, gas and water production (D) Construction (E) Transport and storage (F) Information technology (G) Wholesale and retail trade (H) Real estate (J) Social service (K) Communication and cultural industry (L) Comprehensive (M) Total (Firm-year observation)

Year

Panel B: Distribution of sample firms 2006 2007 2008 Sub-total

Number of firm-years

102 63 170 184 11 78 433 143 362 629 271 54 182 81 175 257 271 167 112 30 226

2.55 1.57 4.25 4.60 0.27 1.95 10.82 3.57 9.05 15.72 6.77 1.35 4.55 2.02 4.37 6.42 6.77 4.17 2.80 0.75 5.65

3 0 11 9 0 2 16 1 9 12 5 4 8 1 3 1 6 9 4 0 7

2.70 0.00 9.91 8.11 0.00 1.80 14.41 0.90 8.11 10.81 4.50 3.60 7.21 0.90 2.70 0.90 5.41 8.11 3.60 0.00 6.31

4001

100.00

111

100.00

Firms holding less than 5% of a bank’s total ownership

Non-SOEs

SOEs

Non-SOEs

SOEs

19 14 15 48

23 18 22 63

429 447 478 1354

841 836 859 2536

111 2.77%

3890 97.23%

Firms holding more than 5% of a bank’s total ownership

Firms holding less than 5% of a bank’s total ownership

Mean

Median

Mean

Median

0.0155 0.0308 0.5599 0.1837 0.0480 0.0147 0.0000 0.0000 0.5428 21.81 0.0272 0.2758 0.1374

0.0172 0.0207 0.8253 0.1939 0.0720 0.0083 0.0149 0.3481 0.5715 21.32 0.0149 0.3134 0.2373

0.0147 0.0255 0.6514 0.1674 0.0141 0.0000 0.0000 0.0000 0.5365 21.24 0.0248 0.2871 0.1502

Panel C: Statistic comparison FINFEEt 0.0165 ROAt 0.0332 SALESt 0.7033 SHORTt 0.2022 LONGt 0.1037 DSHORTt 0.0292 DLONGt 0.0196 NONSOEt 0.4324 LEVt1 0.5302 SIZEt1 21.81 ROAt1 0.0296 AMt1 0.3143 GROWTHt1 0.2034

Percent (%)

Firms holding more than 5% of a bank’s total ownership

Total Percentage

N

Percent (%)

111

t-Tests mean

Wilcoxon rank tests Median

0.63 2.25** 2.36** 0.68 2.31** 2.13** 0.61 1.84* 2.22** 5.70*** 2.82*** 0.05 0.76

0.68 1.64 0.86 1.83* 2.99*** 1.87* 1.18 1.84* 0.10 4.53*** 1.06 0.67 0.09

3890

Variable definition: FINFEE is interest expense divided by total asset. ROA is return on asset, equal to net income divided by total asset. SALES is equal to net sales divided by total asset in the beginning of the year. SHORT is equal to short-term bank loan divided by total asset at the end of the year. LONG is equal to long-term bank loan divided by total asset at the end of the year. DSHORT is equal to short-term bank loan change divided by total asset at the beginning of the year. DLONG is equal to long-term bank loan change divided by total asset at the beginning of the year. NONSOE is an indicator variable that is equal to one if a firm is a non-SOE, and zero otherwise. LEV is financial leverage, equal to total debt divided by total asset. SIZE is firm size, equal to natural log of total asset. AM is equal to PP&E divided by total asset. GROWTH is sales growth, equal to the difference between this year’s sale and the previous year’s sale divided by the previous year’s sale. *** Significance at the 1% level. ** Significance at the 5% level. * Significance at the 10% level.

According to Panel B of Table 1, in approximately 2.77% of the firm-years in the full sample, firms hold more than 5% of a bank’s

total ownership. Of these, 48 (63) firm-years are for non-SOEs (SOEs). Accordingly, we can find that in 1354 non-SOE (2536

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Table 2 Pearson correlation matrix.

FINFEEit ROAit SALESit NONSOEit BANKDEBTit1 LEVit1 SIZEit1 ROAit1 AMit1 GROWTHit1

HOLDit

FINFEEit

ROAit

SALESit

NONSOEit

BANKDEBTit1

LEVit1

SIZEit1

ROAit1

AMit1

0.006 0.020 0.030* 0.029* 0.036** 0.017 0.074*** 0.023 0.001 0.008

0.192*** 0.046*** 0.096*** 0.575*** 0.586*** 0.108*** 0.374*** 0.206*** 0.048***

0.179*** 0.026* 0.082*** 0.057*** 0.078*** 0.263*** 0.012 0.115***

0.094*** 0.162*** 0.049*** 0.140*** 0.184*** 0.048*** 0.105***

0.037** 0.130*** 0.301*** 0.095*** 0.156*** 0.008

0.512*** 0.151*** 0.149*** 0.163*** 0.170***

0.161*** 0.420*** 0.015 0.027*

0.210*** 0.138*** 0.082***

0.018 0.206***

0.040**

The sample period is from 2006 to 2008. The sample includes 4001 firm-years of A Share listed in the Shanghai Stock Exchange (SHSE) and Shenzhen Stock Exchange (SZSE), where 111 firm-years hold significant ownership of banks. Variable definition: HOLD in an indicator variable that equals one if a firm holds more than 5% of a bank’s total ownership, and zero otherwise. FINFEE is interest expense divided by total asset. ROA is return on asset, equal to net income divided by total asset. SALES is equal to net sales divided by total asset at the beginning of the year. BANKDEBT is equal to total bank loan (including short-term debt and long-term debt) divided by total asset. NONSOE is an indicator variable that is equal to one if a firm is a non-SOE, and zero otherwise. LEV is financial leverage, equal to total debt divided by total asset. SIZE is firm size, equal to natural log of total asset. AM is equal to PP&E divided by total asset. GROWTH is sales growth, equal to the difference between this year’s sale and the previous year’s sale divided by the previous year’s sale. *** Significance at the 1% level. ** Significance at the 5% level. * Significance at the 10% level.

SOE) firm-years, firms hold less than 5% bank ownership. The small sample of firm-years in which firms hold more than 5% of total bank ownership is one limitation of our study. In the robustness tests, we also choose 1% as the cutoff point, the percentage of firms holding more than 1% of a bank’s total ownership becomes 7.57%. We will discuss the robustness tests to address this limitation in Section 4.4.7 Panel C of Table 1 presents the comparison descriptive statistics for the sample. About 43% of firms which hold significant ownership of banks are non-SOEs, but only about 35% of firms which do not hold significant ownership of banks are non-SOEs. Thus, based on a univariate comparison, we can conclude that non-SOEs are more likely to hold significant ownership stakes in banks. In addition, firms with significant bank ownership tend to be larger, to be more profitable, and to have more long-term debt. Table 2 presents the Pearson correlation matrix of the firms’ major characteristics. We find that non-SOEs on average have higher interest expenses and worse operating performance in terms of ROA and sales. 4. Empirical tests 4.1. Do non-SOEs have a greater propensity to hold significant bank ownership? H1 predicts that compared to SOEs, non-SOEs have a greater propensity to hold significant bank ownership. To identify the firms holding significant bank ownership, we use an indicator variable, HOLD, which equals one if a firm holds more than 5% of a bank’s total ownership, and zero otherwise. We identify the nonSOEs using an indicator variable, NONSOE, which is equal to one if a firm is a non-SOE, and zero otherwise. We run a Probit regression on the following model to test H1.

