Thesis
Reference
Three essays on Chinese banking and corporate finance
ZHANG, Zhicheng
Abstract
Over the past two decades, the Chinese banking industry has undergone a series of reforms and also played a central role in the disbursement of the 4 trillion RMB stimulus package of 2008, raising concerns about Chinese shadow banking. We offer three studies to address critical questions on the evolution of the Chinese banking industry: (1) Did the ownership reforms in the banking sector improve the efficiency of the Chinese economy and from what perspective? (2) After the reforms, why and how did shadow banking emerge in China? (3) Can Chinese banks play a regulatory role to prevent shadow banking to the same extent as banks in developed economies? To answer these questions, in Chapter 1, we examine how stronger bank-firm relationships mitigate shadow-banking activity among borrowing firms through the implementation of stricter loan contracts and the improvement of corporate governance. In Chapter 2, we model the endogenous generation of shadow lending after the implementation of the 2008 stimulus package and demonstrate how large firms become shadow creditors of credit-rationed small firms. Finally, in Chapter 3, we [...]
ZHANG, Zhicheng. Three essays on Chinese banking and corporate finance. Thèse de doctorat : Univ. Genève, 2020, no. GSEM 89
URN : urn:nbn:ch:unige-1522529
DOI : 10.13097/archive-ouverte/unige:152252
Available at:
http://archive-ouverte.unige.ch/unige:152252
Disclaimer: layout of this document may differ from the published version.
1 / 1
and corporate finance
by
Zhicheng ZHANG
A thesis submitted to the
Geneva School of Economics and Management, University of Geneva, Switzerland,
in fulfillment of the requirements for the degree of PhD in Finance
Members of the thesis committee:
Prof. Harald HAU, Adviser, University of Geneva Prof. Tony BERRADA, Chair, University of Geneva
Prof. Philipp KRUEGER, University of Geneva Prof. Philip VALTA, University of Bern
Thesis No.
December 2020
I am very thankful to many people who both, directly and indirectly, contributed to my thesis. This dissertation would not have been possible without many people’s support, inspiration, and believe.
I am indebted to professor Harald Hau, my advisor, whose guidance and support was unwavering in past years. He allowed me to pursue a Ph.D. in Geneva and provided valu- able support throughout this journey. He was open to a broad range of research topics and always offered me inspiring ideas and suggestions. He also gave me the freedom to pursue my choosing projects, which contributed significantly to my academic independence.
Second, I am much obliged to my thesis committee members: my advisor Professor Harald Hau, chairman Professor Tony Berrada, Professor Philipp Krueger, and Professor Philip Valta. My committee provides me beneficial advice to push my papers to a higher level.
Third, I thank my colleagues. My research benefited a lot from my co-authors, Ying Liu, Difei Ouyang, and Weidi Yuan. We not only spent many hours discussing models and papers together but also share and support each other’s lives when we all study abroad.
Without their help and inspiration, there would be no my dissertation. Besides, I thank all my colleagues in companies, Geneva Institute for Wealth Management and Alphacruncher.
In the past several years, when I need to focus on completing my dissertation, they always unconditionally support me.
Finally, I am deeply thankful for the immeasurable support and unconditional love of my family. Thank the support from my wife and my parents. They helped me a lot in taking care of the entire family when I study and work in Switzerland. Thank my son and my daughter, whose love gave me brave to pursue this outstanding achievement.
Over the past two decades, the Chinese banking industry has undergone a series of reforms and also played a central role in the disbursement of the 4 trillion RMB stimulus package of 2008, raising concerns about Chinese shadow banking. We offer three studies to address critical questions on the evolution of the Chinese banking industry: (1) Did the ownership reforms in the banking sector improve the efficiency of the Chinese economy and from what perspective? (2) After the reforms, why and how did shadow banking emerge in China? (3) Can Chinese banks play a regulatory role to prevent shadow banking to the same extent as banks in developed economies? To answer these questions, in Chapter 1, we examine how stronger bank-firm relationships mitigate shadow-banking activity among borrowing firms through the implementation of stricter loan contracts and the improvement of corporate governance. In Chapter 2, we model the endogenous generation of shadow lending after the implementation of the 2008 stimulus package and demonstrate how large firms become shadow creditors of credit-rationed small firms.
Finally, in Chapter 3, we study the impact of the ownership reform of three of China’s largest state-owned banks and demonstrate an improvement in credit allocation.
Au cours des deux dernières décennies, le secteur bancaire chinois a subi une série de réformes et a également joué un rôle central dans le décaissement du plan de relance de 4 000 milliards de RMB de 2008, ce qui suscite des inquiétudes au sujet de la banque parallèle chinoise. Nous proposons trois études pour répondre à des questions critiques sur l’évolution du secteur bancaire chinois: (1) Les réformes de la propriété dans le secteur bancaire ont-elles amélioré l’efficacité de l’économie chinoise et dans quelle perspective?
(2) Après les réformes, pourquoi et comment le shadow banking est-il apparu en Chine?
(3) Les banques chinoises peuvent-elles jouer un rôle de régulation pour empêcher le shadow banking dans la même mesure que les banques des économies développées? Pour répondre à ces questions, au chapitre 1, nous examinons comment le renforcement des relations banque-entreprise atténue l’activité bancaire parallèle des entreprises emprun- teuses grâce à la mise en œuvre de contrats de prêt plus stricts et à l’amélioration de la gouvernance d’entreprise. Au chapitre 2, nous modélisons la génération endogène de prêts parallèles après la mise en œuvre du plan de relance de 2008 et démontrons comment les grandes entreprises deviennent les créanciers fantômes des petites entreprises rationnées par le crédit. Enfin, au chapitre 3, nous étudions l’impact de la réforme de la propriété de trois des plus grandes banques d’État chinoises et démontrons une amélioration de l’allocation des crédits.