HOLDit ¼ a0 þ a1 NONSOEit þ a2 LEVit1 þ a3 SIZEit1 þ a4 ROAit1 þ a5 GROWTHit1 þ a6 MARKET þ Industry Effect þ Year Effect þ eit

ð1Þ

LEV is financial leverage, equal to total debt divided by total assets. We include this variable to control for the effect of existing financial distress on the propensity to hold bank ownership. SIZE 7

We thank the referee for this suggestion.

represents firm size and is equal to the natural log of total assets. Large firms are more likely to have the financial capacity to hold significant bank ownership. ROA is return on assets and is equal to net income divided by total assets. We include ROA to control for the effect of firm performance because compared to firms with poor performance, firms with better performance are more likely to be able to invest in banks. We also control for sales growth, GROWTH, which is equal to the difference between this year’s sales and the previous year’s sales divided by the previous year’s sales. MARKET is the development index for the regional market; when the index is higher, the regional market is more developed.8 We control for the development of regional markets because in more developed regions, it is easier for firms to obtain long-term bank loans and other financial resources (Lin et al., 2009). Thus, firms in developed regions have lower incentives to hold bank ownership stakes. We also control for the industry effect and year effect. In H1, we predict a1 to be positive. The first column of Table 3 reports the results obtained by estimating model (1) for the full sample. The coefficient of NONSOE is 0.3072 (p-value = 0.029), suggesting that compared to SOEs, nonSOEs are more likely to build economic bonds with banks through holding significant ownership. This finding is consistent with the notion that although non-SOEs are discriminated against for political reasons, they can eliminate such discrimination using an economic approach. The coefficients on the control variables are not significant, except for that of SIZE. Consistent with our prediction, the coefficient for SIZE suggests that larger firms are more likely to hold significant bank ownership shares. Because we focus on the non-SOEs and SOEs listed in the stock market, there is an alternative explanation for these results. The non-SOEs with bank ownership, which is associated with a deep financial backing by banks, are more likely to be approved for listing on the stock exchange, whereas the listed SOEs do not need 8 Our data on the extent of institutional development across regions in China comes from the National Economic Research Institute’s (NERI) marketization index (e.g., Fan et al., 2010). The index captures the following aspects of regional market development: (1) relationships between government and markets, (2) the development of non-state sectors in the economy, (3) the development of product markets, (4) the development of factor markets, and (5) the development of market intermediaries and the legal environment. This index has been widely used in the literature (e.g., Wang et al., 2008; Firth et al., 2009; Li et al., 2009) to measure regional institutional development; a higher index score generally suggests greater institutional development. In this paper, we cite the marketization index for the regions where the headquarters of the listed firms are located.

Z. Lu et al. / Journal of Banking & Finance 36 (2012) 341–354

347

Table 3 The association between non-SOEs and holding bank ownership. The following model is estimated with Probit regression:

indicate the causal relationship between non-SOEs and holding bank ownership.10

HOLDit ¼ a0 þ a1 NONSOEit þ a2 LEVit1 þ a3 SIZEit1 þ a4 ROAit1

4.2. Does holding significant bank ownership create direct benefits from related lending?

þ a5 GROWTHit1 þ a6 MARKET þ Industry Effect þ Year Effect þ eit Dependent variable

ð1Þ

HOLDit Full sample

Matched sample

Industry effect Year effect

6.6150*** (0.000) 0.3072** (0.029) 0.164 (0.447) 0.2319*** (0.000) 0.5881 (0.340) 0.1112 (0.164) 0.0436 (0.170) Controlled Controlled

11.4996*** (0.000) 1.0577*** (0.000) 0.8363 (0.197) 0.4088*** (0.000) 0.4619 (0.719) 0.0487 (0.606) 0.0326 (0.536) Controlled Controlled

Observations Pseudo R2

4001 0.0553

2672 0.1960

Constant NONSOEit1 LEVit1 SIZEit1 ROAit1 GROWTHit1 MARKET

Variable definition: HOLD in an indicator variable that equals one if a firm hold more than 5% of a bank’s total ownership, and zero otherwise. NONSOE is an indicator variable that is equal to one if a firm is a non-SOE, and zero otherwise. LEV is financial leverage, equal to total debt divided by total asset. SIZE is firm size, equal to the natural log of total asset. ROA is return on asset, equal to net income divided by total asset. GROWTH is sales growth, equal to the difference between this year’s sale and the previous year’s sale divided by the previous year’s sale. MARKET is the index of development of regional market; where the index is higher, the regional market is more developed. Note: p-Value is reported in parentheses. ⁄ Significance at the 10% level. ** Significance at the 5% level. *** Significance at the 1% level.

bank ownership to receive financial backing. Thus, it is manifested that in the stock market, the non-SOEs are more likely to hold bank ownership than are the SOEs. To rule out this alternative explanation and verify the causal relationship between non-SOEs and holding bank ownership, we form a subsample in which we match the non-SOEs with SOEs using the propensity scores. In conducting propensity score matching, we tend to match the non-SOEs with SOEs according to their likelihood of being listed on the stock exchange. We estimate the propensity scores using each firm’s previous performance and its major characteristics, which are the important determinants of whether a firm can be listed in the Chinese stock market. We choose the following factors: leverage in the previous year, return on asset in the previous two years, sales growth in the previous two years, the previous year’s investment income scaled by total assets, the development index of the regional market, and the natural log of the population in the province where the firm’s headquarters are located. Then we use the following criteria to match the non-SOEs with SOEs: (1) the same year; (2) the same industry; and (3) the closest propensity score.9 Next, we estimate Eq. (1) using Probit regression for the matched sample. The second column of Table 3 reports the results obtained by estimating Eq. (1) for the matched sample. The coefficient on NONSOE is 1.0577 (p-value = 0.000), suggesting that non-SOEs are more likely to hold significant bank ownership than are SOEs with similar previous performance and firm characteristics. These results

9

We use the replacement method in the propensity score matching process.

4.2.1. Benefits from reduced interest expenses In the hypotheses development section, we discuss several reasons for holding bank ownership and the need to verify that related lending is still an important one. First, we predict that among the non-SOEs that banks discriminated against, firms holding significant bank ownership pay lower interest expenses. To test H2 using a subsample of non-SOEs, we estimate the OLS regression for the following models. Standard errors are corrected with the Huber– White procedure by clustering on firm.

FINFEEit ¼ b0 þ b1 HOLDit þ b2 BANKDEBTit1 þ b3 HOLDit  BANKDEBTit1 þ b4 NOBANKDEBTit1 þ b5 SIZEit1 þ b6 ROAit1 þ b7 GROWTHit1 þ b8 CASHit1 þ Industry Effect þ Year Effect þ eit

ð2Þ

FINFEE represents the firm’s net interest expenses (interest expenses minus interest revenue) divided by its total assets. We cannot obtain the raw interest expense data due to the unavailability of those data in the CSMAR. We argue that net interest expenses can be regarded as a proxy for raw interest expenses because net interest expense is highly correlated with raw interest expense. BANKDEBT is equal to total bank loans (including short-term debt and long-term debt) divided by total assets. NOBANKDEBT is nonloan debt divided by total assets. CASH is cash holdings divided by total assets, controlling for the need to obtain a loan. The variables HOLD, SIZE, ROA, and GROWTH are defined as in model (1). H2 predicts b3 to be negative. Ideally, to test H2, we should focus on the bank in which the firm holds an ownership stake. Because such data are unavailable, we must use the data on interest expenses for all loans borrowed from all banks and the data on loans borrowed from all banks. We make this approximation based on the assumption that firms prefer to borrow from banks with which they have relationships. Under this assumption, interest expenses and loans are likely to result primarily from ‘‘related lending’’. The first column of Table 4 reports the results of the estimation of model (2) using the subsample that only includes non-SOEs. The coefficient on HOLD  BANKDEBT is 0.0167 (p-value = 0.017), suggesting that given the same amount of loan holdings, compared to non-SOEs without significant bank ownership, those holding significant ownership have lower interest expenses.11 These results are consistent with H2. The coefficient on BANKDEBT is positive, suggesting that higher bank loans are associated with higher interest expenses as predicted. We also use this model for the sample that includes only SOEs and for the pooled sample. The results in the second and third columns show that for SOEs, holding significant bank ownership can marginally reduce interest expenses, consistent with the notion that SOEs have preferential access to various financial resources 10 We also test causality using the instrument variable approach, and our conclusions do not change. We estimate the instrumental variable, the probability of NONSOE, using the following regressors: leverage in the previous year, return on assets in the previous two years, sales growth in the previous two years, the previous year’s investment income scaled by total assets, the index of development of the regional market, and the natural log of the population in the province where the firm’s headquarters are located. Then we estimate model (1) by replacing NONSOE with the probability for NONSOE. The coefficient on the probability for NONSOE is 2.3574 (p-value = 0.037). 11 In this paper, we assume that debt is bank loans because publicly available databases do not provide information on bank loans. However, this approach will add noise to the regression and bias against finding the results consistent with H2.