Acknowledgements i
Abstract iii
Résumé v
Introduction 1
1 How bank-firm relationship impacts tunneling through commercial loans 3
1.1 Introduction . . . 3
1.2 Hypotheses . . . 6
1.3 Econometric methodology . . . 8
1.3.1 Heckman model . . . 8
1.3.2 Two-stage least squares (2SLS) . . . 9
1.4 Data and preliminary estimations . . . 9
1.4.1 Key databases and variables . . . 9
1.4.2 Probit model estimation and related data . . . 12
1.4.3 Strength of the bank-firm relationship and instruments . . . 12
1.5 Impact of bank-firm relationship on tunneling . . . 16
1.5.1 Related-party transactions . . . 16
1.5.2 The effect on related-party lending . . . 18
1.5.3 Impact channel via loan contract terms . . . 22
1.5.4 Impact channel via corporate governance . . . 25
1.6 Supplementary analysis . . . 28
1.6.1 Case of collateralizing of controlling shares . . . 28
1.6.2 Case of performance crisis of the listed firm . . . 31
1.7 Conclusion . . . 33
2 Equilibrium bank lending and shadow lending in China 35 2.1 Introduction . . . 35
2.2 Model . . . 38
2.2.1 The firms . . . 38
2.2.2 Banks . . . 39
2.2.3 Benchmark: bank lending with no shadow lending . . . 40
2.3 Shadow banking . . . 41
2.3.1 Endogenous generation of shadow lending . . . 41
2.3.2 Shadow lending . . . 43
2.3.3 The equilibrium shadow raters . . . 45
2.4 Noncompetitive banking sector . . . 46
2.5 Welfare analysis . . . 48
2.5.1 Firm profit . . . 48
2.5.2 Real efficiency . . . 49
2.5.3 Theoretical predictions . . . 51
2.6 Data . . . 52
2.6.1 Firm level loan data . . . 52
2.6.2 Measures of shadow-banking activities . . . 53
2.6.3 Firm characteristics . . . 54
2.6.4 Summary statistics . . . 55
2.6.5 T-test comparison . . . 55
2.7 Empirical Results . . . 56
2.7.1 Lending rate conditional on firm size or loan volume . . . 56
2.7.2 Lending rates conditional on entrusted lending . . . 59
2.7.3 Default risk conditional on entrusted lending . . . 59
2.8 Conclusion . . . 62
3 Did China’s Bank Ownership Reform Improve Credit Allocation? 63 3.1 Introduction . . . 63
3.2 Background of China’s bank ownership reform . . . 67
3.3 Identification . . . 68
3.3.1 Local exposure to bank ownership reform . . . 68
3.3.2 Baseline specification . . . 70
3.4 Data . . . 72
3.5 Results . . . 75
3.5.1 Bank ownership reform and corporate credit access . . . 75
3.5.2 Dynamic effect of the bank ownership reform . . . 79
3.5.3 Bank ownership reform and corporate debt . . . 81
3.5.4 Credit access of unlisted companies . . . 83
3.5.5 Bank ownership reform and firm performance . . . 86
3.6 Robustness check . . . 88
3.6.1 Placebo test: other external financing channels . . . 88
3.6.2 Intensive margin: change in firm-level loan rate . . . 89
3.7 Conclusion . . . 90
A Appendix to Empirical Illustrations 95 B Appendix to mathematical proof 103 B.1 List of Variables . . . 103
B.2 Proof to proposition 1, 2 . . . 104
B.3 Proof of proposition 3 . . . 105
B.4 Proof of Lemma 3 . . . 107
B.5 Proof of proposition 4 . . . 108
B.6 Shadow banking also provide monitoring, but behave monopolistically . . . 109
Since China became a member of the WTO on December 11, 2001, the Chinese banking industry has played a critical role in supporting the growth of the Chinese economy while facing the challenge of surging foreign investment. Over the past two decades, the Chinese banking industry has experienced a series of reforms as well as the listing of most state- owned banks on stock markets following the restructuring of their nonperforming loans.
The industry was also at the center of the disbursement of the 4 trillion RMB stimulus package in 2008-2009 due to the elimination of lending quotas. Within a few years after the implementation of the package, the emergence of Chinese shadow banking had become an issue of global concern.
The major events of the two past decades raise a few critical questions:
1. Did the ownership reform imposed on the banking industry improve the efficiency of the Chinese economy and from what perspective?
2. After this reform, why and how did shadow banking emerge in China?
3. Can Chinese banks play a regulatory role to prevent shadow banking to the same extent as banks in developed economies?
We use three studies to address these research questions in reverse order and to offer a portrait of the Chinese banking industry over the past two decades. Our results try to shed some insights for policymakers in the future.
The first chapter builds the foundation of the three studies. Many works in the literature have studied the relationship between lending banks and borrowing firms in developed economies. In a developed market, the owners of most listed firms and banks are fully diversified public investors, and the market is more open and competitive. Studies show that lending banks can help a listed firm improve its corporate governance. However, in the Chinese economy, under a different political system, most listed firms belong to a business group, the majority stake of most banks still belongs to the government, and the banking sector is less competitive. Under this very different market structure, are modern corporate finance theories still applicable to the Chinese economy? Specifically, can Chinese lending banks still improve the corporate governance of borrowing firms and reduce shadow-banking activities?
The first chapter examines how bank-firm relationships in China impact one crucial type of shadow banking, namely, business group tunneling activities, whereby financial capital is transferred to unlisted firms through commercial loan contracts. By collecting detailed information on the commercial loans of listed firms, transaction-level tunneling activities of business groups, and different measurements of bank-firm relationships, we create a unique dataset for this study. Banks build their relationships with borrowing firms by acquiring soft information through proximity and exploiting inside information
through holding the borrowing firm’s equity. This chapter applies the Heckman model together with instrumental variables to reveal that a stronger bank-firm relationship can reduce the borrowing firm’s tunneling activities through channels related to both loan covenants and corporate governance.
In the second chapter, we address the second question of why and how shadow lending emerges. We model and document the endogenous generation of capital transactions between related parties described in the first chapter. We show that shadow lending is a market reaction to increased competition in the banking sector. Firms with large capitalizations, which can obtain cheap bank loans, tend to overborrow and then relend to other firms through entrusted loans. Credit rationing exists since the success of risky investments depends on the effort of the entrepreneurs who lead the firms. The bank rations the credit of small firms, and entrusted lending does not alleviate this credit rationing since shadow banks do not provide a monitoring service. In the presence of shadow lending, banks increase their lending rate to firms that are engaged in such lending practices. In turn, the profits of these firms are improved. However, bank monitoring of these firms is reduced, which results in a higher default risk. Entrusted lending thus offers a new channel to provide cheaper financing to small and medium-sized firms, but it increases risks to financial stability.
The third chapter studies the impact of the 2004 ownership reform on the credit allocation of three of China’s largest state-owned banks (SOBs). We use firm-level data from 2000 to 2007 to show that more-indebted listed companies experienced a substantial decline in credit access in cities with greater exposure to these SOBs after the bank reform.
This indicates that the change in bank ownership pushed loan officers to consider more credit risks in the lending process. We extend our study further to a universe of unlisted manufacturing companies and find that the bank ownership reform also reduced bank lending to state-owned enterprises (SOEs) and less productive firms in addition to more- indebted firms. The negative effect of the reduced SOB lending on firm performance in terms of profitability and investment is only prominent in SOEs and less productive firms.
In sum, the three studies in this dissertation show a vivid evolution of the Chinese banking industry over the past two decades. The reform of state-owned banks has im- proved credit allocation efficiency and made bankers realize the importance of credit risk.
However, under the 4 trillion RMB stimulus package of 2008, commercial banks had to follow the government policy direction to inject enormous capital into the market. Condi- tional on the rise of the credit risk management mindset, commercial banks tried to raise the lending rate to increase the cost of capital to shadow bankers, the large firms that relend capital to small firms with rationed credits. To further reduce shadow-banking ac- tivities, commercial banks obtain more information about borrowing firms through bank- firm relationships, which help them impose stricter lending contract terms and impact corporate governance. Overall, although China differs greatly from developed economies in terms of firm ownership structures, the economic and political system, and many other dimensions, the Chinese banking industry follows the intrinsic market mechanism and has become more efficient in credit allocation and governance.