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Z. Lu et al. / Journal of Banking & Finance 36 (2012) 341–354

Table 4 Effect of holding bank ownership on interest expense. The following model is estimated with OLS regression:

FINFEEit ¼ b0 þ b1 HOLDit þ b2 BANKDEBTit1 þ b3 HOLDit  BANKDEBTit1 þ b4 NOBANKDEBTit1 þ b5 SIZEit1 þ b6 ROAit1 þ b7 GROWTHit1 þ b8 CASHit1 þ Industry Effect þ Year Effect þ eit Dependent variable

ð2Þ

FINFEEit Non-SOEs

SOEs

Pooled

0.0284 (0.004) 0.0038* (0.078) 0.0407*** (0.000) 0.0167** (0.017) 0.0002 (0.922)

0.0327 (0.000) 0.0048 (0.134) 0.0347*** (0.000) 0.0132* (0.091) 0.0006 (0.691)

Industry effect Year effect

0.0007 (0.136) 0.0369*** (0.000) 0.0009 (0.128) 0.0210*** (0.000) Controlled Controlled

0.0008*** (0.003) 0.0307*** (0.000) 0.0015*** (0.005) 0.0285*** (0.000) Controlled Controlled

0.0310*** (0.000) 0.0042* (0.069) 0.0366*** (0.000) 0.0140** (0.035) 0.0004 (0.787) 0.0007 (0.145) 0.0008*** (0.001) 0.0338*** (0.000) 0.0013*** (0.001) 0.0257*** (0.000) Controlled Controlled

Observations R-squared

1313 0.5159

2515 0.5039

3828 0.5051

Constant HOLDit BANKDEBTit1 HOLDit  BANKDEBTit1 NOBANKDEBTit1

***

***

NONSOEit SIZEit1 ROAit1 GROWTHit1 CASHit1

Variable definition: FINFEE is interest expense divided by total asset. HOLD in an indicator variable that equals one if a firm holds more than 5% of a bank’s total ownership, and zero otherwise. NONSOE is an indicator variable that is equal to one if a firm is a non-SOE, and zero otherwise. BANKDEBT is equal to total bank loan (including short-term debt and long-term debt) divided by total asset. NOBANKDEBT is non-loan debt divided by total asset. SIZE is firm size, equal to the natural log of total asset. ROA is return on asset, equal to net income divided by total asset. GROWTH is sales growth, equal to the difference between this year’s sale and the previous year’s sale divided by the previous year’s sale. CASH is cash holding divided by total asset. Note: p-Value is reported in parentheses. * Significance at the 10% level. ** Significance at the 5% level. *** Significance at the 1% level.

and thus the benefits that they accrue by building bonds with banks are marginal. For all firms, holding bank ownership is associated with lower interest expenses on average. 4.2.2. Benefits from avoiding precautionary cash holding and obtaining short-term loans when the macroeconomic policy is unfavorable to bank lending H3a and H3b predict the benefits from avoiding precautionary cash holding and obtaining short-term loans for related firms. Bernanke and Blinder (1992), Leary (2009), and Sun et al. (2010) note that the monetary policy of the central bank can significantly influence bank lending behavior. When the monetary policy is tighter, banks experience more lending constraints. The firms that banks discriminate against are even less likely to obtain loans from banks under these circumstances, so they are more likely to increase precautionary cash holdings when the monetary policy is tight. However, if a firm can obtain a high credit line from a bank with which it has a relationship, then that firm will be less concerned about the possibility of cash shortages even when the monetary policy is stricter. As we discussed in Sections 2.2 and 2.3, non-SOEs that hold significant bank ownership of banks can get a credit line of up to 10% of the bank’s equity. Accordingly, if holding bank own-

ership can reduce bank discrimination, then as compared to other non-SOEs, the non-SOEs holding significant bank ownership should be less likely to increase their cash holdings when the central bank monetary policy becomes tighter because the firms are more likely to rely on short-term bank loans in such circumstances. We use monetary policy sentiment, MC, as a sentiment index for bankers. These data come from a quarterly survey jointly conducted by the People’s Bank of China (PBC) and the National Bureau of Statistics (NBS). The monetary policy sentiment index indicates the proportion of bankers who consider the government’s monetary policy to be ‘‘too loose’’, ‘‘loose’’, ‘‘appropriate’’, ‘‘tight’’, ‘‘too tight’’, or ‘‘unsure’’. We use ‘‘tight’’ as the measure of MC. The higher the MC, the tighter the monetary policy will be.12 Because the monetary policy sensitivity data are reported quarterly and because cash holdings obviously exhibit seasonality, we use quarterly data to test H3a. Accordingly, we test H3a using the following model (3):

DCASHitj ¼ b0 þ b1 HOLDitj þ b2 MCtj þ b3 HOLDitj  MCtj þ b4 SMBitj þ b5 SSIZEitj þ b6 SCFitj þ Industry Effect þ Year Effect þ eit

ð3Þ

DCASH is (cash holdings and cash equivalent in quarter j of year t – cash holdings and cash equivalent in year t  1)/assets in the quarter j  1 of year t. SMB is (the market value of equity in quarter j of year t + book value of debt in quarter j of year t)/the book value of assets in quarter j of year t. SSIZE is the natural log of assets in quarter j  1 of year t, and SCF is the cash flow from operations in quarter j of year t divided by assets in quarter j  1 of year t. H3a predicts that in the subsample of non-SOEs, b3 is negative. The results presented in Panel A of Table 5 report that in the subsample of non-SOEs, the coefficient on HOLD  MC is 0.0959 (p-value = 0.002), consistent with H3a. Similarly, the coefficient on MC is 0.0685 (p-value = 0.001), consistent with the notion that when the monetary policy tightens, non-SOEs are more likely to increase their cash holdings to avoid cash shortages in the near future. The results presented in column 2 suggest that for SOEs, monetary policy and the decision to hold significant bank ownership do not affect their incentives to increase their cash holdings. These findings are consistent with our argument that SOEs have better access to various financial resources. Finally, column 3 shows that for all firms, on average, a tighter monetary policy encourages firms to hold more cash; however, we also see that firms with relationships with banks have comparatively less of an incentive to do so. We test H3b using model (4) as follows:

DSHORTitj ¼ b0 þ b1 HOLDitj þ b2 MCtj þ b3 HOLDitj  MCtj þ b4 SMBitj þ b5 SSIZEitj þ b6 SCFitj þ b7 SLEVitj þ Industry Effect þ Year Effect þ eit

ð4Þ

DSHORT is the change in short-term bank loans from the end of quarter j to the end of quarter j + 2, scaled by the total assets in quarter j. The other variables are defined as in the model (3). Panel B of Table 5 indicates that in the subsample of non-SOEs, the coefficient 12 The design of the questionnaire and the formulation of the survey plan, the index system, and the calculation methodologies were jointly conducted by the Department of Statistics and Analysis of the PBC and the Service Industry Survey Center of the NBS. The Department of Statistics and Analysis of the PBC is responsible for conducting surveys, analyzing data, and preparing survey reports. The survey results are jointly released by the PBC and the NBS. The surveys include a comprehensive sampling survey of approximately 2850 banking institutions above the prefecture level and a PPS (probability proportional to size) sampling survey of rural credit cooperatives. The surveys target senior managers at the head offices of banking institutions (including foreign commercial banks) and presidents or deputy presidents responsible for credit business in branches or sub-branches at the above institutions.