How bank-firm relationship impacts tunneling through commercial loans
This chapter examines how bank-firm relationships in China impact tunneling activities by business groups through commercial loan contracts. We create a unique dataset by collecting detailed information on the commercial loans of listed firms, transaction-level internal tunneling activities of business groups, and different measurements of bank-firm relationships (proximity and equity exposure). Banks build their relationships with bor- rowing firms by acquiring soft information through proximity and exploiting inside in- formation by holding the borrowing firm’s equity. This chapter applies the Heckman correction together with instrumental variables to reveal that a stronger bank-firm rela- tionship can reduce a borrowing firm’s tunneling activities through by affecting both the terms of loan covenants and the corporate governance of borrowers.
Keywords: Tunneling, Bank-firm relationship, China, Commerical loans
1.1 Introduction
Business groups are the most widespread organizational structure in emerging markets (Khanna and Yafeh, 2007), especially in China. After a listed firm borrows a commercial loan from a bank, how it uses the loan becomes a paramount concern. When a listed firm is controlled by a business group, the group has very concentrated share ownership and control rights. Minority shareholders pay close attention to internal transactions between the listed firm and its parent firm or other sibling firms (i.e., subordinate firms under the same parent firm). Engaging in such transactions is called “tunneling”, and it is a source of tremendous expropriation of minority shareholders’ earnings. Given that capital-related tunneling can potentially transfer loans from banks to firms’ related parties, how bank- firm relationships may further impact tunneling activities through the terms of the loan contract between the firm and the bank is the critical question examined in this chapter.
The Chinese banking industry has experienced earth-shaking changes since 2004.
These changes began with the restructuring and IPO of four large Chinese state-owned commercial banks from 2004 to 2008. After an enormous number of city-level commercial bank registrations, mergers and restructurings, since 2008, the Chinese banking indus- try has expanded to disburse massive commercial loans under the 4 trillion RMB fiscal policy. Current innovations include the emergence of e-banking (such as through Ant Financial) and P2P lending. All together, there have been significant changes not only
to the structure of the banking industry but also to the relationships between banks and their borrowers, especially firms holding commercial loans.
In the meantime, the private sector has also experienced enormous changes. To drive economic growth, the Chinese economy has begun to transition towards a market orien- tation. The Chinese government encourages state-owned enterprises to restructure and form business groups (Keister, 1998, White et al., 2008), and private-owned firms also commonly follow this path (Carney et al., 2009). Thus, business groups represent a criti- cal component of the Chinese economy, and most listed firms are part of a business group (Jia et al., 2013b).
During the process of the economic transformation, listed firms have become a capital- raising mechanism for the controlling shareholders. After publicly listing their star sub- sidiary firm, business groups transfer capital either by making intercorporate loans to the majority shareholder or affiliates (G. Jiang et al., 2010a) or by acting as a guarantor of loans to the other group members and assuming liability for repayment of the loans in case of default (G. Jiang et al., 2010a, Fisman and Wang, 2010). These tunneling behaviors among member firms of the same business group are pervasive in China (Keister, 1998, Keister, 2001). The China Security Regulatory Committee (CSRC) now requires listed firms to publicly reveal in detail all transactions with related parties, including loans, guarantees, and operations, which allows for the analysis of tunneling behaviors at the transactional level.
As the two sectors have experienced enormous changes at the same time, the con- nection between the banking sector and industry firms has become more substantial and complicated. In the Chinese market, banks are the ultimate supplier of enormous financ- ing resources to industry firms. In contrast to the prediction of traditional pecking-order theory (Myers and Majluf, 1984), banks always occupy the position of top external fi- nancing channel of Chinese listed firms. By aggregating three external financing cash flows of Chinese listed firms across years, figure A.1illustrates that the value of corporate bonds and new shares issued in the Chinese market is very small compared with that of commercial loans from banks. In 2015, the total amount of newly issued commercial loans to all listed firms was more than 6 trillion yuan and accounted for 78% of all external financing vehicles, which is the lowest proportion of bank loans from 1998 to 2015. Before 2007, this ratio was more than 90%. Due to this extreme reliance on commercial loans, there is a symbiosis between listed firms and banks, where one can severely impact the other.
Against this background, it becomes meaningful to investigate bank-firm relationships and how they impacts borrowers’ tunneling activities.
There are different views in the literature on tunneling activities and the role that strong bank-firm relationships play in them. On the one hand, if a strong bank-firm relationship can help a listed firm sign an advantageous contract, a business group can transfer more capital out of the listed subordinate. The transferred funding can benefit other organizations within the same business group, a behavior that Johnson et al.,2000 defines as “tunneling”. Based on agency theory, many studies characterize this behavior as expropriation of the returns of minority shareholders and identify similar activities in different countries, e.g., Bae et al.,2002 in Korea, Bertrand et al.,2002in India, G. Jiang et al., 2010ain China, and Morck et al.,2005in Japan. Alternatively, the listed firm may act as a guarantor to help its sibling firms receive bank loans, which the listed firm will pay back the debt if the sibling firm goes into default. From the study of Scharfstein and Stein, 2000, when the different divisions or subsidiaries under the same firm or a business
group and, more importantly, when they have different strength, e.g., the listed firm is the star of the business group, the massive inefficiencies are very likely to arise through the internal capital market. The strong subsidiary company will subsidize the weak ones in many ways, such as the tunneling approach.
On the other hand, Dass and Massa, 2011 demonstrates that a stronger bank-firm relationship pushes banks to govern misbehaviors in corporations, such as tunneling ac- tivities. If banks are aware of tunneling activities, they are assumed to take measures to protect their assets to avoid an increased probability of default risk. A stronger bank-firm relationship could allow the bank access to inside information to improve its capital allo- cation and enhance its monitoring capability (e.g., Diamond,1984, James, 1987, Besanko and Kanatas, 1993a), while strengthening the listed firm’s corporate governance (Levine, 2002).
In addition to the positive and negative impacts of tunneling, Jia et al.,2013bbrings up coinsurance theory to demonstrate that tunneling activities can be bidirectional. That is, they can boost the capital of the business group but can also save the listed firm. However, it is unknown how bank-firm relationships impact tunneling behaviors and directions in different situations.
Therefore, this chapter aims to study how bank-firm relationships impact listed firms’
tunneling activities under different circumstances in the Chinese market.
Although complete related-party transactions are available for listed firms, to execute this analysis, we also requires a measurement of the strength of a bank-firm relationship.
The literature provides two variables as proxies: the proximity between the lending bank and the firm and the availability of insider information to the bank as one of the firm’s top shareholders (Dass and Massa, 2011). Proximity is defined as the geographical distance between the borrowing firm and the lending bank, which is associated with the bank’s information-gathering capability (Coval and Moskowitz, 2001, Berger et al., 2005). The equity exposure measure considers the extent of bank access to inside information and is calculated as the direct equity ownership stake of the lending bank in the borrowing firm, which is the fraction of the borrower’s equity held by the investment fund of the lending bank (Kahn and Winton, 1998).
Constructing the measurements of the bank-firm relationship requires a massive col- lection of banking information and loan-level data on all listed firms. However, this task is difficult given the incomplete character of the Chinese finance data system. The au- thor designed an algorithm to extract a dataset with information on commercial loans by analyzing the text of annual financial reports. This dataset contains details on the characteristics of bank loans made to a broad panel of firms in China from 2001 to 2015, including bank names with branches, amount, duration, interest rates, and collaterals if reported. By merging this unique dataset with the financial statements of listed firms and the information on bank branches, I can calculate both the proximity and equity exposure measurements.
With the bank-firm relationship variables and related-party transaction observations, the study of their relationship must address two potential biases: selection bias and the endogeneity of the bank-firm relationship proxies. To solve these issues, this chapter ap- plies the lambda from the Heckman model to correct for selection bias. It introduces instrumental variables to address the endogeneity issue via the 2SLS model. After ex- cluding the possibility of the results being affected by these biases, this chapter illustrates that a close bank-firm relationship can help to mitigate tunneling activities in all perspec- tives, including direct capital lending by the borrowing firm, and other noncash tunneling
activities, such as trading business goods with related parties and acting as the guarantor of a related firm with another bank as different as the lending bank of the listed firm.
The analysis is not complete if it only looks at the direct relationship between bank- firm relationship (BFR) and related-party transaction (RPT) without studying the chan- nel and causal mechanism of the effect. The in-depth analysis considers two channels through which the bank-firm relationship impacts the borrower’s tunneling activity. The classical mechanism is the terms of the loan contract. Proximity and equity exposure can impact the loan contract in different ways, such as by reducing the lending amount and tenor and increasing interest rates. On the other hand, a stronger bank-firm relationship can also strengthen the corporate governance of the borrowing firm, which is reflected in a reduced prevalence of directors sitting on multiple boards across firms, an increased percentage of independent directors and an improved probability of having a nonexecu- tive chair. All these indicators show a significant improvement in corporate governance quality alongside the reduction in tunneling activities.
Last but not least, given the coinsurance theory, it is necessary to test whether the av- erage effect of the bank-firm relationship on related-party transactions holds in particular situations. When the ultimate controller of the listed firm faces severe financial difficulties and its controlling shares are used as collateral, a stronger bank-firm relationship helps prevent tunneling towards the parent firm. On the other hand, when a listed firm faces the risk of being delisted due to two consecutive years of negative profits, the lending bank reduces the strictness of its monitoring of internal transactions to protect the firm’s listed position on the stock market.
This chapter makes several contributions. First, it represents the first attempt, to the author’s knowledge, to quantify the bank-firm relationship in China. Second, it shows how banks can impact a firm’s internal tunneling activities and sheds some light on corporate and shadow-banking governance through the bank-lending channel. The remainder of the chapter is structured as follows. Section 2 introduces the hypothesis. Combining the econometric methodology described in section 3 and the data source discussed in section 4, the chapter constructs the critical variables and offers preliminary estimations. Section 5 reports the main findings and discusses the channel of the effect. Furthermore, section 6 provides a supplementary analysis to study the different facets of the main findings in particular situations. Conclusions and potential future research are presented in the final section.
1.2 Hypotheses
Dass and Massa, 2011 demonstrates that in a developed country such as the US, banks can influence the corporate governance of borrowers, namely, listed firms with no busi- ness group affiliation, through commercial lending. The bank-firm relationship can be measured with different types of information that banks receive through the loan lending process. Banks can acquire soft information on firms at closer geographical distances (proximity), and banks can access inside information by holding equity shares of bor- rowing firms. A more-informed bank can govern its borrowers through lending, and the bank is willing to monitor firms closely. In markets such as the US or UK, very few listed firms are publicly held by any controlling shareholders or affiliated with a business group. If a firm goes bankrupt, the lending bank will bear significant losses even if it is one of the most senior debt claimants. The lending bank must estimate the probability of bankruptcy when the borrowing firm increases its leverage and therefore monitor the
financial performance of the borrowing firm carefully to avoid losing the market value of the firm or its collaterals. The ultimate purpose of the bank is to preserve the value of its loans and obtain repayment and interest at maturity.
In the context of a developing country such as China and a different organizational structure such as a business group, people conventionally think that the government controls banks and that banks always provide liquidity for large business groups. The fundamental difference is that the bankruptcy concern is much lower for a listed firm under a business group than for a firm without controlling shareholders. When a listed firm faces a performance issue or bankruptcy crisis, its business group will support the listed firm in overcoming its difficulties by injecting financing resources or providing lower- cost goods (Jia et al., 2013b).
However, even with the lower threat of bankruptcy, the ultimate purpose of the com- mercial lending bank is the same as that of commercial banks in developed countries in terms of recovering the principal and maximizing interest earnings. When the lending bank has more information, measured by proximity and equity exposure, a commercial bank should still have an incentive to monitor borrowing firms’ misbehaviors, such as direct transfers of borrowed cash to fund other related parties. According to Besanko and Kanatas, 1993a, acquiring more information enhances the monitoring ability of the bank.
Moreover, it can improve capital allocation and corporate governance in the borrowing firm (Levine, 2002).
In addition, when a bank holds the borrower’s equity shares in its investment arm (measured as its equity exposure), the bank should have even more incentive to monitor and reduce internal transactions in the borrowing firm. Directly tunneling loans out of the borrowing firm reduces the total cash flow that the firm can distribute across shareholders, including the lending bank. Utilizing inside information acquired from holding equity shares as one of the top 10 investors, the lending bank should be able to reduce cash- related tunneling activities, for example, direct loans to sibling or parent firms, on the part of the borrowing firm. Furthermore, when borrowing firms participate in noncash-related tunneling activities, e.g., providing guarantees and supporting the cash flow of sibling or parent firms, the improvement of corporate governance should also act to monitor noncash related-party transactions.
Therefore, combining the two bank-firm relationship measures, it is reasonable to formulate the following hypotheses:
Hypothesis 1 A stronger bank-firm relationship should reduce firm’s tunneling activities, both cash-related or noncash-related.
Hypothesis 2 A stronger bank-firm relationship should improve corporate governance in the same way as in developed countries.
However, there is an exception if the lending bank provides direct-lendings to both the listed firm and its sibling firms, and the listed firm plays the guarantor’s role. With opaqueex anteprivate information, banks usually require collaterals to compensate for the risk of commercial lending to ease the adverse selection and credit rationing in the spirit of Stiglitz and Weiss, 1981. A stronger bank-firm relationship reduces the asymmetric information between the lending bank and the listed firm’s sibling firms. Therefore the bank lends more to the sibling firms as long as the listed firm is the guarantor, and the lender is the same bank. On the other set of theories, theex postfrictions, including moral hazard concerns, difficulties in enforcing contracts, and costly state verification, raise the
risk of the borrowers and reduce the lending probabilities (Berger et al., 2011). Therefore, when the lending bank strengthens the borrower’s corporate governance via loan contract and equity exposure, it can monitor and verify the lending contract’s risk for the sibling firms. The reduction of theex postfrictions encourages lending to the sibling firms as well.