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Z. Lu et al. / Journal of Banking & Finance 36 (2012) 341–354 Table 5 Effect of holding bank ownership on cash holding and short term loan when monetary policy is tight. The following model is estimated with OLS regression:

DCASHitj ¼ b0 þ b1 HOLDitj þ b2 MCtj þ b3 HOLDitj  MCtj þ b4 SMBitj þ b5 SSIZEitj þ b6 SCFitj þ Industry Effect þ Year Effect þ eit

ð3Þ

DSHORTitj ¼ b0 þ b1 HOLDitj þ b2 MCtj þ b3 HOLDitj  MCtj þ b4 SMBitj þ b5 SSIZEitj þ b6 SCFitj þ b7 SLEVitj þ Industry Effect þ Year Effect þ eit

ð4Þ

Dependent variable

DCASHit Non-SOEs

Panel A: Effect of holding bank ownership on cash holding when monetary policy is tight Constant 0.1661*** (0.008) HOLDitj 0.0444*** (0.001) MCtj 0.0685*** (0.000) HOLDitj  MCtj 0.0959*** (0.002) NONSOEitj

SOEs

Pooled

0.0889*** (0.009) 0.0012 (0.925) 0.0490*** (0.000) 0.0164 (0.623)

Industry effect Year effect

0.0003 (0.255) 0.0064** (0.030) 0.3109*** (0.000) Controlled Controlled

0.0004** (0.042) 0.0038** (0.017) 0.2961*** (0.000) Controlled Controlled

0.1219*** (0.000) 0.0191** (0.049) 0.0554*** (0.000) 0.0509** (0.033) 0.0029 (0.289) 0.0003* (0.064) 0.0050*** (0.000) 0.3014*** (0.000) Controlled Controlled

Observations R-squared

4519 0.0671

8856 0.0613

13,375 0.0585

SMBitj SSIZEitj SCFitj

Panel B: Effect of holding bank ownership on short term loan when monetary policy is tight Constant 0.0461* 0.0366* (0.077) (0.057) HOLDitj 0.0120 0.0022 (0.165) (0.815) MCtj 0.0183* 0.0185** (0.069) (0.036) HOLDitj  MCtj 0.0478** 0.0110 (0.043) (0.668) NONSOEitj

Industry effect Year effect

0.0004*** (0.001) 0.0019 (0.132) 0.0182 (0.303) 0.0198*** (0.001) Controlled Controlled

0.0000 (0.847) 0.0017* (0.062) 0.0316* (0.067) 0.0127*** (0.004) Controlled Controlled

0.0113 (0.494) 0.0040 (0.572) 0.0174** (0.018) 0.0263 (0.183) 0.0002 (0.893) 0.0001* (0.073) 0.0008 (0.288) 0.0239* (0.082) 0.0187*** (0.001) Controlled Controlled

Observations R-squared

3103 0.0243

6396 0.0105

9499 0.0110

SMBitj SSIZEitj SCFitj SLEVitj

Variable definition: DCASH is (cash holding and cash equivalent in quarter j of year t – cash holding and cash equivalent in year t  1)/asset in quarter j  1 of year t. DSHORT is change of short term bank loan from the end of quarter j to the end of quarter j + 2, scaled by the total asset at quarter j. HOLD in an indicator variable that equals one if a firm holds more than 5% of a bank’s total ownership, and zero otherwise. NONSOE is an indicator variable that is equal to one if a firm is a non-SOE, and zero otherwise. MC is the monetary policy sentiment index of bankers, which is taken from a quarterly survey jointly conducted by the People’s Bank of China (PBC) and the National Bureau of Statistics (NBS). The monetary policy sentiment index is the proportion of bankers who consider monetary policy stance as ‘‘too loose’’, ‘‘loose’’, ‘‘appropriate’’, ‘‘tight’’, ‘‘too tight’’, or ‘‘unsure’’. We use ‘‘tight’’ as the measure of MC. The higher the MC, the tighter the monetary policy will be. SMB is (market value of equity in the quarter j of year t + book value of debt in quarter j of year t)/book value of asset in quarter j of year t. SSIZE is natural log of asset in quarter j  1 of year t. SCF is cash flow from operation in quarter j of year t divided by the asset in quarter j  1 of year t. SLEV is financial leverage in quarter j. Note: p-Value is reported in parentheses. * Significance at the 10% level. ** Significance at the 5% level. *** Significance at the 1% level.

on HOLD  MC is 0.0478 (p-value = 0.043), consistent with H3b.13 In addition, we cannot find evidence that SOEs change their short-term bank loans when the monetary policy tightens. This suggests that SOE borrowing behavior is not sensitive to changes in monetary policy. 13 The results are qualitatively similar when we define DSHORT as change in shortterm bank loans from the end of quarter j to the end of quarter j + 1 scaled by the total assets in quarter j.

4.2.3. Robustness tests of H2, H3a and H3b: Using treatment-effect models There may be endogeneity between the direct benefits to firms holding bank ownership and the likelihood of their holding stakes in banks. For example, large, profitable firms are more likely to have significant bank shares, and these firms may also enjoy low interest rates and easy access to bank loans under tighter monetary policies because banks are likely to trust these good performers.

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According to Green (2003), this is a self-selection problem and may lead to biased OLS regression estimates for Eqs. (2)–(4). Therefore, in this section, we test H2, H3a and H3b, using treatment-effect models.14 Using the estimation procedure presented in Green (2003), we estimate a selection model with our binary interest variable (HOLD) as the dependent variable. Then we obtain the estimated coefficients and use them, together with the corresponding variables, to calculate the effect variable, LAMBDA.15 LAMBDA is used for selectivity correction to eliminate selection bias in the OLS regressions for interest. In the next step, we include the LAMBDA estimates in models (2)–(4). A significant coefficient for LAMBDA indicates that an obvious endogeneity problem is affecting the OLS regressions. We define the selection model presented in Eq. (5) by extending Eq. (1), whose control variables are major determinants of whether a firm wishes to hold bank ownership. However, Eq. (2) has the same control variables as in Eq. (1), so we must introduce an instrumental variable into Eq. (5) for differing the two sets of independent variables in the regressions. We choose the resident population as the instrumental variable because there are more banks in more populated provinces and thus, the firms in more populated provinces have more opportunities to hold ownership stakes in their local banks. However, there is little direct relation between population figures and lending.16 PEOPLE is defined as the population (according to year 2005 data) of the province where the company’s headquarters are located.

HOLDit ¼ a0 þ a1 NONSOEit þ a2 LEVit1 þ a3 SIZEit1 þ a4 ROAit1 þ a5 GROWTHit1 þ a6 MARKET þ a7 PEOPLE þ Industry Effect þ Year Effect þ eit

ð5Þ

Panel A of Table 6 reports the estimates for model (5); the coefficients are similar to those in Table 3. In the first column, Panel B of Table 6 presents the estimation for model (2) using the subsample that only includes non-SOEs. The coefficient on HOLD  BANKDEBT is 0.0178 (p-value = 0.087), consistent with the findings in Table 4. Specifically, in the first column, the coefficient on LAMBDA is insignificant, indicating that there is no endogeneity problem for the non-SOEs and that conclusions drawn from Table 3 should be unbiased. In the second column, the coefficient on LAMBDA is significant, indicating that the endogeneity problem for non-SOEs is significant and that a treatment effect model is necessary. Interestingly, after controlling for selection bias, we find that for SOEs, those holding significant bank ownership also receive loans with lower interest rates than those of their counterparts. These results suggest that developing economic bonds with banks can lower the interest expenses of firms, regardless of whether those firms have political connections. Panel C of Table 6 reports the estimation results for model (3) and model (4) obtained using the subsample of non-SOEs. As shown in the column for model (3), the coefficient on HOLD  MC is 0.1013 (p-value = 0.014), consistent with H3a. Similarly, as shown in the column for model (4), the coefficient on HOLD  MC 14 The treatment-effect model considers the effect of an endogenously chosen binary treatment on another endogenous continuous variable, conditional on two sets of independent variables, and the idea of the treatment-effect model is to control the self-selection bias in the estimation of OLS regressions. In our case, the endogenously chosen binary treatment is whether to hold significant bank ownership; the endogenous continuous variables are low interest expense rates and easy access to bank loans when monetary policy is tightened. Through the treatment-effect model, we consider the effect of whether firms are likely to hold bank ownership when we analyze how holding bank ownership can affect their interests and cash holdings. 15 The estimation equation for LAMBDA can be found on pages 784 and 788 of Green (2003). 16 We find that population is significantly associated with the likelihood of holding bank ownership but insignificantly associated with the level of and with changes in bank loans.

is 0.0494 (p-value = 0.048), also consistent with H3b. In addition, the coefficients o LAMBDA are insignificant for both regressions, suggesting that endogeneity problem is not a concern when we investigate the causal relationship between holding bank ownership and firm behavior under tighter monetary policies. Overall, we find robust results in testing H2, H3a and H3b, after controlling for the endogeneity problem. 4.3. Additional analyses 4.3.1. The effects of holding bank ownership on loan holding If the firms that hold bank ownership have higher credit lines and can obtain loans with lower interest rates, then we can conjecture that they are more likely to increase bank loans in their capital structures because the cost of obtaining bank loans is lower for them than it is for firms without bank relationships. We test the association between holding bank ownership and bank loan changes using model (6), which includes the same control variables as in Rajan and Zingales (1995) and Li et al. (2009):

DEBTit ¼ b0 þ b1 HOLDit þ b21 NONSOEit þ b3 HOLDit  NONSOEit þ b4 SIZEit1 þ b5 ROAit1 þ b6 GROWTHit1 þ b7 AMit1 þ b8 MARKET þ Industry Effect þ Year Effect þ eit

ð6Þ

DEBT is the level of bank loans during the fiscal year and includes two separate measures, SHORT and LONG. SHORT is equal to the short-term bank loan change divided by total assets at the beginning of the year. LONG is equal to the long-term bank loan change divided by total assets at the beginning of the year. AM is equal to PP&E divided by total assets. GROWTH is sales growth and is equal to the difference between this year’s sales and the previous year’s sales divided by the previous year’s sales. The variables HOLD, NONSOE, LEV, SIZE, ROA, GROWTH, and MARKET are defined as in previous models. The results presented in Table 7 show that when the dependent variable is SHORT, the coefficients on HOLD and HOLD  NONSOE are not significantly different from zero. This, in turn, suggests that holding a significant bank ownership stake does not influence changes in short-term debt for either SOEs or non-SOEs. When the dependent variable is LONG, the coefficient on HOLD is insignificant, but the coefficient on HOLD  NONSOE is significantly negative. These results suggest that holding bank ownership does not influence long-term debt change but that relative to other non-SOEs, the non-SOEs that hold significant bank ownership reduce long-term debt. We first conjecture that rather than wishing to obtain more debt, non-SOEs seek to build relationships with banks to obtain credit lines, which can improve financial flexibility and help these firms to avoid future financial constraints. This concept is consistent with H3a and H3b. In addition, consistent with H3a and H3b, firms with bank relationships are more likely to obtain short-term loans when necessary. In contrast, due to their lack of financing flexibility, firms without bank relationships may need to consider using long-term loans to finance some of their short-term projects. Thus, we conjecture that as compared to firms without bank relationships, firms with bank relationships have less of a need to shore up their precautionary cash holdings or finance short-term projects using more costly long-term loans. If this conjecture is true, then non-SOEs that hold bank ownership are less likely to change their long-term loans frequently and thus have less volatile long-term loan holdings. Because of the sample constraints, we estimate the volatility of loan holdings using a quarterly rolling window. We test this conjecture using model (7).

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Z. Lu et al. / Journal of Banking & Finance 36 (2012) 341–354 Table 6 Robustness tests of H2, H3a and H3b: Using treatment-effect models.

Table 6 (continued)

HOLDit ¼ a0 þ a1 NONSOEit þ a2 LEVit1 þ a3 SIZEit1 þ a4 ROAit1 þ a5 GROWTHit1 þ a6 MARKET þ a7 PEOPLE þ Industry Effect þ Year Effect þ eit

ð5Þ

.

Dependent variable

Model (3) DCASHitj

Model (4) DSHORTitj

SSIZEitj

0.0079*** (0.001) 0.2864*** (0.000)

Industry effect Year effect

0.0006 (0.964) Controlled Controlled

0.0013 (0.486) 0.0192 (0.379) 0.0211*** (0.000) 0.0086 (0.386) Controlled Controlled

Observations Wald chi2 Prob > chi2

4260 491.75 0.0000

3642 254.10 0.0000

SCFitj Dependent variable

HOLDit SLEVitj

Panel A: First-stage of treatment-effect model Constant

***

Industry effect Year effect

6.6150 (0.000) 0.3072** (0.029) 0.164 (0.447) 0.2319*** (0.000) 0.5881 (0.340) 0.1112 (0.164) 0.0436 (0.170) 0.1983** (0.029) Controlled Controlled

Observations Pseudo R2

4001 0.0553

NONSOEit LEVit1 SIZEit1 ROAit1 GROWTHit1 MARKET PEOPLE

Dependent variable

FINFEEit Non-SOEs

SOEs

Panel B: Second-stage of treatment-effect model: Model (2) Constant 0.0322*** 0.0333*** (0.000) (0.000) HOLDit 0.0088 0.0293*** (0.275) (0.003) BANKDEBTit1 0.0408*** 0.0346*** (0.000) (0.000) HOLDit  BANKDEBTit1 0.0178* 0.0137*** (0.087) (0.000) NOBANKDEBTit1 0.0002 0.0008 (0.843) (0.429) NONSOEit

Pooled

Industry effect Year effect

0.0009** (0.036) 0.0371*** (0.000) 0.0009** (0.019) 0.0209*** (0.000) 0.0023 (0.496) Controlled Controlled

0.0009*** (0.000) 0.0318*** (0.000) 0.0013*** (0.001) 0.0284*** (0.000) 0.0106** (0.011) Controlled Controlled

0.0321*** (0.000) 0.0083 (0.315) 0.0367*** (0.000) 0.0140*** (0.000) 0.0004 (0.594) 0.0006 (0.122) 0.0009*** (0.000) 0.0339*** (0.000) 0.0012*** (0.000) 0.0257*** (0.000) 0.0018 (0.613) Controlled Controlled

Observations Wald chi2 Prob > chi2

1313 1439.06 0.0000

2515 2386.23 0.0000

3828 3940.51 0.0000

SIZEit1 ROAit1 GROWTHit1 CASHit1 LAMBDA

Dependent variable

Model (3) DCASHitj

Model (4) DSHORTitj

Panel C: Second-stage of treatment-effect model: Models (3) and (4) in the subsample of only Non-SOEs Constant 0.1960*** 0.0061 (0.000) (0.878) HOLDitj 0.0426 0.0000 (0.182) (0.280) MCtj 0.0717*** 0.0038 (0.000) (0.762) HOLDitj  MCtj 0.1013** 0.0494** (0.014) (0.048) SMBitj 0.0003* 0.0001 (0.086) (0.357)