Conversely, when the sibling firm uses the listed firm as the guarantor to borrow from a bank that does not have the bank-firm relationship with the listed firm, the listed firm’s lending bank executes its corporate governance role and reduces this tunneling activity.
Hypothesis 3 A stronger bank-firm relationship facilitates the guaranteed lending to the related parties with the same bank lender but reduces the guaranteed lending with a different bank lender.
1.3 Econometric methodology
In examining the relationship between bank-firm relationships (BFRs) and firm behaviors, e.g., related-party transactions (RPTs), as well as the channels connecting those variables, the basic ordinary least squares (OLS) regression method suffers from two kinds of biases.
First, selection bias exists because the BFR-RPT relationship is conditional on a firm’s decision to borrow from a bank. Thus, when one firm decides to borrow from one specific bank rather than another random bank, selection bias arises. Second, endogeneity bias might exist due to omitted variables. For example, a firm with fewer relate-party trans- actions may be more likely to borrow from a bank that already holds more of its own equity shares before lending.
This chapter refers to the solution proposed by Heckman, 1979 to address selection bias and includes the inverse Mills ratio in the main equations; it then applies a two- stage least squares (2SLS) approach to deal with endogeneity bias in the estimation of the main equations. This approach is described in procedure 17.2 under section 17.4.2 in Wooldridge, 2010. Dass and Massa, 2011 show a solid example of how this approach can be applied to bank-firm relationship analysis.
1.3.1 Heckman model
The straightforward way to study the RPT-BFR relationship is to estimate equation 1.1 at the loan level, where RP T is the related-party transaction series, BF R is one of the bank-firm relationship proxies, and β is the coefficient of BF R. However, equation 1.1 is estimated conditionally on the existence of a loan contract in the first place. Given existing situations in which some firms do not borrow any loans from any bank or one firm borrows from one specific bank but not another random bank, the nonrandomness in the sample selection would cause inconsistent estimations of the relationship.
RPT=βBFR+u (1.1)
Therefore, it is necessary to apply the Heckman correction to take the selection con- dition into consideration, as in equation 1.2. By estimating the probit model in equation 1.3, where W is the matrix of explanatory variables andγ is a vector of coefficients, if the fitted loanˆ ∗ ≤ 0, then RPT∗ is not observed and loan = 0. If the fitted loanˆ ∗ > 0, then RPT∗ is observed and loan= 1.
loan=
1 if loanˆ ∗ >0
0 if loanˆ ∗ ≤0 (1.2)
loan∗ =W γ+ν (1.3)
After estimating the probit model in equation 1.3, the fitted value, Lambda, can be derived from equation 1.4, where ˆγ is a vector of estimated coefficients and Φ is the cumulative distribution function of the standard normal distribution.
λ= Φ(Wγ)ˆ (1.4)
By including the variableLambdain equation1.1, equation1.5can ease selection bias.
However, endogeneity bias remains a problem, requiring the application of two-stage least squares methods and the introduction of instrumental variables to solve this issue.
RPT=βBFR+σλ+u (1.5)
1.3.2 Two-stage least squares (2SLS)
In the conventional setup, 2SLS requires finding instrumental variables Z for the key explanatory variables, i.e., the bank-firm relationship proxies (BF R) in equation 1.6.
BFR=Zδ+ε (1.6)
By estimating equation 1.6, the fitted value of BF R is instrumented to remove endo- geneity bias. Furthermore, the instrumented BFR variables can be replaced in equation 1.5 to become equation 1.7, which is the main equation to be estimated in the rest of the chapter. In particular, the dependent variable, RP T, can replace other explained variables while keeping the previous steps unchanged.
RPT=βBFRˆ +σλ+u (1.7)
The rest of the chapter will start by introducing the estimation of the detailed probit model in equation 1.3 and its required data. Based on the estimation result, the variable Lambda will be generated. Next, estimation of the detailed first stage of equation1.6will describe the related data and variables at the same time. Finally, by applying the fitted BFR variables and the correction variableLambda, the result sections will investigate the actual RPT-BFR relationship and the channels through which the relationship operates.
1.4 Data and preliminary estimations
1.4.1 Key databases and variables
Commercial loan contract terms of Chinese listed firms
To conduct this research, datasets from several different sources are merged to construct the different stages of analysis. The central data for all the analyses in this chapter are the loan contract data, and the rest of the dataset is illustrated along with each regression model.
A firm begins to build a relationship with a bank when a commercial loan contract is agreed upon. Thus, to study how a bank can impact firm-level activities, all analyses are based on loan deals, and having more details on such contracts allows for more in-depth investigation. Unfortunately, the public financial databases that the author checked do not provide this information, nor does any previous literature declare the usage of commercial loan datasets for Chinese listed firms.
Qian and Yeung,2015aaccess a private and detailed sample of loans from one Chinese national bank whose borrowers are mainly private firms. However, to detect tunneling activities, audited financial statements and internal transactions are required, and private firms do not provide such information publicly.
Bailey et al., 2011atries to search all bank loan-related announcements from the web- site of the China Securities Regulatory Commission (CSRC). Although the China Stock Market & Accounting Research (CSMAR) Database, from a Shenzhen-based data vendor (GTA), has built this public announcement database, it is missing an enormous number of observations. First, disclosure regulations require listed firms to announce only impor- tant events that may significantly affect their stock prices. Listed firms, therefore, only selectively announce loans above a certain value. Second, corporate governance policies in some firms allow the CEO or chair to sign borrowing contracts without shareholder approval as long as the amount is below a given threshold. Third, most loan-related public announcements state the overall amount of loans or credit lines after the signing of an agreement with a bank, but the number in the announcement is not the annually realized value of the commercial loan.
In the absence of a Chinese corporate loan database counterpart to the Loan Pricing Corporation’s (LPC’s) DealScan database for US firms, the author designed an algorithm to extract loan information from firms’ annual financial reports. Chinese listed firms are encouraged but not required to disclose their top short- and long-term borrowings by size.
Such information is not limited to bank loans but also includes sources from trusts and even from individuals. Thus, most listed firms report the top 5 or 10 or even all commercial loans in the additional notes of their annual financial statements. Based on the annual report PDFs, the algorithm extracts bank-loan observations, including the exact lending bank’s name (not the top-level name of a commercial bank but the exact branch), the loan amount, the tenor, the interest rates or the rules for calculating interest, and the currency of the loan. This information covers the essential terms of a loan contract. Some firms even provide further details, such as collaterals used for the loan contract or any guarantors of the loan. This task is not easy at all since firms’ annual reports differ and have no consistent format, which causes tremendous difficulty in collecting the data.