LAMBDA

Variable definition – Panel A: HOLD in an indicator variable that equals one if a firm hold more than 5% of a bank’s total ownership, and zero otherwise. NONSOE is an indicator variable that is equal to one if a firm is a non-SOE, and zero otherwise. LEV is financial leverage, equal to total debt divided by total asset. SIZE is firm size, equal to the natural log of total asset. ROA is return on asset, equal to net income divided by total asset. GROWTH is sales growth, equal to the difference between this year’s sale and the previous year’s sale divided by the previous year’s sale. MARKET is the index of development of regional market; where the index is higher, the regional market is more developed. PEOPLE is the population (year 2005 data) of the province where the company’s headquarter locates. Variable definition – Panel B: FINFEE is interest expense divided by total asset. HOLD in an indicator variable that equals one if a firm holds more than 5% of a bank’s total ownership, and zero otherwise. NONSOE is an indicator variable that is equal to one if a firm is a non-SOE, and zero otherwise. BANKDEBT is equal to total bank loan (including short-term debt and long-term debt) divided by total asset. NOBANKDEBT is non-loan debt divided by total asset. SIZE is firm size, equal to the natural log of total asset. ROA is return on asset, equal to net income divided by total asset. GROWTH is sales growth, equal to the difference between this year’s sale and the previous year’s sale divided by the previous year’s sale. CASH is cash holding divided by total asset. Variable definition – Panel C: DCASH is (cash holding and cash equivalent in quarter j of year t – cash holding and cash equivalent in year t  1)/asset in quarter j  1 of year t. DSHORT is change of short term bank loan from the end of quarter j to the end of quarter j + 2, scaled by the total asset at quarter j. HOLD in an indicator variable that equals one if a firm holds more than 5% of a bank’s total ownership, and zero otherwise. NONSOE is an indicator variable that is equal to one if a firm is a non-SOE, and zero otherwise. MC is the monetary policy sentiment index of bankers, which is taken from a quarterly survey jointly conducted by the People’s Bank of China (PBC) and the National Bureau of Statistics (NBS). The monetary policy sentiment index is the proportion of bankers who consider monetary policy stance as ‘‘too loose’’, ‘‘loose’’, ‘‘appropriate’’, ‘‘tight’’, ‘‘too tight’’, or ‘‘unsure’’. We use ‘‘tight’’ as the measure of MC. The higher the MC, the tighter the monetary policy will be. SMB is (market value of equity in the quarter j of year t + book value of debt in quarter j of year t)/book value of asset in quarter j of year t. SSIZE is natural log of asset in quarter j  1 of year t. SCF is cash flow from operation in quarter j of year t divided by the asset in quarter j  1 of year t. SLEV is financial leverage in quarter j. Note: p-Value is reported in parentheses. *** Significance at the 1% level. ** Significance at the 5% level. * Significance at the 10% level.

STDitj ¼ b0 þ b1 HOLDitj þ b2 NONSOEitj þ b3 HOLDitj  NONSOEitj þ b4 SLEVitj1 þ b5 SSIZEitj1 þ b6 SCFitj1 þ Industry Effect þ Year Effect þ eit

ð7Þ

STD is the quarterly standard deviation of bank loan levels calculated using the rolling window and including two measures: SHORTSTD and LONGSTD. SHORTSTD is the standard deviation of short-term bank loans from quarter j  3 to quarter j. LONGSTD is the standard deviation of long-term bank loans from quarter j  3 to quarter j. SLEV is financial leverage, equal to total debt in quarter j  1 of year t divided by assets in quarter j  1 of year t. The other variables are defined as in model (3). The results presented in Table 8 show that when the dependent variable is SHORTSTD, the coefficients on HOLD and HOLD  NON-

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Table 7 Effect of holding bank ownership on the level of bank loan holding. The following model is estimated with OLS:

Table 8 Effect of holding bank ownership on the volatility of bank loan holding. The following model is estimated with OLS regression:

DEBTit ¼ b0 þ b1 HOLDit þ b21 NONSOEit þ b3 HOLDit  NONSOEit þ b4 LEVit1

STDitj ¼ b0 þ b1 HOLDitj þ b2 NONSOEitj þ b3 HOLDitj  NONSOEitj þ b4 SLEVitj1 þ b5 SSIZEitj1 þ b6 SCFitj1 þ Industry Effect þ Year Effect þ eit

þ b5 SIZEit1 þ b6 ROAit1 þ b7 GROWTHit1 þ b8 AMit1 þ b9 MARKET þ Industry Effect þ Year Effect þ eit

Dependent variable Dependent variable

SHORTit

LONGit

Constant

Industry dummies Year dummies

0.2575*** (0.004) 0.0298 (0.156) 0.0158* (0.053) 0.0337 (0.301) 0.0056 (0.191) 0.4184*** (0.000) 0.0002 (0.975) 0.1049*** (0.000) 0.0045** (0.031) Controlled Controlled

0.4906*** (0.000) 0.0408 (0.113) 0.0077 (0.112) 0.0755** (0.012) 0.0267*** (0.000) 0.0100 (0.526) 0.0017 (0.434) 0.0864*** (0.000) 0.0065*** (0.000) Controlled Controlled

Observations R-squared

4001 0.1055

4001 0.2241

HOLDit NONSOEit HOLDit  NONSOEit SIZEit1 ROAit1 GROWTHit1 AMit1 MARKET

SOE are not significantly different from zero, suggesting that the change in short-term debt is not sensitive to holding significant bank ownership. When the dependent variable is LONGSTD, the coefficient on HOLD  NONSOE is 0.0076 (p-value = 0.041), suggesting that non-SOEs holding significant bank ownership have less volatile long-term loan holdings. These results are consistent with the notion that because non-SOEs that hold bank ownership can obtain short-term bank loans when necessary, they are less likely to obtain long-term loans, which carry higher interest costs. In contrast, non-SOEs without economic bonds with banks must consider obtaining long-term loans to finance short-term projects. 4.3.2. Does holding significant bank ownership improve a firm’s operating performance? The above results support the statement that related lending can have direct benefits such as reductions in interest expenses and increases in financial flexibility. It is conjectured that such benefits can influence firms’ operating performance. We test this conjecture with the following model (8):

PERFORMANCEit ¼ c0 þ c1 HOLDit þ c2 NONSOEit þ c3 HOLDit  NONSOEit þ c4 SIZEit1 þ c5 ROAit1 þ c6 LEVit1 þ c7 GROWTHit1 þ Industry Effect ð8Þ

SHORTSTDitj

Industry effect Year effect

0.0329*** (0.006) 0.0004 (0.890) 0.0018* (0.095) 0.0076** (0.041) 0.0325*** (0.000) 0.0015*** (0.009) 0.0059 (0.453) 0.0001 (0.117) Controlled Controlled

Observations R-squared

13,086 0.0419

13,086 0.1090

HOLDitj NONSOEitj HOLDitj  NONSOEitj SLEVitj1 SSIZEitj1 SCFitj1 SMBitj1

***

LONGSTDitj

0.0368 (0.009) 0.0023 (0.463) 0.0034*** (0.005) 0.0016 (0.764) 0.0329*** (0.000) 0.0011 (0.108) 0.0021 (0.793) 0.0001 (0.388) Controlled Controlled

Constant

Variable definition: DEBT is the bank loan at the end of the fiscal year, including two measures: SHORT and LONG. SHORT is equal to the short-term bank loan level divided by the total asset at the beginning of the year. LONG is equal to the longterm bank loan level divided by the total asset at the beginning of the year. HOLD in an indicator variable that equals one if a firm hold more than 5% of a bank’s total ownership, and zero otherwise. NONSOE is an indicator variable that is equal to one if a firm is a non-SOE, and zero otherwise. SIZE is firm size, equal to the natural log of total asset. ROA is return on asset, equal to net income divided by total asset. AM is equal to PP&E divided by the total asset. GROWTH is sales growth, equal to the difference between this year’s sale and previous year’s sale divided by previous year’s sale. MARKET is the index of development of regional market, where the index is higher, the regional market is more developed. Note: p-Value is reported in parentheses. * Significance at the 10% level. ** Significance at the 5% level. *** Significance at the 1% level.

þ Year Effect þ eit

ð7Þ

ð6Þ

Variable definition: STD is quarterly standard deviation of bank loan level calculated using rolling window, including two measures: SHORTSTD, and LONGSTD. SHORTSTD is standard deviation of the short-term bank loan from quarter j  3 to quarter j. LONGSTD is standard deviation of the long-term bank loan from quarter j  3 to quarter j. HOLD in an indicator variable that equals one if a firm holds more than 5% of a bank’s total ownership, and zero otherwise. NONSOE is an indicator variable that is equal to one if a firm is a non-SOE, and zero otherwise. SLEV is financial leverage, equal to in total debt in quarter j  1 of year t divided by the asset in quarter j  1 of year t. SSIZE is natural log of asset in quarter j  1 of year t. SCF is cash flow from operating activities in quarter j of year t divided by the asset in quarter j  1 of year t. SMB is (market value of equity in the quarter j of year t + book value of debt in quarter j of year t)/book value of asset in quarter j of year t. Note: p-Value is reported in parentheses. * Significance at the 10% level. ** Significance at the 5% level. *** Significance at the 1% level.

PERFORMANCE represents operating performance, including measures of ROA and SALES; ROA is return on assets, which is equal to net income divided by total assets; SALES is equal to net sales divided by total assets at the beginning of the year. The other variables are defined as in the above models. The results in Table 9 show that when the dependent variable is ROA, the coefficient on HOLD  NONSOE is 0.0240 (p-value = 0.045).17 When the dependent variable is SALES, the coefficient on HOLD  NONSOE is 0.2356 (p-value = 0.083). These results suggest that for non-SOEs, holding bank ownership can improve operating performance, in terms of better return on assets and sales.18 4.4. Robustness tests for small sample of our interested firms One limitation of our paper is our small sample of interested firms (i.e., non-SOE firms with significant bank ownership). To address this concern, we significantly increase the number of interested firms by lowering our threshold from 5% bank ownership to 1%. The percentage of firms holding more than 1% of a bank’s to17 The results are robust even if we do not control for the ROA in the year t  1. The coefficient on HOLD  NONSOE is 0.0189 (p-value = 0.049). 18 To test for potential multicollinearity problems in all of our regressions, we obtain the values of the variance inflation factor (VIF) for regressions(2)–(4) and (6)–(8). The VIF values for most independent variables are lower than 2. O’Brien (2007) suggests that a VIF of 5 or above indicates a multi-collinearity problem. If this criterion is acceptable, then multi-collinearity problem does not significantly affect our analyses.

Z. Lu et al. / Journal of Banking & Finance 36 (2012) 341–354 Table 9 Effects of holding bank ownership on operating performance. The following model is estimated with OLS regression:

PERFORMANCEit ¼ c0 þ c1 HOLDit þ c2 NONSOEit þ c3 HOLDit  NONSOEit þ c4 SIZEit1 þ c5 ROAit1 þ c6 LEVit1 þ c7 GROWTHit1 þ Industry Effect þ Year Effect þ eit

ð8Þ

Dependent variable

ROAit

SALESit

Constant

Industry dummies Year dummies

0.0292 (0.366) 0.0016 (0.850) 0.0005 (0.875) 0.0240** (0.045) 0.0009 (0.588) 0.4379*** (0.000) 0.0073 (0.718) 0.0090*** (0.000) Controlled Controlled

1.2298*** (0.000) 0.2189*** (0.000) 0.0904*** (0.006) 0.2356* (0.083) 0.0932*** (0.000) 1.3725*** (0.000) 0.0904 (0.115) 0.0783*** (0.000) Controlled Controlled

Observations R-squared

3828 0.1855

3828 0.1981

HOLDit NONSOEit HOLDit  NONSOEit SIZEit1 ROAit1 LEVit1 GROWTHit1

Variable definition: PERFORMANCE is the operating performance, including measures of ROA, SALES, and INVESTINCOME. ROA is return on asset, equal to net income divided by total asset. SALES is equal to net sales divided by total asset at the beginning of the year. HOLD in an indicator variable that equals one if a firm holds more than 5% of a bank’s total ownership, and zero otherwise. NONSOE is an indicator variable that is equal to one if a firm is a non-SOE, and zero otherwise. LEV is financial leverage, equal to total debt divided by total asset. SIZE is firm size, equal to the natural log of total asset. GROWTH is sales growth, equal to the difference between this year’s sale and the previous year’s sale divided by the previous year’s sale. Note: p-Value is reported in parentheses. * Significance at the 10% level. ** Significance at the 5%level. *** Significance at the 1%level.

tal ownership is 7.57%, about three times the percentage (2.77%) that we obtain when we use 5% as the threshold. When we use the escalated sample, the coefficient on NONSOE is 0.3071 (p-value < 0.001) for model (1); in the subsample of non-SOEs, the coefficient on HOLD  BANKDEBT is 0.0119 (p-value = 0.037) for model (2). The coefficient on HOLD  MC is 0.0427 (p-value = 0.049) for model (3). Other results are also robust when we use 1% of bank ownership as the threshold. For the sake of brevity, we do not tabulate the results of the robustness tests, but they are available from the authors upon request. Therefore, we can draw the same conclusions from the original firm sample and the larger sample. Be that as it may, significant effects are only observed in firms with bank holdings greater than 5% (inclusive). Firms with holdings between 1% and 5% do not show significant effects in our analyses. These results suggest that 5% is a significant threshold for the impact of partial bank ownership on bank loan decisions.19 Some studies that rely on small samples use the matching approach to ensure that they are not using an unbalanced sample. As in these studies, we match each firm with more than 5% bank ownership to the two most similar firms without significant bank ownership using the following criteria: (1) same industry, (2) same fiscal year, (3) same tercile of total assets, and (4) closest return on

19 We also use 3% of bank ownership as the cutoff point and find the results to be similar to those for a 1% cutoff point.

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assets. We also find the robust results based on the matching approach. Many finance studies rely on the use of small samples.20 One important rationale presented in these papers is that although the final samples are relatively small, they are not created using a biased selection process. Restricted by the filtering criteria used, such small samples are usually made up of all observations for a specific event such as a venture capital acquisition, leveraged buy-out, lawsuit, or IPO. The samples, although small, provide unique settings in which researchers can examine specific finance theories. With some limitations, these small samples enable researchers to draw reasonable conclusions. This rationale should also apply to our study. We have hand-collected all of the available observations for the sample period within this unique setting to test general finance theories regarding bank discrimination. Moreover, although we only find 48 cases in which non-SOEs hold more than 5% bank ownership shares, our total sample includes 4001 observations. It is also worth mentioning that the limited sample used, according to econometric theory, should increase the noise in our analyses, reduce testing power, and thus make it more difficult to obtain significant results. Our small sample is a limitation in empirical testing, but the results from our robustness tests yield the same conclusions. In short, we understand our limitation that the sample of our firms of interest is small, but we address this important concern by proving the robustness of our results using an expanded sample that covers an increased number of interested firms, as well as by explaining the econometric rationale for accepting our current results. 5. Conclusion In China, non-SOEs have many fewer political connections than do SOEs. As a result, banks give preferential treatment to SOEs and discriminate against non-SOEs. Consequently, China provides a natural setting in which to investigate how firms that are likely to encounter financial constraints for political reasons raise the necessary funding to support their rapid growth. We find that compared with SOEs, non-SOEs have a greater propensity to hold ownership in commercial banks. This finding is consistent with the notion that non-SOEs, which generally experience loan discrimination for political reasons, tend to build economic bonds with banks to facilitate related lending. Moreover, we find that compared to firms without significant bank ownership, non-SOEs that hold significant bank ownership pay lower interest expenses and are less likely to increase their cash holdings when the central bank’s monetary policy becomes tighter. These results suggest that building economic bonds with banks can eliminate loan discrimination, in terms of reduction of interest expense and increased likelihood of receiving loans during difficult times. We also find that among the non-SOEs, those holding significant bank ownership have fewer long-term loans, and they are less likely to change the loan holdings. We conjecture that because firms that have relationships with banks are more likely to obtain necessary funds through related lending, they have less of a need to finance their short-term projects with long-term loans and are 20 For example, Kracaw and Zenner (1996) examine the wealth effects of bank financing announcements in highly leveraged transactions with a sample of only 41 events. Groh and Gottschalg (2011) investigate the effect of leverage on the cost of capital in US buyouts with a sample comprised of transactions by only 41 funds. Stouraitis (2003) investigates the effects of management buy-out in the UK market with a sample that includes only 70 unit management buy-outs with available financial data. Lowry and Shu (2002) draw conclusions regarding the relation between litigation risk and initial public offering (IPO) underpricing based on a sample of 84 lawsuits. The list of small sample studies extends beyond what we have discussed above.