Although this collected dataset does not cover all commercial loans from all listed firms and these variables are not always available for each loan contract in all annual reports, the loan dataset offers a good representation of the Chinese commercial loan market. To the best of our knowledge, this is the first work to collect and study a dataset on Chinese commercial loan contracts.
Overall, the algorithm collects over 100,000 pieces of borrowing information. However, since this chapter only studies new loans, the algorithm further identifies a new loan contract as a zero loan amount at the beginning of the financial year and a nonzero amount at the end of the year. Borrowing information with a nonzero starting amount only reports repayment results instead of the initiation of a new loan contract. After this filter is implemented, this sample contains approximately 30,000 new loan contracts agreed from 2001 to 2015. Comparing the actual cash borrowing events with the extracted
borrowing events, defined as at least one loan contract is captured from the same firm’s financial report in the same year, this dataset covers 96.4% of borrowing decisions. Since listed firms only disclose their top loan contracts, this dataset captures 83.4% borrowing amount, compared to the “new borrowing” aggregate item in financial reports. Overall speaking, this lending contract dataset is relatively representative.
Bank branch profile
In addition to the specific bank branch details in the loan contract information, the analysis also requires bank information for all branch levels, such as a bank branch’s name, its level, and its address, to identify its geographic location, bank-firm relationship, and other bank-related measurements.
Since the establishment of any bank branch requires a banking certification from the China Banking Regulatory Commission (CBRC), the full Chinese bank branch data are scraped from the CBRC website. This dataset covers all Chinese financial institutions, including not only commercial banks but also rural credit cooperatives, policy banks, and financial firms. The variables include a bank branch’s name, address, foundation date, branch level within the bank hierarchy, bank category, and bank city code. The address of each bank branch can be used to locate its latitude and longitude and calculate the distance to another set of coordinates. Matching the bank names with the lender name in each loan contract via an algorithm completes the linkage between a firm and a bank branch through a loan contract.
Firm financial characteristics
Firm-level financial control variables were obtained from the database of RESSET, a Bei- jing data vendor specialized in financial data. Multiple regression models in the following analysis commonly use a few firm-level control variables defined in the same way, but the calculated periods are different. Size is the natural logarithm of the book value of the total assets in the same year. Leverage is the ratio of long-term debt over total as- sets in the same calendar year. Cash is the total cash in proportion to the lagged total assets. Capital expenditure is the capital expenditure in cash-flow statements divided by lagged total assets. ROA (return on assets) is income divided by lagged total assets.
The market-to-book ratio is the stock price at the end of the year multiplied by total shares and divided by total book equity value. The construction of the Kaplan-Zingales (KZ) index follows the methodology of Baker et al., 2003 to measure the likelihood that the firm faces financial constraints. The firm age is calculated as the number of years since the firm was founded. The SOE dummy (1 for SOE and 0 for non-SOE) repre- sents state-owned enterprises, which account for a significant proportion of Chinese listed firms. Controlling for these firm characteristics helps to isolate the direct impact of the bank-firm relationship on firm activities that is not due to other factors, e.g., asymmetric information related to size, leverage, or profitability (Murray Z. Frank and Vidhan K.
Goyal, 2003a) or extra investment opportunities from cash and capital expenditure. In addition, year and industry dummies are used to further capture fixed effects. The year fixed effects cover from 2001 to 2015, where the latest loan contract was collected. Since the calculation of both dependent and independent variables at the firm level takes the average value across the loan contract duration and there exist loan contracts lasting after 2015, the study also collects financial statements until 2019 and only considers the loan contract terminated before the end of 2019. Firm industry information is obtained
from RESSET’s basic firm information table. The definition of the industry fixed effects follows the second level of the Global Industry Classification Standard (GICS) categories, ranging from 1010 to 5510 total 22 levels. Finally, the sample of interest only includes nonfinancial listed firms within business groups.
1.4.2 Probit model estimation and related data
As introduced in section1.3, the first step for the entire analysis is to correct for selection bias. Equation1.8 specifies the probit model of general equation1.3. The analysis results are shown in table 1.1.
loan-taking decisioni,t =αi,t+Wi,t−1γ +νi,t (1.8) The loan-taking decision is a dummy variable that equals 1 when firm i initiates at least one loan contract in year t and 0 in complementary firm-year observations. Wi,t−1 contains a series of explanatory variables at yeart−1. “Existence of another outstanding loan” is a dummy variable taking the value of 1 if the firm already has an existing long-term commercial loan at the beginning of the calendar year and 0 otherwise. The “Metropolis”
dummy takes the value of 1 if the firm is located in one of the four largest metropolises in China by population (Shanghai, Guangzhou, Beijing, or Shenzhen) and 0 otherwise.
“Concentration of the banking market” is measured by the lagged Herfindahl–Hirschman Index (HHI) of the bank branches located in the same city as the firm. HHI is defined by the sum of the squares of the market shares of the firms within the city. Since the market share and the total assets of the bank branches are not available, this variable uses the number of bank branches under different banks to calculate the HHI score to represent the competitiveness of the banking industry in each city. The result of using these three instruments to estimate the probability of the loan-taking decision is described in the first column of the table 1.1.
As described in section 1.4.1 above, the probit model further introduces a series of explanatory variables Wi,t−1, including size, leverage, cash, capital expenditure, ROA, market-to-book ratio, Kaplan-Zingales index, and firm age, but they are measured at the lagged year t−1. The estimation results with three instruments and these control variables are shown in the second column of table 1.1.
The last column of the same table includes year and industry dummies to capture year- and industry-specific fixed effects that might affect the loan-taking decision of firms. The regression results of table 1.1 show that firms with outstanding loans are more likely to take a new loan and that firms located in a metropolis are less willing to take commercial loans. The result is consistent with the coefficients in Dass and Massa, 2011. Therefore, column 3 of table1.1 is used to calculate the lambda (inverse Mills ratio) as in Heckman, 1979; this lambda variable will be required in the second-stage regression model.