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thus more likely to exhibit smooth, low levels of long-term loan holdings. Finally, we find that non-SOEs with significant bank ownership exhibit better operating performance than those without significant bank ownership. Acknowledgements We acknowledge the comments and help from Johnny Chan, Yining Chen, Joseph Fan, Guohua Jiang, Li Jin, Charles Lee, Jevons Lee, Ike Mathur (editor), Jason Xiao, Yu Xin, Heng Yue, Ran Zhang, an anonymous referee and participants in the workshops in National Taiwan University, Peking University, Shanghai University of Finance and Economics, Zhejiang University. The paper is a part of the projects ‘‘Accounting Information and the Efficiency of Resources Allocation’’ and ‘‘Firms’ Motivations and Economic Consequences of Holding Bank Ownership’’ sponsored by NSFC. We also acknowledge the financial supports from the National Social Science Foundation of China (approval number 10AZD014), the Program for Innovative Research Team in UIBE and the research fund from NUS Business School. References Aharony, J., Lee, C.W., Wong, T.J., 2000. Financial packaging of IPO firms in China. Journal of Accounting Research 38, 103–126. Allen, F., Qian, J., Qian, M., 2005. Law, finance, and economic growth in China. Journal of Financial Economics 77, 57–116. Allen, F., Qian, J., Qian, M., Zhao, M.X., 2008. A Review of China’s Financial System and Initiatives for the Future. Working Paper. University of Pennsylvania. Bae, K., Kang, J., Kim, J., 2002a. Tunneling or value added? Evidence from mergers by Korean business groups. Journal of Finance 57, 2695–2740. Bae, K., Kang, J., Lim, C., 2002b. The value of durable bank relationships: evidence from Korean banking shocks. Journal of Financial Economics 64, 181–214. Bates, T.W., Kahle, K.M., Stulz, R.M., 2009. Why do US firms hold so much more cash than they used to? Journal of Finance 64, 1985–2021. Bernanke, B.S., Blinder, A.S., 1992. The federal funds rate and the channels of monetary transmission. The American Economic Review 82, 901–921. Brandt, L., Li, H., 2003. Bank discrimination in transition economies: ideology, information or incentives? Journal of Comparative Economics 31, 387–413. Charumlind, C., Kali, R., Wiwattanakantang, Y., 2006. Connected lending: Thailand before the financial crisis. Journal of Business 79, 181–217. Claessens, S., Feijend, E., Laevena, L., 2008. Political connections and preferential access to finance: the role of campaign contributions. Journal of Financial Economics 88, 554–580. Cull, R., Xu, L.C., 2003. Who gets credit? The behavior of bureaucrats and state banks in allocating credit to Chinese state-owned enterprises. Journal of Development Economics 71, 533–559. Cull, R., Haber, S., Imai, M., 2006. All Bad, All of the Time? Related Lending and Financial Development. Working Paper. World Bank. Dinc, I.S., 2005. Politicians and banks: political influences on government-owned banks in emerging markets. Journal of Financial Economics 77, 453–479. Faccio, M., 2006. Politically connected firms. American Economic Review 96, 369– 386. Faccio, M., Masulis, R.W., Mcconnell, J.J., 2006. Political connections and corporate bailouts. Journal of Finance 61, 2597–2635. Fan, G., Wang, X., Zhu, H., 2010. NERI Index of Marketization of China’s Provinces. Economics Science Press, Beijing. Firth, M., Lin, C., Liu, P., Wong, S.M.L., 2009. Inside the black box: bank credit allocation in China’s private sector. Journal of Banking and Finance 33, 1144– 1155. Ge, Y., Qiu, J., 2007. Financial development, bank discrimination and trade credit. Journal of Banking and Finance 31, 513–530. Green, W.H., 2003. Econometric Analysis, fifth ed. Prentice Hall Business Publishing, New Jersey.

Groh, A.P., Gottschalg, O., 2011. The effect of leverage on the cost of capital of US buyouts. Journal of Banking and Finance 35, 2099–2110. Han, S., Qiu, J., 2007. Corporate precautionary cash holdings. Journal of Corporate Finance 13, 43–57. Johnson, S., Mitton, T., 2003. Cronyism and capital controls: evidence from Malaysia. Journal of Financial Economics 67, 351–382. Khwaja, A.I., Mian, A., 2005. Do lenders favor politically connected firms? Renting provision in an emerging financial market. Quarterly Journal of Economics 120, 1371–1411. Kornai, J., 1993. The evolution of financial discipline under the postsocialist system. Kyklos 46, 315–336. Kracaw, W.A., Zenner, M., 1996. The wealth effects of bank financing announcements in highly leveraged transactions. Journal of Finance 51, 1931– 1946. Kummer, D.R., Arshadi, N., Lawrence, E.C., 1989. Incentive problems in bank insider borrowing. Journal of Financial Services Research 3, 17–31. La Porta, R., Lopez-de-Silanes, F., Shleifer, A., 2002. Government ownership of banks. Journal of Finance 57, 265–301. La Porta, R., Lopez-De-Silanes, F., Zamarripa, G., 2003. Related lending. Quarterly Journal of Economics 118, 231–268. Laeven, L., 2001. Insider lending and bank ownership: the case of Russia. Journal of Comparative Economics 29, 207–229. Leary, M.T., 2009. Bank loan supply, lender choice, and corporate capital structure. Journal of Finance 64, 1143–1185. Leuz, C., Oberholzer-Gee, F., 2006. Political relationships, global financing, and corporate transparency: evidence from Indonesia. Journal of Financial Economics 81, 411–439. Li, H., Zhou, L., 2005. Political turnover and economic performance: the disciplinary role of personnel control in China. Journal of Public Economics 89, 1743–1762. Li, K., Yue, H., Zhao, L.K., 2009. Ownership, institutions, and capital structure: evidence from China. Journal of Comparative Economics 37, 471–490. Lin, X., Zhang, Y., Zhu, N., 2009. Does bank ownership increase firm value? Evidence from China. Journal of International Money and Finance 28, 720–737. Lowry, M., Shu, S., 2002. Litigation risk and IPO underpricing. Journal of Financial Economics 65, 309–335. Luo, W., Zhang, Y., Zhu, N., 2011. Bank ownership and executive perquisites: new evidence from an emerging market. Journal of Corporate Finance 17, 352–370. Maurer, N., Haber, S., 2007. Related lending and economic performance: evidence from Mexico. Journal of Economic History 67, 551–581. O’Brien, R.M., 2007. A caution regarding rules of thumb for variance inflation factors. Quality and Quantity 41, 673–690. Pinkowitz, L., Williamson, R., 2001. Bank power and cash holdings: evidence from Japan. The Review of Financial Studies 14, 1059–1082. Qian, Y., 1994. A theory of shortage in socialist economies based on the ‘‘soft budget constraint’’. American Economic Review 84, 145–156. Rajan, R.G., Zingales, L., 1995. What do we know about capital structure? Some evidence from international data. Journal of Finance 50, 1421–1460. Sapienza, P., 2004. The effects of government ownership on bank lending. Journal of Financial Economics 72, 357–384. Stouraitis, A., 2003. Acquisition premiums when investment banks invest their own money in the deals they advise and when they do not: evidence from acquisitions of assets in the UK. Journal of Banking and Finance 27, 1917–1934. Su, X., Yang, Z., 2009. State Control, Financial Constraints and Firm Growth: Evidence from China. Working paper. City University of Hong Kong. Sun, L., Ford, J.L., Dickinson, D.G., 2010. Bank loans and the effects of monetary policy in China: VAR/VECM approach. China Economic Review 21, 65–97. Wang, Q., Wong, T.J., Xia, L., 2008. State ownership, the institutional environment, and auditor choice: evidence from China. Journal of Accounting and Economics 46, 112–134. Weinstein, D.E., Yafeh, Y., 1995. Japan’s corporate groups: collusive or competitive? An empirical investigation of Keiretsu behavior. The Journal of Industrial Economics 43, 359–376. Weinstein, D.E., Yafeh, Y., 1998. On the cost of a bank centered financial system: evidence from the changing main bank relations in Japan. Journal of Finance 53, 635–672. Yao, Y., Yueh, L., 2009. Law, finance, and economic growth in China: an introduction. World Development 37, 753–762. Zhou, W.B., 2009. Bank financing in China’s private sector: the payoffs of political capital. World Development 37, 787–799.