1.4.3 Strength of the bank-firm relationship and instruments
After the correction of selection bias, there is still one more step to complete before the regression of the main analysis can be estimated: using instrumental variables to solve the endogeneity issue. In the two-stage least squares (2SLS) regression model, equation 1.9 is the specified version of equation 1.6 in section 1.3. BF R includes two proxies to represent the bank-firm relationship variables, measured in year t for each loan contract j. Zj,t is a series of instrumental variables that are related to BFR variables only and
Table 1.1: Firm Loan-Taking Decision
Loan-taking Decision
(1) (2) (3)
Exist another outstanding loan 0.730∗∗∗ 0.628∗∗∗ 0.672∗∗∗
(0.019) (0.023) (0.024)
Metropolis −0.159∗∗∗ −0.118∗∗∗ −0.143∗∗∗
(0.022) (0.022) (0.024)
Concentration of the banking market −0.211 −0.222 0.039
(0.145) (0.152) (0.164)
Size 0.183 0.435∗∗∗
(0.128) (0.134)
Size-square −0.005∗ −0.011∗∗∗
(0.003) (0.003)
Leverage 0.778∗∗∗ 0.601∗∗∗
(0.068) (0.073)
ROA −1.211∗∗∗ −2.050∗∗∗
(0.402) (0.432)
Cash −0.569∗∗∗ −0.710∗∗∗
(0.064) (0.069)
Capital Expenditure 0.022 −0.029
(0.123) (0.131)
Market-to-Book 0.001 0.001
(0.001) (0.001)
Tobin-Q 0.005 0.003
(0.003) (0.004)
Free Cashflow 0.766∗∗ 1.146∗∗∗
(0.323) (0.346)
Dividend payment −0.772 −0.457
(0.496) (0.525)
Firm Age 0.009∗∗∗ 0.005∗∗∗
(0.002) (0.002)
SOE 0.004 −0.014
(0.020) (0.022)
Constant −0.978∗∗∗ −2.699∗ −5.396∗∗∗
(0.024) (1.411) (1.478)
Year Fixed Effects No No Yes
Industry Fixed Effects No No Yes
Observations 23,010 23,010 23,010
Log Likelihood −12,929.880 −12,759.880 −11,527.020
Akaike Inf. Crit. 25,867.750 25,551.760 23,158.050
Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01
are orthogonal to the second-stage dependent variables, such as related-party transaction amounts. The following sections introduce the construction of these measurements.
BFRj,t =αj,t+Zj,tδ+εj,t (1.9) Given the datasets on commercial loan transactions and bank profiles introduced in the previous sections, the construction of the measurements of bank-firm relationship strength follows that of Dass and Massa, 2011.
Proximity
Using the geographic distance between lenders and borrowers can reflect the degree of informational closeness between the borrower and the lender because a lender at a closer distance can acquire soft information to help it make lending decisions (Berger et al., 2005). Proximity is defined as −ln(1 +distance). The shorter the distance between the borrower and lender, the greater is the proximity.
The proximity measure calculates the distance between the headquarters of the bor- rowing firm and the lending bank branch, the two parties involved in each loan contract.
The calculation requires locating the geographical latitude and longitude of the address of each party. The firm profile dataset comes from the RESSET database and includes the detailed headquarters address of each listed firm. The bank branch addresses are captured in the bank profile information dataset introduced in section 1.4.1. The author designed a search program to identify each firm or bank address via the Google Maps system and decodes its latitude and longitude.
The geographical distance between the firm headquarters and the bank branch location is calculated by formulas 1.10 and 1.11. The distance di,j in meters between firm i and bank branch j is
di,j =r×∆σ (1.10)
where ∆σ is given by:
∆σ= arccos(sin(lati)×sin(latj) + cos(lati)×cos(latj)×cos(|lngj −lngi|)) (1.11) while lat and lng refer to the latitude and longitude, respectively, and r is the radius of Earth in kilometers (6371 km). This method is applicable to all distance calculations in this chapter.
Equity exposure
Equity exposure proxies the internal information that banks can potentially acquire from the borrower. It measures the borrower’s equity shares held by the lending bank each year as a fraction of the borrower’s total shares. Similar to banks in developed countries, Chinese commercial banks have both a lending function and an investment arm. Given the inside information it provides, greater equity exposure might stimulate banks to govern borrowers more strictly to protect their investment value. If necessary, banks can liquidate borrowers’ shares and therefore affect the stock price.
To construct the measurement of equity exposure, we use the RESSET database, which provides the name and share percentage of the top 10 shareholders of each listed firm.
By applying an expression algorithm, it is possible to match the top shareholder names
with the names of commercial banks in the bank profile dataset. Then, by matching the names of the banks that hold equity shares in firms with the names of banks that lend commercial loans to the same firm, the equity exposure (shareholding percentage) of each lender corresponding to each loan contract is finally identified.
Instrumented result of the first stage regression
One of the most critical concerns is that the firm endogenously determines the main explanatory variables (proximity and equity exposure). They are affected by the char- acteristics of the firm and its external constraints. For addressing this issue, this study adopts the instrumental variables approach, similar to the one of Dass and Massa, 2011, to deal with the endogeneity of the remaining loan characteristics. The instrumental variables need to correlate with proximity and equity exposure, but orthogonal to other omitted characteristics. The instruments should be uncorrelated with the dependent vari- able through any channel other than their effect via the endogenous explanatory variables.
In order to ease endogeneity bias, the vital instrument for the proximity variable is the proximity (−ln(1 +distance)) between the headquarters of the borrowing firm and the nearest large branch of any bank, in line with Dass and Massa, 2011. The large branch uses the city-level headquarters of each bank. Most Chinese commercial banks set up their organizational structures with headquarters at the national level, province level, and city level, and small branches and ATMs all over each city. Since the full bank profile dataset is available, it is possible to identify the city-level headquarters of all banks, to calculate the distances between one firm’s headquarters location and the lending bank branch’s coordinates, and to find the nearest city-level branch of any bank as the instrumental variable for the proximity measurement.
Similar to the proximity measurement, the equity exposure variable requires a unique instrument. Following Dass and Massa, 2011, we use the average equity exposure at the industry level as our instrument for equity exposure. First, if multiple banks hold equity shares in the same firm, all equity stakes held by banks for each firm need to be summed up each year. Second, the instrument for the equity exposure variable uses the industry average equity exposure to banks in a given year excluding the lending bank’s equity share. Last, the equity exposure is measured one year before the loan contract.
Besides the unique instruments for each BFR variable, several sets of instruments, Zj,t, are commonly used in the literature.
The first group of instruments is related to the firm’s product market and indus- try. Given the linkage of leverage and borrowing choice to product market competi- tion (Kovenock and Phillips, 1995), this study includes a few industry-level averages of company characteristics as instruments to proxy the industrial structure of the product market: size, leverage, cash, capital expenditure, ROA, market-to-book ratio, and the Kaplan-Zingales index. The definitions of these variables are the same as in section 1.4.1, but the calculation method takes the average of variables over firms in the same industry and the same year but excludes the corresponding value of the borrowing firm. Also, they are measured before the loan deal initiates.
The second set of instruments consists of the characteristics of the local commercial- bank market. The banking-market characteristics provide instruments for the loan char- acteristics that are orthogonal to the residuals (Dass and Massa,2011). Following Berger et al., 2005, this study uses the banking market concentration and the metropolis dummy as proxies to characterize the local banking market. These instruments proxy for the
availability and relative cost of other sources of capital available to the firms. As de- fined earlier in this section, a local banking market’s concentration level calculates as the Herfindahl–Hirschman Index (HHI) of the bank branches located in the same city as the firm. HHI calculates the sum of the squares of the firms’ market shares within the city.
Since the market share and the bank branches’ total assets are not available, this vari- able uses the number of bank branches under different banks to calculate the HHI score to represent the competitiveness of the banking industry in each city. The metropolis dummy takes the value of 1 if the firm is located in one of the four largest metropolises in China by population (Shanghai, Guangzhou, Beijing, or Shenzhen) and 0 otherwise.
Furthermore, as Dass and Massa, 2011 suggested, the local banking market’s impact is stronger for smaller or younger firms and decreases as the firm grows bigger or older and can access alternative capital markets more efficiently. Therefore, the interactions be- tween the borrower’s size and age and the metropolis dummy complement the previous instruments. All these variable measurements calculate the year before the inception of the loan.
Table1.2shows the first-stage regression result of the loan characteristics on the above instrumental variables with the year and industry fixed effects. The proximity and equity exposure measurements are instrumented separately in columns 1 and 2. Each of them uses one unique instrumental variable, as introduced above, in addition to the indus- try average instrumental variables. Proximity is positively related to the unconditional proximity of the firm to any bank’s city-level branch. Proximity is also affected by the characteristics of the firm’s industry (capital expenditure, market-to-book ratio, and age) and strongly negatively related to the local banking market concentration. Equity expo- sure is positively related to its unconditional average. The negative relationships on the interaction terms of the borrower’s size and age with the metropolis dummy prove that the large and older firms can access alternative capital markets more efficiently in the top 4 cities. The lending bank increases its equity stake when the borrowing firm locates in a large financial center. It is also affected by the firm’s industry characters (such as capital expenditure, market-to-book, institutional holdings, and leverage) and the local banking market concentration level. The F-test values report at the bottom of the table, and the rejection of the null hypothesis confirms that none of the instrumental variables are weak. In sum, the instruments are statistically correlated with the potentially endogenous variables of interest and do not seem to affect the dependent variables through a channel other than their effect via the endogenous loan characteristics.
1.5 Impact of bank-firm relationship on tunneling
1.5.1 Related-party transactions
The literature uses two sets of measurements to capture tunneling activities in China.
G. Jiang et al., 2010a detects that a substantial portion of “other receivables” items in financial statements are directly traceable to intercorporate loans towards the largest shareholder and its affiliates. On the other hand, Jia et al., 2013b discover that some finance databases have collected data on direct tunneling activities or so-called related- party-transactions (RPTs), which the CSRC mandates all publicly listed firms disclose.
This study uses this RPT dataset from the RESSET database. It captures detailed in- formation on each RPT deal, recording the detailed profile of the related party in each transaction, including its name and relation with the listed firm (parent firms, subordi-
Table 1.2: Bank-firm relationship variables
Proximity Equity Exposure
(1) (2)
Proximity to the Nearest Large Branch of Any Bank 0.221∗∗∗
(0.016)
Industry-Average of Equity Exposure in the same year 0.568∗∗∗
(0.027) Size of Other Firms in Borrower’s Industry −0.345∗ −0.002 (0.186) (0.005) Leverage of Other Firms in Borrower’s Industry −0.731 −0.025 (1.054) (0.023) Cash of Other Firms in Borrower’s Industry −0.600 0.048∗∗
(0.829) (0.019) ROA of Other Firms in Borrower’s Industry −9.481∗∗∗ −0.056 (1.924) (0.039) Capital Expenditure of Other Firms in Borrower’s Industry 4.487∗∗ −0.014 (2.197) (0.040) Market-to-Book of Other Firms in Borrower’s Industry 0.053∗∗ 0.0003
(0.021) (0.0005) Kaplan-Zingales of Other Firms in Borrower’s Industry −0.070 0.008∗∗
(0.107) (0.004) Age of Other Firms in Borrower’s Industry 0.051∗∗ 0.001
(0.026) (0.001)
Metropolis 0.686 0.057∗∗∗
(0.872) (0.016) Concentration of the banking market −2.421∗∗∗ 0.016∗
(0.552) (0.009)
Size ×Metropolis −0.017 −0.002∗∗∗
(0.038) (0.001)
Age× Metropolis −0.003 −0.0005∗∗∗
(0.011) (0.0002)
Constant −3.209 0.091
(3.783) (0.103)
Year Fixed Effects Yes Yes
Industry Fixed Effects Yes Yes
Observations 15,592 2,622
Adjusted R2 0.053 0.267
F Statistic 26.469∗∗∗ 22.189∗∗∗
Note: ∗p<0.1; ∗∗p<0.05;∗∗∗p<0.01
nates, other sibling firms under the same parent firm). Since the listed firm’s consolidated financial statements include transactions with subordinates and earnings or losses are dis- tributed among all shareholders equally, tunneling activity as defined in this chapter does not include the “transactions with subordinates” category. On the other hand, transac- tions with parent and sibling firms actually tunnel benefits to the controlling shareholder, and minority shareholders bear the cost and risk of this behavior. Thus, this research will only take into account transactions with parent and sibling firms.
This dataset also includes the details of each transaction, such as the amount, currency, interest rates if applicable, duration, date, and the transactions category. By filtering by the category code, all internal transactions are separated into three general categories:
loans, guarantees, and operational exchanges.
Figures A.2 and A.3 illustrate the evolution of the number of listed firms and the aggregate amounts of RPT loans, guarantees, and operations. The percentage of listed firms directly providing intercorporate loans to other business group affiliates rose from 18% in 2007 to 39% in 2015. The total value of RPT loans reached 1.1 trillion yuan (approximately 160 billion USD) in 2015 from only 0.1 trillion yuan (15 billion USD) in 2007.
By 2015, 54% of firms had acted as guarantors for other members of their business groups, assuming liability for their loans in case of default; this represented growth from an average of less than 30% before 2008. The total value of guarantees exceeded 2 trillion yuan (approximately 290 billion USD), twice the value of direct loans. Because of trans- parent disclosure requirements for financial statements, listed firms prefer to play the role of guarantor rather than lender. However, if borrowing firms default, listed firms acting as guarantors have to repay their debts and convert the loan guarantees into intercorpo- rate lending, which constitutes a heavy financial burden on listed firms (G. Jiang et al., 2010a).
Operational RPTs include internal purchases and sales of goods or assets, services, compensation, leases, and the formation of new joint-venture firms. In these transactions, products and services may flow in either direction. Listed firms can both purchase from and sell to sibling or parent firms. In either case, operational RPTs often involve favor- able pricing terms or low transaction costs. Utilizing this approach can help listed firms improve their performance (Fisman and Wang, 2010, G. Jiang et al., 2010a) and avoid being downgraded to an ST (special treatment) firm (Jia et al., 2013b).
The next section will analyze how soft information (proximity) and inside information (equity exposure) impact the decision to engage in tunneling and the amount tunneled in three categories. Specifically, this work focuses on RPT outflows (from the listed firm to other related parties) but not RPT inflows. In each category, the RPT amount is the aggregation of all transactions made in the respective calendar year by each firm.
1.5.2 The effect on related-party lending
In line with the econometric methodology described in section 1.3, it is more appropri- ate to apply a Tobit model rather than an OLS model in equation 1.7 at the second stage to analyze the relationship between the related-party transaction (RPT) and bank- firm relationship (BFR) measurements after correcting for selection and endogeneity bias.
Equation 1.12 illustrates the specific regression model:
