1

State ownership and banks’ information monopoly rents:

Evidence from Chinese data

ADSTRACT

In a lending relationship, banks with an information monopoly charge higher interest rates.

State ownership in banks and borrowing firms has a bearing on this relationship. On the one

hand, state-owned enterprises (SOEs), suffering from worse information asymmetry and

inefficient risk-taking, tend to get stuck with incumbent banks. On the other hand,

state-owned banks place less emphasis on the information production, and hence are less

likely to benefit from information monopoly compared to profit-maximizing private banks.

To investigate the hypotheses, we use equity initial public offering (IPO) as the information

releasing event, and the loan interest rate decline around the IPO as the proxy of pre-IPO

information monopoly rent. With proprietary loan-level data from China, we find SOEs

experience a larger decline in their loan interest rates around their IPOs, the

central-government-controlled Big Four banks exhibit a smaller decline in rates they charge,

and their interest rate declines concentrate in loans made to SOEs.

Keywords State Ownership, Information Monopoly Rent, Loan Interest Rate, IPO, Banking

Relationship

JEL G21; G24; G32

Jan 15, 2018

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1. Introduction

The conventional wisdom about lending holds that banks are relationship lenders who

acquire proprietary, firm-specific information about the borrowers through screening

and monitoring services to overcome the information asymmetry (e.g., Diamond, 1984;

Allen, 1990). A dark side of banks’ information production is that it creates an

informational gap between incumbent banks and outside banks. A firm seeking to

switch banks may be perceived by uninformed outsiders as a “lemon” regardless of its

true financial condition. This gives incumbents monopoly power to “hold up” the

borrowers and charge high interest rates (Sharpe, 1990; Rajan, 1992). Recent studies,

such as Santos and Winton (2008), Hales and Santos (2009) and Schenone (2010), use

loan data in the United States and provide empirical evidence of banks’ information

monopoly rent.

This paper, focusing on China’s banking market where state-owned companies and

banks are heavyweight players, seeks to understand whether information monopoly rent

exists in such a market, and how state ownership influences this rent. We argue that

state-owned enterprises (SOEs) are subject to greater hold-up costs, because of the three

features of SOEs that differentiate them from privately-owned companies.

First, the objective function of SOEs is not, at least not solely, profit maximization;

rather it has a focus on providing social services (Baumol, 1984). Bai, Lu and Tao (2006)

contend that SOEs in China pursue two goals simultaneously: financial profits and

social stability. This dual objectives problem makes it more difficult to monitor and

evaluate managers. Second, the public ownership in SOEs is nontransferable, which

“inhibits the capitalization of future consequences into current transfer prices and

reduces owners’ incentives to monitor managerial behavior” (De Alessi, 1980). In other

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words, the public lacks the incentive to monitor performance of SOEs. And third, SOEs

enjoy implicit government guarantees, that is, bailouts may be available in case of

financial distress.

Both the dual objectives and nontransferable public ownership of SOEs hinder

monitoring and cause greater information asymmetry. Consistent with this, prior

research shows that state ownership is associated with lower financial reporting quality

and financial transparency (Bushman, Piotroski, and Smith, 2004; Guedhami, Pittman,

and Saffar, 2009). In a lending relationship, greater information barriers are more

costly to overcome for potential competitor banks, benefiting the incumbent banks.

Implicit government guarantees further exacerbates the adverse selection problem in the

credit market, because the perception is then SOEs should have no difficulties receiving

loans from incumbent banks, and only the worst “lemons” would seek funds from

outsiders.

Another unintended effect of implicit government guarantees is that SOEs are

subject to the classical moral hazard problem in that their managers and private

shareholders benefit from risk-taking but may not bear the cost of financial distress.

SOEs also tend to make investment decisions that are financially inefficient, thanks to

their dual objectives.1 As a result, SOEs often hold a majority of the nonperforming

loans (NPLs) in China’s banking system.2 Recovery of these NPLs from SOEs incurs

tremendous time and financial costs, often ending up with substantial loan write-offs

following long-term gridlock.3

Thus SOEs might represent greater risks to

1 For instance, SOEs in China are subject to restrictions on, among others, firing workers, and hence are slow to

terminate unprofitable projects. 2 For instance, PwC in a December 2015 report attributes the rapid increase in NPLs to the RMB 4 trillion financial

stimulus in 2009, a majority of which flew to SOEs, and industrial over-capacity, which inflicted SOEs particularly

badly. 3 According to the China Banking Regulatory Committee (CBRC), China’s banks had written off more than 2 trillion

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profit-maximizing banks, despite the implicit government guarantees. Consistent with

this judgement, China’s banks assign significantly lower internal credit ratings to SOEs

than non-SOEs (Qian, Strahan, and Yang, 2015). Rajan (1992) theorizes that firms

with greater risk should suffer more from informational hold-up problems, because

outside banks are less willing to bid on loans perceived risky.

In a nutshell, SOEs, compared to non-SOEs, suffer from worse information

asymmetry. The adverse selection problem they face is exacerbated by the implicit

government guarantees. The government protection coupled with dual objectives lead to

SOEs’ inefficient risk-taking. All these factors indicate SOEs would be subject to

greater information monopoly rents in the credit market.

China’s banking sector also features government ownership and interventions. The

four largest commercial banks4, often dubbed as the Big Four, are controlled by the

central government and pursue social welfare maximization rather than firm value

maximization (Sapienza, 2004; Iannotta, Nocera, and Sironi, 2013). 5

Intuitively,

information production is needed in the lending relationship only if both banks and

borrower firms are independent and profit-maximizing (Bailey, Huang, and Yang, 2011).

Unlike other commercial banks that have widely dispersed ownership structures often

without a controlling owner, the Big Four place more emphasis on political and social

goals (Berger, Hansen, and Zhou, 2008), and hence their lending decision-making is less

likely based on the borrowers’ creditworthiness. For this reason, the quantity and quality

of proprietary information the Big Four acquire might be lower, and so would

information monopoly rents they are able to charge. yuan ($308 billion) during the three years preceding Jun 2016. 4 The Agricultural Bank of China (ABC), the China Construction Bank (CCB), the Bank of China (BOC), and the

Industrial and Commercial Bank of China (ICBC), combined held about half of the industry assets as during

2003-2012 and made 60 percent of loans in our sample. 5 See the ultimate controllers of Chinese commercial banks in Appendix Table A1.

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The lending relationships between the Big Four banks and SOEs are especially

interesting, as the common political and social goals could bring them into the loan

transactions. As a matter of fact, nearly half of loans the Big Four make go to SOEs, and

this fraction is much higher than that for non-Big Four banks.6 In such SOE-Big Four

transactions, impacts of state ownership on both parties would play a role in

determining the cost of loans. On the one hand, the high risk and information

asymmetry of SOEs impede them from switching to alternative lenders, suggesting high

information monopoly rents. On the other hand, the shared political and social goals

diminish the importance of information production, which would lead to lower

information monopoly rents. The net effect is thus an empirical issue.

Based on the above analyses, we make two hypotheses about the information

monopoly rents in China’s credit market.

1) Holding all else constant, SOEs are subject to greater information monopoly rents

than non-SOE firms.

2) Holding all else constant, the Big Four banks enjoy smaller information monopoly

rents than non-Big Four banks, especially when loans are to non-SOE borrowers.

Our investigation is based on a proprietary, loan-level dataset that spans the period

May 1996 through December 2014. The dataset contains detailed information of 10,534

traditional loans to private and public firms in China. In order to detect information

monopoly rents in a bank-firm relationship, we compare the loan pricing before and

after the firm’s IPO, in the same spirit of Hale and Santos (2009) and Schenone (2010).

In these studies, IPOs serve as major information releasing events that level the playing

field among banks and erode incumbent banks’ information monopoly. A decline in the

6 See Appendix Table A4.

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cost of loans around the IPO indicates an information monopoly rent prior to the IPO.

We measure the loan cost by the percentage interest rate spread, namely, the

difference of a loan’s interest rate from China’s benchmark interest rate, as a percentage

of the benchmark. Overall, SOEs exhibit a greater decline in the spread around IPOs

(4.27%) than non-SOEs (1.47%). After controlling for loan, firm and bank

characteristics, an average SOE’s spread decline is 2.33 percentage points greater than

that of a non-SEO firm, consistent with our expectation. With similar controls, the

declines in the Big Four’s interest rate spreads around the borrowers’ IPOs are on

average 1.80 percentage points lower than those of non-Big Four banks, also consistent

with our expectation. This effect is concentrated on loans made to non-SOEs: the Big

Four’s spread decline is 3.36 percentage points lower than non-Big Four banks. In

contrast, for loans made to SOEs, the Big Four’s spread decline is 3.01 percentage

points larger than that of other banks. This result indicates that the high switching costs

of SOEs in China outweigh the low information advantage of the Big Four, allowing the

later to fetch higher information monopoly rents from the banking relationships with

SOEs.

Competing, but not necessarily conflicting, interpretations of the loan rate decline

around IPOs include IPO’s risk effect and cash flow effect. The former holds that an

equity IPO lowers the firm’s debt ratio and financial risk, which in turn leads to lower

credit cost (Pagano et al., 1998; Hsu et al., 2010). The latter argues that certification by

investment banks (Carter and Manaster, 1990) and increased investor recognition could

lead to higher future cash flows and hence reduce credit cost. We control for these

factors by incorporating capital structure, underwriter reputation and analyst coverage in

the model and obtain the same results.

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Alternatively, the SOE vs. non-SOE discrepancy in loan rate decline may arise due

to ownership structure changes of SOEs around IPOs. In an IPO, an SOE brings in new

owners, likely private investors, swaying the objective function towards profit

maximization. Increased profit maximization incentives may drive the firm to more

aggressively negotiate loan terms, resulting in a larger drop in loan rate.7 Inconsistent

with this conjecture, though, we find that SOEs with an increase or a minor decrease in

state ownership experience greater interest rate declines after IPO than those with a

major decrease in state ownership. When we exclude those SOEs with a major drop in

state ownership, we still find that SOEs experience significantly greater loan rate

decline than non-SOEs. Hence the interest rate decline is not attributable to the change

in state ownership around IPOs.

We have considered the potential influence of interest rate liberalization in China

on banks’ information monopoly rent.8 Relaxation in interest rate regulation expands

banks’ pricing capacity, incentivizes their information production, and may ultimately

entrench or compromise incumbent bank’s information advantage. After controlling for

the interest rate liberalization, our findings do not change.

Our results are also robust to alternative sample selections, with different time

periods around the IPO, and with the restriction that sample loans are from the same

banks around the IPO. In addition, we use the matched sample approach to control for

the potential endogeneity of IPO decisions and the results remain qualitatively the same.

Despite the accumulating evidence of banks’ information monopoly rents, all

investigations thus far consider a developed market in which profit-maximizing,

7 We thank an anonymous referee for this suggestion. 8 Commercial banks in China could set their lending rates between a floor rate and a ceiling rate during 1996- 2004.

The ceiling was removed in 2004 and the floor removed in 2013. See details of the evolving process of regulation on

loan interest rates in China in appendix Table A3.

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privately-held companies are domiciled. Very little is known about how banks and

borrowing companies interact to determine the cost of loans in an environment where

either party may have incentives other than pursuing maximum profits for its private

owners. Our paper represents the first peek into the lending relationship confounded by

government ownerships and interventions. It complements and extends the existing

literature of relationship banking (Petersen and Rajan,1994; Degryse and Van

Cayseele ,2000; Bharath et al.,2011; López-Espinosa, Mayordomo and Moreno,2016;

Prilmeier, 2017), and explores yet another implication of state ownership in business

management. While promoting social welfare, state ownership undercuts the efficiency

of both banks and borrowing firms in that it weakens banks’ incentive to information

production and yet enhances borrowers’ cost of debt.

The rest of the paper is organized as follows. Section 2 provides the background of

the Chinese banking market. Section 3 describes our variables and methodology. Main

results are presented in Section 4. Section 5 concludes.

2. Overview of China’s banking market

Government intervention in the credit market is perceived as very common in

China (Berger et al., 2009; Jia, 2009). For a long time the People Bank of

China(hereafter PBOC), the central bank in China, limited the commercial banks’

pricing capacity on both deposits and loans by setting target interest rates along with

upper and lower bounds. In other words, Chinese banks could set the interest rates only

within the floating range on interest rates. Interest rates regulation has been gradually

relaxed over time. Prior to May 1996, lending institutions had to provide credit at the

exact interest rates mandated by the PBOC. During May 1996 through October 2004,

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the central bank designated both ceilings and floors for interest rates on loans made by

different types of lending institutions to different types of borrowing companies. The

ceiling and floor rates are set as certain percentage of a benchmark rate that depends on

loan maturity. For instance, in May 1996, the benchmark interest rate for a one-year

loan is 10.98%, and the ceiling rate for loans made by commercial banks to small-sized

enterprises is 110% of the benchmark, and the floor rate is 90% of the benchmark. The

interest rate ranges were expanded a few times during this period. In October 2004, the

PBOC eliminated the ceilings on interest rates for commercial banks' lending. In July

2013, ceilings and floors were removed for all bank loans, and banks gained full pricing

capacity.9 The interest rate regulation typically repressed the price of credit, which

leads to excessive demand. As a result, quantity-based control such as credit rationing,

in the forms of borrower rationing or loan size rationing, became widespread

(Kirschenmann, 2016). In such a credit environment, interbank competition is perceived

as very low, and borrowers would prefer to be “locked-in” with relationship banks.

Another salient characteristic of China’s banking sector is the state ownership of

banks. Compared to smaller lending institutions, the four largest, state-owned

commercial banks are subject to more government intervention in their credit decisions.

Although government intervention makes state-owned banks less efficient and have

poorer asset quality (e.g., La Porta et al., 2002; Barth et al., 2004), the Big Four still

dominate the banking market in China. According to the 2015 Almanac of China’s

Finance and Banking, during 2010-2014 the Big Four on average owned over 40% of

industry assets. Other players in China’s credit market include 12 joint-stock

commercial banks, city commercial banks, rural credit unions, and others.

9 Appendix Table A3 demonstrates the evolving process of regulation on loan interest rates in China.

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The Big Four differ from other players not only in size but also in ownership

structure. The actual controller of the Big Four banks is the central government

(Ministry of Finance), while non-Big Four banks have no controlling owner or are

controlled by provincial or city governments, enterprises, or individual investors.

Appendix Table A1 provides the information about actual controllers of China’s banks.

Bai et al. (2006) argue that compared with local governments, central governments care

more about social stability and would impose higher level of restrictions and

intervention on affiliated banks. In addition, Big Four banks have far larger percentages

of state ownership than others. Using data from banks’ annual reports and the

Bankscope database, Gao et al. (2017) show that the average and median state

ownership of the Big Four are over 10 times of those of non-Big Four banks. The

contrast in ownership structure means non-Big Four banks are probably subject to

substantially less political interference and behave more like profit-maximizers (Ferri,

2009).

The third defining feature of China’s banking market is the existence of

state-owned borrowers. Despite their dwindling number, SOEs still play an important

role in the economy.10

Extant evidence indicates that SOEs and non-SOEs receive

discriminative treatments in a state-dominated banking system (Brandt and Li, 2003;

Cull and Xu, 2003; Firth et al., 2009; Guariglia et al., 2011). Because state-owned banks

seek political and social goals similar to SOEs (e.g., Sapienza, 2004; Iannotta, Nocera,

and Sironi, 2013), they prefer to lend to SOEs over non-SOEs. In the event of financial

distress, the governments always channeled fiscal resources to state-owned firms to

keep them afloat. Government officials have an incentive to assist SOEs to obtain bank

10 According to Wind database, SOEs generated 38 percent of the industrial output in 2003, and this ratio declined

slowly to 22 percent in 2014.

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loans because they could gain political capital from the success of SOEs (Li and Zhou,

2005; Wang et al., 2008).

3. Data and Variables

3.1. Data

The sample construction starts with identifying the 1,704 IPOs on the Shanghai

Stock Exchange and Shenzhen Stock Exchange between 1999 and 2012. We then

manually collect these firms’ loan data prior to and after their IPOs. The pre-IPO loan

data are obtained from IPO prospectuses. The Chinese Securities Regulatory

Commission requires that a firm preparing to go public must disclose its loan contracts

information. This information typically includes the name of bank, the loan amount and

maturity, the interest rate, and other covenants of the loan contracts. We obtain the

post-IPO loan data from these companies’ annual reports, wherein details of loans are

typically available in notes to the financial statements. We collect loan data only for the

three years immediately following the IPO, because Pagano et al. (1998) find that the

information effects of IPO hold for no more than three years. For symmetry we use

pre-IPO loan data during the three years prior to the IPO. The sample thus obtained is

the first ever to have loan-level price information on China’s credit market. 11

For

robustness we also use loan data in only one year prior to IPO and one year post-IPO to

conduct our examinations.

In addition, we extract the firm-specific data from the China Stock Market and

Accounting Research (CSMAR) database as well as the Wind database. Bank-specific

information is from the BankScope database.

11 Extant studies on Chinese loan markets use yearly aggregate firm-level data from the China Stock Markets and

Accounting Research Database (CSMAR) (e.g. Chen et al., 2013), rely on loan-level datasets provided by a few

state-owned banks (Chang et al., 2014; Qian et al., 2015), or focus on non-price terms of the post-IPO loan contracts

( Xu et al., 2015; Gao et al.,2017).

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Following Hale and Santos (2009) and Schenone (2010), we exclude 32 financial

firms, 947 firms with only pre- or post-IPO loans, and 230 firms for which our control

variables are missing. This leaves us 10,534 loans for 495 IPO firms during 1996-2014,

with an average of about 21 loans per firm. Of these loans, 5,491 (52.1%) are made

pre-IPO, and 5,052 (48.9%) are made post-IPO; 3,690 (35.0%) are borrowed by SOEs,

and the rest by non-state-owned firms; 6,395 (60.7%) are made by the Big four banks,

and the rest by the non-Big Four banks. Appendix Table A4 shows the distribution of

loans in various categories.

3.2. Variables

Our main interest is in the change in interest rates on bank loans around IPOs.

Instead of the actual interest rate, we focus on the percentage spread (Spread), measured

as the difference in interest rate from the benchmark interest rate set by the PBOC as a

percentage of the benchmark rate.12

Because the benchmark rate reflects the PBOC’s

assessment of market conditions and differs for loans of different maturities, the spread

to some extent controls for market conditions and loan maturity. It is calculate as

*100 (1)

Actual interest rate Bechmark interest rateSpread

Bechmark interest rate

To capture the interest rate changes around IPOs, we create a dummy variable,

PostIPO, that takes the value of one for a loan that is made after the borrower firm’s

IPO, and takes the value of zero if made before the IPO. We also use two dummy

variables to differentiate different types of borrowers and lenders: SOE is equal to one

for loans of state-owned firms, and zero otherwise; Big4 is equal to one for loans made

12 Note this spread is relative to the benchmark interest rate set by the PBOC, different from those used in the

literature (e.g., Santos and Winton, 2008; Hale and Santos, 2009; Schenone, 2010) that are relative to LIBOR.

Besides, we use the percentage spread rather than the raw spread as the baseline measure. The rational is that the

actual interest rates in China’s banking market are often cited as a percentage of the benchmark rate. The PBOC set

ceilings and floors for interest rates also as certain percentages of the benchmark rate. For instance, a ceiling of 1.1

means the maximum interest rate is 110% of the benchmark.

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by the Big Four banks, and zero otherwise.

In the baseline model, we control for major loan, firm, and bank relationship

characteristics that are known to influence loan interest rates. Loan characteristics

include the maturity (Maturity), the loan amount (Amount), and whether a loan is

secured (Secured). Secured is equal to one if a loan has collaterals or guarantors, and

zero otherwise. Firm characteristics include firm age (Age), size (Size), asset tangibility

(Tangible), investment return (ROA), financial leverage (Leverage), and earnings

volatility (Earnings volatility). Size is measured as the logarithm of total assets in

millions of constant RMB yuan of year 1990. Asset tangibility is the sum of inventories

and plant, property and equipment as a ratio to total assets. Financial leverage controls

for the decline in financial risk of the borrower firm around IPO thanks to the equity

capital raised. Following Boubakri et al. (2013), earnings volatility is found as standard

deviation of operating profit margin (EBIT/Assets) in rolling periods of three preceding

years and proxies for the borrower’s operating risk. In robustness checks, we further

consider the post-IPO information asymmetry by bringing in IPO underwriter reputation,

analyst coverage, and information disclosure quality of the borrower firm to make sure

the loan rate declines around IPOs we document reflect the extent to which incumbent

banks’ information advantage dissolves.

We measure a firm’s banking relationship with the following three variables. At the

loan level, we follow Schenone (2010) to define Relationship intensity as the number of

loans a bank has made to the firm as a ratio to the total number of loans the firm has

received from all banks. At the firm level, we measure its Loan concentration in a year

as the Herfindahl-Hirschman Index (HHI) of bank shares. HHI proxies for the intensity

of competition among banks for a firm’s business; a higher HHI value corresponds to

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less competition for incumbent banks. At the bank level, we compute the share of a

bank (Bank share) in each of its borrowers’ total loan amount during a year. This

variable captures a bank’s competitiveness in securing business from its existing clients.

In addition, we control for the changes in China’s credit market conditions using

three dummy variables, Liberalization I，Liberalization II, and Recession. The first two

captures the process of interest rate liberalization in China during the sample period.

Liberalization I is equal to one for loans taken after the PBOC removed the ceilings for

commercial bank loans on October 28, 2004 but before the PBOC completely

eliminated the control on loan interest rates on July 19, 2013, and zero otherwise.

Liberalization II is equal to one for loans after July 19, 2013. The interest rate

liberalization may have mixed effects on banks’ information monopoly rents: it gave the

banks greater pricing capacity, encouraging information production of incumbents for a

consolidated advantage, and at the same time inspires pricing competition from

outsiders. Recession is equal to one for loans taken in recession periods. Santos and

Winton (2008) and Mattes et al. (2012) show that banks take greater information

monopoly rents during abnormal financial periods when increased uncertainty

magnifies difficulties of outside capital suppliers in evaluating the quality of borrowers.

Following them, we identify two recession periods using the Early Warning Index of

Macroeconomic Climate13

, respectively from July 1997 to October 2002, and from

January 2012 to December 2014.

[ INSERT TABLE 1 ABOUT HERE ]

13 The Early Warning Index of Macroeconomic Climate is a monthly index that measures the probability with which

the economy is in a recession using three categories of economic variables after the removal of measurement errors as

well as seasonal and other short-term fluctuations. In vein of Santos and Winton (2010) and Mattes et al. (2012), we

identify a recession as a period when the Early Warning Index is below its long-run average for at least four

consecutive quarters.

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Table 1 provides detailed variable definitions. Statistic summary of the variables in

our sample is presented in Table 2. The actual interest rate averages 6.04% and ranges

between 0.6% and 14.94%. Spread on average is negative at -0.114, indicating the

actual loan rates are merely 0.114 percent, or 0.7 basis points, lower than the average

benchmark rate. The median spread is zero. However, actual rates can be widely

deviated from the benchmark, with the spread ranging from -66.93 percent to 50 percent.

About 48% of loans in our sample are made after borrowers’ IPOs. Thirty five percent

of them are loans to SOEs, and the Big Four make 60.6% of the loans. Loan maturity

ranges from about a week (0.017) to 25 years, with an average of about 2 years and a

median of 1 year. Thus roughly half of the loans are short-term loans. The average loan

amount is 41.81 million yuan. About three fourth of the loans are either collateralized or

guaranteed.

An average borrower firm is 6.8 years old, with total assets of 4.247 billion yuan

and a ROA of 7.8%. Tangible assets and debt account for 42.7% and 44.7%,

respectively, of the firm’s total assets. Earnings volatility averages 0.104.

The HHI measuring loan concentration at the firm level averages 0.338, ranging

from nearly zero to one. Relationship intensity has a mean of 0.404, indicating that for

an average loan, its bank has made two loans out of every five loans the firm has

received. More than 10 percent of the loans are from banks which have not lent any

loans to the borrower in the prior years. For a bank, on average it provides funds to its

clients that amounts to 30.8 percent of their total borrowing in a year.

[ INSERT TABLE 2 ABOUT HERE ]

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4. Results

4.1. Changes in loan and firm characteristics around the IPO

Table 3, Panel A presents the comparison of loan spread and other characteristics

around borrowers’ IPOs. Pre-IPO loans have on average a positive spread of 0.989%,

while post-IPO loans’ average spread is negative at -1.314%, representing a decline of

2.303 percentage points that is statistically significant at the one percent level. If the

benchmark rate is 6%, then such a decline amounts to 14 basis points. According to the

literature, this decline in loan spread is indicative of the information asymmetry rents

banks charge. For loans of SOEs, this decline in spread is 4.274 percentage points,

much larger than that for loans of non-SOEs (1.470 percentage points), consistent with

our conjecture that SOEs suffer from greater information asymmetry rents. After a firm

goes public, loan maturity lengthens and the amount increases, and fewer loans need

collaterals or guarantees, all consistent with IPOs moderating information asymmetry of

issuing companies.

Panel B presents the changes in banking relationship characteristics around IPOs.

An average firm’s loan concentration (HHI) is 0.356 prior to its IPO, which declines to

0.318 after the IPO. Relationship intensity also declines from 0.427 to 0.378. At the

bank level, a bank’s average loan share declines from 0.362 to 0.335. Thus a firm use

loans from more banks post-IPO, as predicted by the theory. Notably, the decline in

SOEs’ loan concentration (-0.060) is more pronounced than that in non-SOE firms’;

relationship intensity for SOEs’ loans declines more (-0.106) around IPOs than for

non-SOE borrowers; banks’ shares in SOE loans also witness a greater decline (-0.039)

than that in non-SOE loans (-0.025). These comparisons all point to that SOEs’

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borrowing opportunities improve after the IPO by a larger degree than non-SOE firms,

indicating that the hold-up problem prior to their IPOs is more severe for SOEs.

[ INSERT TABLE 3 ABOUT HERE ]

4.2. State ownership and Cost of Bank Loan around IPO

Table 4 reports the first set of multivariate tests that investigate the determinants of

loan interest rate. Specifically, we estimate the following model,

𝑆𝑝𝑟𝑒𝑎𝑑𝑖𝑗𝑡 = 𝛼𝑖 + 𝛾𝑡 + 𝛽1𝑃𝑜𝑠𝑡𝐼𝑃𝑂𝑖𝑗𝑡 + 𝛽2𝑆𝑂𝐸𝑖𝑡 + 𝛽3𝑃𝑜𝑠𝑡𝐼𝑃𝑂𝑖𝑗𝑡 × 𝑆𝑂𝐸𝑖𝑡

+ 𝛿𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠 + 휀𝑖𝑗𝑡 (2)

where i, j, and t are subscripts for borrower firm, lending bank, and year of the loan,

respectively. The dependent variable is the interest rate spread. Main explanatory

variables are PostIPO, the indicator of a post-IPO loan, SOE, the indicator of a SOE

firm, and their interaction. Control variables include loan characteristics, firm

characteristics, and banking relationship characteristics. Firm and year fixed effects are

controlled for.

In column (1) we run the regression in the full sample, and PostIPO is the variable

of interest. It receives a negative coefficient of -1.415, statistically significant at the one

percent level. It indicates that, holding all else constant, an average firm’s loan spread

declines by 1.415 percentage points after its IPO. Assuming a 6 percent benchmark

interest rate, this represents a rate decline of 8.5 basis points. As the IPO disseminates a

large amount of information to the credit market, we interpret this rate decline as

evidence of information monopoly rents prior to the IPO in the Chinese banking market.

To examine whether state ownership influences information monopoly rents, we

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run the same regression in the subsample of SOE loans and non-SOE loans, separately,

and report the results in column (2) and column (3), respectively. In the SOE subsample,

the coefficient on PostIPO is -3.059, statistically significant at the one percent level; in

contrast, in the non-SOE subsample, this coefficient is much lower in magnitude, -0.857,

which is not statistically different from zero. This contrast indicates SOEs suffer from

more severe information monopoly rents, consistent with our hypothesis. To verify this

finding, we run a regression in which SOE, an indicator variable that is equal to one for

loans of SOEs and zero otherwise, as well as its interaction with PostIPO, are brought

in. Column (4) presents the results. PostIPO receives a coefficient of -0.972, that is of

marginal significance statistically. This coefficient captures the information monopoly

rents charged on non-SOE borrowers. The coefficient on SOE is negative but not

statistically different from zero. The interaction term between SOE and PostIPO obtains

a negative and statistically significant coefficient of -1.575, capturing the difference

between SOEs and non-SOE firms in spread decline around the IPOs, corroborating the

subsample regression results. Assuming a 6% benchmark interest rate, this coefficient

indicates that the raw interest rate decline around IPO for SOEs is 9.5 basis points larger

than for non-SOEs. In short, the results show that firms are able to borrow from banks

at lower interest rates after their equity IPOs, thanks to the information releasing effect

of IPOs, and SOEs enjoy greater interest rate declines than their non-SOE peers,

consistent with our hypothesis.

[INSERT TABLE 4 ABOUT HERE ]

Most control variables load in the regressions, speaking for their impacts on cost of

bank loans. Among firm characteristics, we find that the percentage spread decreases

with the loan maturity. This negative association is probably mechanical because a same

19

raw spread represents a smaller percentage spread for a longer term loan with a greater

benchmark interest rate. The spread decreases as the loan amount increases, consistent

with the economies of scale in loan size (Hale and Santos, 2009; Fields et al., 2012).

Secured obtains a negative coefficient, because collaterals and guarantees are sought

only for riskier credits (e.g., Berger and Udell, 1990). Coefficients on firm

characteristics are well expected, showing that firms that have more tangible assets and

lower leverage enjoy lower interest rates. It is worth noting that the inclusion of

financial leverage controls for the decline in financial risk thanks to the equity capital

raised in the IPO. Earnings volatility and loan concentration both load positively,

indicating that riskier firms and firms facing low bank competition pay higher cost for

their loans. Relationship intensity does not load. A potential interpretation is that loan

concentration is an alternative measure of the strength of banking relationship, which

subsumes the effect of relationship intensity observed in Schenone (2010).

4.3. Robustness checks

4.3.1. Alternative samples

We check the robustness of the above results in this section. First, we shorten the

length of window for each sample firm from six years around its IPO, i.e., three years

pre-IPO and three years post-IPO or the [-3,3] period, to two years around the IPO, i.e.,

one year pre-IPO and one year post-IPO or the [-1,1] period. This change shrinks the

sample size but minimizes the variations in loan interest rate attributable to factors not

related to the IPO information releasing event. We run the same regressions as above

the reports the results in Table 5, columns (1)-(3). In the subsample of SOE loans,

20

PostIPO receives a statistically significant coefficient of -3.597; in the subsample of

non-SOE loans, the same coefficient is -1.417, statistically different from zero. In the

full sample regression augmented with the SOE indicator variable and its interaction

with PostIPO, PostIPO barely loads and the interaction term loads with a negative

coefficient of -0.972. These results are qualitatively identical to those in Table 4.

In a second check, we restrict our sample to only loans that have counterpart loans

from the same banks in the other time period across the IPO event. To illustrate, assume

firm A has a loan from Bank M and a loan from Bank N during the three years prior to

its IPO, and a loan from Bank M during the three years post-IPO. Then only the two

loans from Bank M will be kept and the loan from Bank N drops out. This additional

restriction ensures that we compare interest rates between the same bank-firm pairs

during the pre- and post-IPO periods. The results remain qualitatively unchanged as

presented in columns (4)-(6) in Table 5.

[ INSERT TABLE 5 ABOUT HERE ]

4.3.2. Alternative interpretation: Change in ownership structure

Besides information monopoly power of incumbent banks, the change in ownership

structure of SOEs may give rise to the SOEs vs. non-SOEs discrepancy in loan rate

decline we have documented. As we discussed, SOEs pursue both financial and social

goals. In an IPO, an SOE sells newly-created and/or existing shares to new investors,

including other state-owned institutions and corporations, privately-owned institutional

investors and individual investors. If private investors gain greater influence on the

SOE’s business decisions, they will move its objective towards profit maximization.

Increased profit maximization incentives then could drive managers to become more

21

aggressive negotiators when dealing with banks, which in turn would lead to a drop in

the loan interest rate. Non-SOEs, however, are already profit maximizers before IPOs,

and probably won’t experience a similar change in incentives or the interest rate.

To examine this alternative interpretation, we first tabulate the ownership structure

of SOEs in our sample pre- and post-IPO in Appendix Table A2. Panel A shows that

only 4 out of 150 SOEs changed their ultimate controllers within 3 years after IPO. Yet,

all the 4 new ultimate controllers are a government. In other words, all SOEs remain

state-owned post-IPO and still facing the dual objectives problem. Out of all SOEs in

our sample, 20 experienced a non-negative change in state ownership, and the rest

decreased their state ownership. State ownership declines by less than 20% for 36 firms,

by greater than 40% for 15 firms, and by an extent between 20% and 40% for 79 firms.

We then consider the interest rate declines around IPOs given various levels of

change in state ownership and report the results in Table 6. First, we divide all SOEs

into two groups: Group 1 includes those that experienced an increase or minor decrease

(less than 20%) in state ownership around IPO, and Group 2 includes those with a major

decrease (over 20%) in state ownership. If ownership structure change is the cause of

interest rate decline, we would expect Group 2 SOEs exhibit greater rate declines

around IPO. Regressions of interest rate spread on PostIPO and all control variables, as

shown in the first two columns of Table 2, produce negative and statistically significant

coefficients on PostIPO, indicating both groups of SOEs witness a decline in loan cost

after IPO. However, the coefficient for Group 1 (-4.083) is greater in magnitude than

that for Group 2 (-3.253), inconsistent with the conjecture that ownership structure

change matters. To further attack this issue, we run the Equation (2) regression in

subsamples that include SOEs with a nonnegative change in state ownership and

22

non-SOEs (Column 3). The result is very similar to the baseline results reported in Table

4. The coefficient of SOE×PostIPO is -5.209, statistically significant at the one percent

level, confirming that in absence of a decline of state ownership, SOEs on average

experience a markedly larger decline in loan rate. In Column 4, we exclude from the

sample only those SOEs with a state ownership decline greater than 40%, and the

results are qualitatively similar. Thus we can conclude that the consideration of

ownership structure does not alter our main findings, i.e., greater information monopoly

causes SOEs’ interest rates to decline by more after IPO.

[ INSERT TABLE 6 ABOUT HERE ]

4.3.3. More alternative interpretations

Loan interest rates may go lower post-IPO as the result of a decline in perceived

risk of issuer firms. Pagano et al. (1998) and Hsu et al. (2010) point out that equity

issuance lowers the issuer’s debt ratio, enhances its ability of service existing debt, as

well as raise its debt capacity. The reduced financial risk would lead to lower cost of

debt. This effect would cause a similar decline in interest rates around IPO as the

relationship banks’ information monopoly rents. In all our prior investigations, we

control for a borrower firm’s financial risk using its debt-to-asset ratios and earnings

volatility both before and after its IPO. Both variables load positively, consistent with

the positive relationship between financial risk and cost of credit. Untabulated

comparison show that our sample firms on average exhibit a drop in both debt ratios and

earnings volatility post-IPO, indicating post-IPO firms do have reduced financial risk.

However, these findings do not subsume our information monopoly rent interpretation.

Underwriter reputation could exert a certification effect (e.g., Carter and Manaster,

23

1990), and analyst coverage following IPOs would lead to improved investor

recognition. One can argue that both may positively contribute to IPO firms’ future cash

flows and give rise to lower interest rates. The results in Table 10 show that our findings

about information monopoly rents remain intact in the presence of underwriter

reputation and analyst coverage.

4.3.4. Endogeneity of IPOs

Our detection of information monopoly rents hinges on the information

dissemination effect of IPOs. A concern is that IPOs are not exogenous, and in particular,

IPO decisions and the decline in loan interest rates may be driven by a same set of

factors, for example, improved market conditions. If this is the case, then the decline in

loan interest rates cannot be interpreted as the result of IPO information dissemination.

Similar to Hale and Santos (2010), we employ the match sample approach to address

this concern. The idea is that as the treatment sample (the listed firms) are similar to the

control sample (the unlisted firms) in all characteristics but the IPO, the difference in

interest rate patterns between the two matched samples is attributable to the IPO event.

For a firm in our sample, the pool of candidate control firms includes all other firms

in year -2 relative to their respective IPO years. The one-year gap between the control

firm-year and the IPO year minimizes the possibility that the IPO information

dissemination has begun. To prevent overlapping years, we restrict our investigation

period to the one-year period before the IPO date and the one-year after the IPO date,

i.e., the [-1, 1] period. The result is that for any pair of treatment firm and control firm,

we have two years around the IPO for the treatment firm, and two years for the control

firm. For instance, Firm A went public on May 31, 2010 and we have its loan

information during 2007-2013, three years prior to the IPO year and three years

24

following the IPO year. This firm in year 2008 then can be a candidate control firm.

Suppose it is matched to Firm B that went public on Nov 1, 2006. We keep loans of

Firm B during Nov 1, 2015-Oct 31, 2017 as the treatment observations, and use loans of

Firm A during Jun 1, 2007-May 31, 2009 as the control observations.

To find the control firms, we first estimate the Probit model of having the equity

IPO in any given year, using as explanatory variables a set of firm characteristics as

follows,

𝑃(𝐼𝑃𝑂𝑖𝑡) = Φ(𝛼 + 𝛽𝑋𝑖𝑡−1) (3)

where IPOit is an indicator that firm i issued an IPO in year t, Xit-1 is the vector of

aforementioned firm characteristics, and Φ(.) is the cumulative probability density

function of the normal distribution. We use the estimated values of α and 𝛽 to

construct the propensity score for each firm i in year t as the predicted probability of an

equity IPO.

Using this propensity score, we utilize radius matching (Radius=0.01) to match IPO

firms to those that have not yet issued a public equity. We do not force each equity IPO

firm to have a matching non-IPO firm; instead, we drop those IPO firms that do not

have a close match. We also drop the non-IPO firms that are not used as a match for any

IPO firm. Thus, our matched sample consists of only those firms that are similar in their

probability of issuing public equity. In the first attempt, we require that a non-IPO firm

can be used as a control for one IPO firm (one-to-one matching), and obtain 67 pairs of

IPO firms and control firms, with in total 1,282 loans during the [-1,1] periods. In

another attempt, we allow a non-IPO firm to be controls for multiple IPO firms

(one-to-N matching), and find 182 private firms to match 297 IPO firms combined

25

having 3,045 loans. In either the matched sample, we create a dummy variable,

Treatment, that is equal to one for an IPO firm and zero for a match firm.

We then re-run the regression (2) in the matched sample with the additional

interaction between PostIPO and Treatment, and the three-way interaction among SOE,

PostIPO, and Treatment, on the right-hand side. For a control firm, PostIPO is set to

take the value one if the loan is taken in the second half of the two-year period, and take

the value zero if the loan is in the first half of the period. Effectively we assume a

hypothetical IPO date for the control firm that is the day exactly two years prior to its

actual IPO date, and contrast the loan interest rate change around the hypothetical IPO

date of the control firm against that around the IPO date for the IPO firm. The

regression with the two aforementioned interaction terms hence constitute

difference-in-difference tests that can reliably expose the information effect of IPO as

well as whether SOEs are victims of greater information monopoly rents.

[ INSERT TABLE 7 ABOUT HERE ]

Table 7 reports the regression results, with those based on the one-to-one matching

in columns (1) and (2), and those based on the one-to-N matching in columns (3) and

(4). In (1) and (3), the interaction term PostIPO×Treatment loads negatively, which

tells that the interest rate spread declines only around the actual IPOs, confirming the

existence of information monopoly pre-IPO. In (2) and (4), the three-way interaction

term SOE×PostIPO×Treatment also loads negatively, confirming our baseline finding

in Table 4 that SOEs are subject to more severe information monopoly rents. Therefore,

controlling for the potential endogeneity of equity IPO decisions does not alter our

results.

26

4.3.5. Interest rate liberalization

During our sample period, China’s banking sector underwent gradual

liberalization of interest rates, as depicted in Figure 1. The gray area represents the

allowable interest rate range that expands over time. The process exhibits two notable

observations. First, actual interest rates of commercial banks (dotted line) closely track

the official benchmark interest rates (gray line). Second, interest rate spread (solid

line), the percentage gap between actual and benchmark interest rates, fluctuates around

the average value of zero. There is not a discernable pattern how the expansion in

allowable interest rate range influences the interest rate spread.

Theoretically, the impact of interest rate liberalization on banks’ information

monopoly rents is unclear as it provides incentives to both incumbent and outside banks

to engage in information production. On the one hand, interest rate regulation restricts

banks’ pricing capacity. Under such restrictions, banks can earn substantial economic

rent privileged by government authorities (Hutchison and Pennacchi, 1996; Kwan,

2003), but limited monopoly rent involving information advantage, because deficient

loan pricing ability impedes the complete compensation for cost of information

production, which dampens incumbent banks’ information production incentives.

On the other hand, interest rate regulation is also a source of monopoly power for

incumbent banks (Salas and Saurina,2003), as information monopoly rents can be

partially or completely competed away via lower rates offered by outside lenders when

interest rates is liberalized. Besanko and Thakor (1992) find that increased competition

introduced by banking deregulation erodes informational rents associated with

relationship banking.

Despite the ambiguous implication of interest rate liberalization on commercial

27

banks’ loan interest rate, we control for its potential impact by employing two dummy

variables, Liberalization I and Liberalization II, to capture the two major steps in the

process of China’s interest rate liberalization process. Liberalization I takes the value of

one for loans made during the period from October 28, 2004 to July 19, 2013, after the

PBOC removed the ceilings for commercial bank loans interest rates. Liberalization II

is equal to one for loans taken after July 20, 2013, when loan interest rates liberalization

was completed and loan interest rates began to be determined mainly by the market

force.

We add Liberalization I and Liberalization II to the right-hand side of model (2).

Regression results based on three different specifications are in Table 8. In

specifications (1)-(3), neither SEO nor the interaction terms are included; in

specification (4), SOE and its interaction with PostIPO is brought back in. The two

liberalization indicators obtain positive coefficients in the full sample and the SOE

subsample, and load in the latter. They receive negative but insignificant coefficients in

the non-SOE subsample. These results suggest that as banks gain more discretion in

determining loan interest rates, they tend to raise the interest rates for SOEs but cut

those for non-SOE. Our variable of interest is still the interaction term SOE×PostIPO,

which receives a negative and statistically significant coefficient in column (4),

indicating after controlling of interest rate liberalization, SOEs still experience more

pronounced interest rate cuts after their IPOs. Thus the interest rate liberalization does

not undercut our finding that interest rate decline around IPOs is the result of dissolving

information monopoly.

[ INSERT TABLE 8 ABOUT HERE ]

28

4.4. The Big Four banks and information monopoly rents

We then proceed to investigate whether the Big Four banks behave differently from

other banks in exploiting proprietary information they hold of borrowers. Univariate

comparisons are presented in Table 9. In the full sample, loans made by the Big Four

charge a slightly lower spread (-0.821 percentage points) after the borrower IPOs, while

non-Big Four banks lower their loan spreads by a much larger discount of 4.384

percentage points. In the subsample of SOE loans, the Big Four and non-Big Four banks

both witness a decline in interest rate spread of about 4 percentage points. When the

borrowers are non-SOE firms, a stark contrast arises: the Big Four banks’ percentage

loan spreads on average increase by 1.179 percentage points, while those for other

banks decline by 4.581 percentage points. These comparisons seem to suggest that the

Big Four enjoy lower information monopoly rents, especially when they lend to

non-SOE firms.

[ INSERT TABLE 9 ABOUT HERE ]

Table 10 reports multivariate analyses. In these analyses we run a regression that

has Big4, the indicator of a loan from a Big Four bank, and its interaction with PostIPO

as additional independent variables. Our main variable of interest is the interaction term.

Column (1) presents the estimates in the subsample of SOE loans. PostIPO (Big4) has a

negative (positive) but statistically insignificant coefficient, but their interaction term

loads negatively at the five percent level of statistical significance. The sum of the

coefficient on PostIPO and that on the interaction is -4.471, and it is statistically

significant at the one percent level, as shown at the bottom of the table. When a SOE

29

borrows from a non-Big Four bank, its cost of debt does not change significantly around

its IPO; When it borrows from a Big Four bank, its interest rate spread relative to the

benchmark rate declines by 4.471 percentage points. When we consider a benchmark

rate of 6%, this amounts to a decline of 27 basis points in raw interest rate, which is

economically substantial.

When the borrower is a non-SOE firm, PostIPO, Big4, and their interaction all

receive statistically significant coefficients, which are -2.805, -2.012, and 3.357,

respectively, as shown in column (2). The negative coefficient on Big4 indicates that

overall the Big Four banks charge lower rates on non-SOE loans than other banks,

consistent with their role of promoting growth in the economy. When non-SEO firms

borrow from a non-Big Four bank, its interest rate would decline by 2.805 percentage

points after its IPO; if it borrows from a Big Four bank, however, no interest rate

decline can be expected (net effect is 0.552 = -2.805 + 3.357, which is not statistically

significant.)

In column (3), the estimates are for the full sample, including loans to both SOE

and non-SOE borrowers. The results are qualitatively similar to those in column (2), in

that PostIPO and Big4 load negatively, their interaction loads positively, and the sum of

the PostIPO coefficient and the interaction coefficient is not significantly different from

zero. The subsample and full-sample results combined indicate that the Big Four banks

charge information monopoly rents only on SOEs, and overall they enjoy lower rents

than their smaller peers, consistent with our hypothesis.

[ INSERT TABLE 10 ABOUT HERE ]

30

4.5. Controlling for Post-IPO Information Asymmetry

Thus far we have used the IPO as the information releasing event, and interpret the

decline in loan interest rates as the result of vanishing information monopoly. An

implicit assumption is in place for us to compare information monopoly rents across

SEOs vs. non-SEOs, and the Big Four vs. non-Big Four banks: A bank’s information

monopoly, if any, completely vanishes after the borrower IPO. In other words, the IPO

process perfectly eradicates asymmetric information. We acknowledge this is a very

strong assumption as a wide array of stock market evidence show that varying levels of

information asymmetry exist on publicly-listed companies. The decline in loan interest

rate thus captures only the fraction of information asymmetry that is eliminated by the

IPO information dissemination event.

To address this issue, we control for the residual information asymmetry after IPO

using three additional variables: Underwriter reputation, Analyst coverage, and

information disclosure quality. During the bookbuilding procedure that prevails in

China’s IPO market, investment banks acquire information about the issuer quality

through due diligence as well as soliciting indication of interest from informed investors

(e.g., Benveniste and Spindt, 1989; Hanley, 1989; Ljungqvist and Wilhelm, 2002), and

more reputable underwriters produce more information than less reputable ones (Wang

and Yung, 2011). We measure a borrower’ IPO underwriter reputation using the market

share of the lead underwriter during the year (e.g., Beatty and Ritter, 1986; Megginson

and Weiss, 1990 ). Analysts are important information producers (e.g., Hong et al., 2000;

Frankel and Li, 2004) and we use the log of one plus the average number of financial

analysts following a firm after its IPO to capture the post-IPO information asymmetry.

31

Besides, post-IPO information asymmetry depends on the quality of information

disclosure by the publicly-listed companies themselves, as low disclosure quality

creates uncertainty about, among others, the credibility of financial statements (Graham

et al., 2008). We measure information disclosure quality by a dummy variable that is

equal to one if a firm is criticized for or charged with violations of information

disclosure regulations by the stock exchange where its stocks trade, or China Securities

Regulatory Commission (CSRC) in the [-3,3] event window, and zero otherwise.

We re-run all the regressions with the three post-IPO information asymmetry

measures, Underwriter reputation, Analyst coverage and Information disclosure quality,

as additional control variables, in a smaller sample where the values of these variables

are available. Table 11 reports selected regression results but all other results are similar.

While Underwriter reputation and Analyst coverage do not load, Information disclosure

quality obtains positive and statistically significant coefficients; nevertheless, they do

not alter our main results. PostIPO loads negatively in all specifications, SOE×PostIPO

loads negatively in column (2), and Big4×PostIPO loads positively in column (3). Thus

after controlling for post-IPO information asymmetry, SOEs still benefit more from the

leveling of information asymmetry among commercial banks, and Big Four banks lose

more of its information advantage around the borrower IPOs.

[ INSERT TABLE 11 ABOUT HERE ]

5. Conclusion

In this paper, we empirically investigate how Chinese banks exploit the

information monopoly. Theory suggests that an information advantage enables a

32

incumbent bank to hold up borrowers for higher interest rates. Since a large amount of

information about a firm is disseminated at the time of its equity IPO, it will erode the

incumbent bank’s information advantage and force it to cut loan interest rates after the

borrower’s IPO. The decline in interest rates around a firm’s IPO hence indicates the

existence of information monopoly rents banks enjoy prior to the IPO.

As governments own some of borrower firms as well as some of the banks, China’s

credit market provides a unique laboratory to examine how state ownership influences

the cost of credit. We argue that SOEs suffer from worse information asymmetry,

aggravated adverse selection problem, and inefficient risk-taking, stemming from the

dual objective problem, nontransferability of state ownership, and implicit government

guarantees. As a result they often face high switching costs in the credit market and pay

high information monopoly rents to their incumbent lenders. The state-owned Big Four

banks, pursuing political and social goals rather than purely profit maximization, place

less emphasis on information production in the lending process, and hence would enjoy

lower information monopoly rents than their smaller, privately-owned peers, and this

effect is more pronounced when borrowers are non-SOE firms.

Using a large proprietary sample of bank loan contracts in China spanning the

period from 1996 to 2014, we find that loan interest rate spreads (relative to the

benchmark rate set by the central bank) decline after firms undertake their IPOs. This

indicates that Chinese banks overall do enjoy information monopoly rents, despite their

lack of pricing capacity during most of our sample period. SOEs experience

significantly larger decline in loan interest rates around their IPOs than non-SOE

borrowers. The Big Four banks cut their loan rates by a smaller percentage than non-Big

Four banks after the borrowers go public, and the Big Four’s rate cuts are concentrated

33

in loans made to SOEs. All these results are consistent with our expectations, and robust

to a wide array of robustness checks.

Our paper represents the first attempt to understand the lending relationships

confounded by government ownerships and interventions. It extends the existing

literature of banking relationships by providing an institutional angle to view the

variation in information monopoly rents in the credit market. Our findings reveal yet

another implication of state ownership in business management. While promoting social

welfare, state ownership undercuts the efficiency of both parties in the credit market in

that it increases the cost of debt for borrowers and weakens banks’ incentives for

efficient credit allocation.

34

Appendix Table A1. Controllers of banks in China

This table discloses the actual or ultimate controllers of the commercial banks in China since 2004

when Big Four become privatized and publicly listed on both domestic and Hong Kong stock

exchanges.

Data Sources: Banks’ annual financial reports, CSMAR and Bankscope databases.

Bank Name Bank Type Actual Controller（2004-2015）

Bank of China(BOC) Big Four

Ministry of Finance (Through Central Huijin

Investment Co.)

Industrial and Commercial Bank of

China(ICBC)

Big Four Ministry of Finance (Through Central Huijin

Investment Co.)

Agricultural Bank of China(ABC)

Big Four Ministry of Finance (Through Central Huijin

Investment Co.)

China Construction Bank(CCB)

Big Four Ministry of Finance (Through Central Huijin

Investment Co.)

China Merchants Bank(CMB) Non Big Four China Merchants Group

Shanghai Pudong Development

Bank(SPDB)

Non Big Four State Asset Supervision and Administration

Commission of Shanghai

China Everbright Bank(CEB) Non Big Four Central Huijin Investment Co.

Industrial Bank(CIB)

Non Big Four State Asset Supervision and Administration

Commission of Fujian

Huaxia Bank(HXB)

Non Big Four State Asset Supervision and Administration

Commission of Beijing

China Guangfa Bank(CGB)

Non Big Four State Asset Supervision and Administration

Commission of Guangdong

China Bohai Bank

Non Big Four State Asset Supervision and Administration

Commission of Tianjin

Bank of Communications

(BOCOM)

Non Big Four No Actual Controller

China Citic Bank(ECITIC) Non Big Four No Actual Controller

PingAn Bank(PAB) Non Big Four No Actual Controller

China Zheshang Bank(CZB) Non Big Four No Actual Controller

HengFeng Bank(HFB) Non Big Four No Actual Controller

China Minsheng Banking(CMBC) Non Big Four No Actual Controller

City Commercial Banks Non Big Four Urban enterprises, citizens or local

governments

35

Panel A. Change of ultimate controller around IPO

(0, 3)

SOEs 4 4 150

Non-SOEs 3 0 345

All 7 4 495

Pane B. Changes in state ownership for SOEs around IPO

< 50% [50%, 90%] > 90%

≥ 0 20 0 0 20

(-20%, 0) 13 21 2 36

(-40%, -20%) 3 25 51 79

≤ -40% 0 5 10 15

Appendix Table A2. Change in Ownership Structure around IPO

Period relative to IPO date (in years) New state

controller# of firms

State ownership pre-IPOAll

Panel A shows the number of firms whose ultimate controller has changed around IPO. Panel B

shows the changes in state ownership for SOEs around IPO. The data source is CSMAR database

and Wind database, complemented with hand-collected information from firms' prospectuses and

annual reports.

Range of Change

(-1, 1)

0

0

0

36

Appendix Table A3. Loan interest rates liberalization in China

This table shows the process of loan interest rates liberalization in China. Prior to 1996/05/01, loan interest

rates were set as fixed by the PBOC, and banks were completely deprived of loan pricing capacities. On

1996/05/01, the PBOC relaxed its control on interest rates by setting upper and lower bounds for interest

rates around the benchmark rates, forming different floating ranges based on types of banks and borrowers.

The floating ranges were then expanded several times. On 2004/10/29, the PBOC eliminated the ceilings

on loan interest rates for commercial banks' lending. On 2013/07/20, the process of loan interest rates

liberalization in China was completed with PBOC’s final removal of interest rate bounds for all types of

loans. Since then, financial institutions could, in theory, independently decide on lending rates based on

market forces.

Sources: The People ’s Bank of China (PBOC) website (http://www.pbc.gov.cn/).

Floating ranges of loan interest rates

Periods

Commercial banks Urban credit cooperatives Rural credit

cooperatives

Small sized

enterprises

Large and

medium-sized

enterprises

Small sized

enterprises

Large and

medium-sized

enterprises

All

enterprises

-1996/04/30 1 1 1 1 1

1996/05/01-1998/10/30 [0.9, 1.1] [0.9, 1.1] [0.9, 1.1] [0.9, 1.1] [0.9, 1.4]

1998/10/31-1999/08/31 [0.9, 1.2] [0.9, 1.1] [0.9, 1.2] [0.9, 1.1] [0.9, 1.5]

1999/09/01-2003/12/31 [0.9, 1.3] [0.9, 1.1] [0.9, 1.3] [0.9, 1.1] [0.9, 1.5]

2004/01/01-2004/10/28 [0.9, 1.7] [0.9, 1.7] [0.9, 1.7] [0.9, 1.7] [0.9, 2]

2004/10/29-2012/06/07 [0.9, ∞] [0.9, ∞] [0.9, 2.3] [0.9, 2.3] [0.9, 2.3]

2012/06/08-2013/07/19 [0.8, ∞] [0.8, ∞] [0.8, 2.3] [0.8, 2.3] [0.8, 2.3]

2013/07/20 [∞, ∞]

37

Appendix Table A4. Loan distributions in our sample

This table shows the shares of different lenders (the Big Four vs. others) and borrowers (SOEs and

non-SOEs) in our sample. Big4 refer to the four largest, state-owned banks, namely, the Agricultural

Bank of China (ABC), the China Construction Bank (CCB), the Bank of China (BOC) and the

Industrial and Commercial Bank of China (ICBC). A SOE is a firm whose ultimate controlling

shareholder is a local or central government. Panel A and Panel B presents the distributions in loan

transactions and loan amount, respectively.

Panel A. Distribution of loan transactions

Full sample Pre-IPO Post-IPO

Transactions Percent Loan Percent Loan Percent

SOE

Big4 2673 72.44% 1701 76.11% 972 66.80%

Non-Big4 1017 27.56% 534 23.89% 483 33.20%

Total 3690 100% 2235 100% 1455 100%

Non-SOE

Big4 3719 54.27% 1866 57.31% 1853 51.52%

Non-Big4 3134 45.73% 1390 42.69% 1744 48.48%

Total 6853 100% 3256 100% 3597 100%

Panel B. Distribution of loan amounts

Full sample Pre-IPO Post-IPO

Amount Percent Amount Percent Amount Percent

SOE

Big4 3038.18 69.10% 1682.82 73.79% 1355.35 64.05%

Non-Big4 1358.55 30.90% 597.834 26.21% 760.71 35.95%

Total 4396.73 100% 2280.66 100% 2116.07 100%

Non-SOE

Big4 4644.36 53.82% 2235.86 56.05% 2408.50 51.90%

Non-Big4 3985.44 46.18% 1753.39 43.95% 2232.05 48.10%

Total 8629.81 100% 3989.25 100% 4640.55 100%

38

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41

Variable Definition

Actual Interest rate The loan’s raw interest rate.

SpreadThe difference of a loan interest rate from the benchmark interest rate set by the

People's Bank of China, expressed as a percentage of the benchmark rate,

Post IPO A dummy variable, equal to one if loan takes out after firm’ s equity IPO, and zero

otherwise.

SOE A dummy variable, equal to one if the ultimate controlling shareholder of a borrower is

the central government or a local government, and zero otherwise.

Big4 A dummy variable, equal to one for the loans made by the four first tier commercial

banks in China: Agricultural Bank of China (ABC), the China Construction Bank

(CCB), the Bank of China (BOC) and the Industrial and Commercial Bank of China

(ICBC), and zero otherwise.

Maturity Loan maturity, measured in years.

AmountThe log of one plus loan amount, where the loan amount is in millions of constant

RMB yuan of year 1990.

SecuredA dummy variable equal to one if the loan has collaterals or guarantors, and zero

otherwise.

Age The number of years since incorporation.

ROA Net income divided by assets.

Tangible Inventories plus plant, property, and equipment over assets.

Size Log of assets in millions of constant RMB yuan of year 1990.

Leverage Total debt over assets.

Earnings volatility The standard deviation of EBIT-to-asset ratio in rolling periods of three preceding

years.

Loan concentration The Herfindahl-Hirschman Index (HHI) on total banking lending in a year.

Following Schenone (2010), Relationship intensity is defined as:

Bank share A bank’s loan share in a borrower’ total loan amount in a year.

Liberalization I A dummy variable, equal to one for loans taken after 2004/10/28, when the PBOC

removed the ceiling for commercial bank interest rates (See Table A2), and before

2013/07/19, when the PBOC completely removed the restrictions on interest rates,

and zero otherwise.

Liberalization II A dummy variable, equal to one for loans taken after 2013/07/19, when the PBOC

completely removed the restrictions on interest rates, and zero otherwise.

Recession A dummy variable, equal to one for loans taken during recession periods, Jul 1997 to

Oct 2002, and Jan 2012 to Dec 2014.

Underwriter reputation Market share of the IPO lead underwriter in China's IPO market during the year.

Analyst coverage Log of one plus the average number of analysts following the firm in the three years

after its IPO.

Information disclosure

quality

a dummy variable, equal to one if a firm is criticized for or charged with violations of

information disclosure regulations by the stock exchange where its stocks trade, or

China Securities Regulatory Commission (CSRC) in the [-3,3] event window, and

zero otherwise.

Post-IPO information asymmetry variables

Time variables

Table 1. Variable Definitions

Banking Relationship Characteristics

Relationship intensity

Loan Characteristics

Firm Characteristics

𝑒𝑙𝑎𝑡 𝑜𝑛𝑠 𝑝 𝑛𝑡𝑒𝑛𝑠 𝑡 = 𝑡 𝑡 𝑡

𝑖 𝑡 𝑡

𝑠𝑝𝑟𝑒𝑎𝑑 = 𝑡 𝑖 𝑡 𝑡 𝑡 − 𝑖 𝑡 𝑡 𝑡

𝑖 𝑡 𝑡 𝑡 × .

42

Table 2. Summary statistics

The sample includes 10,534 loans during 1996-2014 for 495 firms that had their IPOs during

1999-2012. This table reports summary statistics for loan, firm, banking relationship characteristics

and time variables. Variables are as defined in Table 1. Loan amount and firm size (total assets) are

in millions of constant RMB yuan of year 1990.

Variable Mean Median Std. Dev. Min Max Obs.

Actual Interest rate 6.036 5.900 1.447 0.006 14.940 9,857

Spread -0.114 0.000 7.011 -66.930 50.000 10,534

PostIPO 0.479 0.000 0.500 0.000 1.000 10,534

SOE 0.350 0.000 0.477 0.000 1.000 10,534

Big4 0.606 1.000 0.488 0.000 1.000 10,534

Loan Characteristics

Maturity 2.047 1.000 2.174 0.017 25.000 10,534

Amount (million yuan) 41.81 135.11 15.31 0.020 4,402.76 10534

Secured 0.744 1.000 0.437 0.000 1.000 10534

Firm Characteristics

Age 6.781 5.920 4.656 0.001 26.500 9,656

ROA 0.078 0.064 0.091 -1.153 1.418 9,656

Tangible 0.427 0.398 0.222 0.002 0.974 9,656

Size (million yuan) 4,247 11,993 1,564 75 136,752 9,656

Leverage 0.447 0.458 0.166 0.004 0.943 9,656

Earnings volatility 0.104 0.089 0.077 0.013 0.811 9,656

Bank Relationship Characteristics

Loan concentration 0.338 0.273 0.275 0.000 1.000 10,534

Relationship intensity 0.404 0.333 0.310 0.000 1.000 10,534

Bank share 0.308 0.233 0.245 0.000 1.000 10,534

Time variables

Liberalization I 0.696 1.000 0.460 0.000 1.000 10,534

Liberalization II 0.019 0.000 0.136 0.000 1.000 10,534

Recession 0.234 0.000 0.424 0.000 1.000 10,534

43

Table 3. Loan and banking relationship characteristics: pre- vs. post-IPO

This table presents the comparison in loan and banking relationship characteristics between pre-IPO and

post-IPO subsamples. Spread is measured as a percentage above or below the benchmark interest rate set by the

People's Bank of China. Maturity is the loan maturity measured in years. Amount is the log of one plus loan

amount in millions of constant RMB yuan of year 1990. Secured is dummy variable equal to one if the loan is

collateralized or guaranteed. Relationship intensity measures the strength of banking relationship as a loan is

made. Loan concentration measures a firm’s bank loan concentration using the Herfindahl-Hirschman Index.

Bank share is a bank’s average share in its borrowers’ total loan amount. The mean values are presented for all

loans, and for loans of SOEs and those of non-SOEs, in the full sample period, and in the pre-IPO period and

post-IPO period, separately. T-tests are conducted to test the differences between the pre-IPO mean and the

post-IPO mean. *,

** and

*** mark statistical significance at the 10, 5, and 1 percent levels, respectively.

Full period Pre-IPO Post-IPO Difference t value

Panel A: Loan Characteristics

Spread

All Loans -0.114 0.989 -1.314 -2.303*** -6.923

SOE -0.566 1.119 -3.154 -4.274*** -7.182

Non-SOE 0.129 0.900 -0.570 -1.470*** -3.637

Maturity ( years)

All Loans 2.046 1.749 2.370 0.621*** 14.801

SOE 2.328 2.132 2.628 0.496*** 6.099

Non-SOE 1.895 1.486 2.265 0.779*** 16.348

Amount

All Loans 1.236 1.142 1.337 0.196*** 19.672

SOE 1.192 1.020 1.454 0.434*** 22.166

Non-SOE 1.259 1.225 1.290 0.065*** 5.911

Secured

All Loans 0.744 0.785 0.698 -0.087*** -10.310

SOE 0.713 0.782 0.607 -0.175*** -11.708

Non-SOE 0.760 0.788 0.735 -0.053*** -5.118

Panel B: Banking Relationship Characteristics

Loan concentration

All Loans 0.338 0.356 0.318 -0.038*** -7.128

SOE 0.316 0.339 0.279 -0.060*** -6.329

Non-SOE 0.350 0.368 0.333 -0.035*** -5.356

Relationship intensity

All Loans 0.404 0.427 0.378 -0.050*** -8.307

SOE 0.441 0.483 0.376 -0.106*** -9.863

Non-SOE 0.384 0.390 0.378 -0.012 -1.611

Bank share

All Loans 0.349 0.362 0.335 -0.027*** -4.615

SOE 0.338 0.353 0.314 -0.039*** -3.880

Non-SOE 0.355 0.368 0.343 -0.025*** -3.440

44

Table 4. State ownership and cost of bank loans around IPOs

This table presents the fixed-effects estimation of the regression of interest rate spread on explanatory

variables. The sample is the 10,534 loans for 495 firms that had their IPOs during 1999-2012. For each

firm, all loans in the three years prior to the IPO and the three years post-IPO are included. The dependent

variable is Spread, the percentage interest rate spread relative to the benchmark interest rates set by the

PBOC. PostIPO is equal to one for a loan taken after the firm’s IPO, and zero otherwise. SOE is equal to

one a firm whose ultimate controlling shareholder is a local or central government. Loan, firm, and bank

relationship characteristics as well as time variables are controlled for. In parentheses t statistics are

reported. *,

**, and

*** mark statistical significance at the 10, 5, and 1 percent levels, respectively.

All SOE Non_SOE All

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

PostIPOijt -1.415***

-3.059***

-0.857 -0.972*

(-2.80) (-3.02) (-1.42) (-1.71)

SOEit -1.426

(-1.33)

SOEit ×PostIPOijt -1.575***

(-2.70)

Maturityijt -1.084***

-1.006***

-1.174***

-1.093***

(-11.47) (-6.62) (-9.36) (-11.55)

Amountijt -0.234 -1.975***

0.711 -0.207

(-0.61) (-3.02) (1.51) (-0.54)

Securedijt 3.106***

4.296***

2.605***

3.083***

(7.42) (5.12) (5.38) (7.36)

Ageit-1 -0.219 2.028***

-1.417***

-0.204

(-0.52) (2.74) (-2.75) (-0.48)

Sizeit-1 3.349**

8.351**

0.254 2.963**

(2.30) (2.10) (0.16) (2.01)

ROAit-1 -4.668 -1.662 -3.580 -4.106

(-1.45) (-0.14) (-1.07) (-1.27)

Tangibleit-1 4.170**

-2.515 6.598**

3.968**

(2.24) (-0.87) (2.38) (2.13)

Leverageit-1 5.454***

11.979***

5.554* 5.635

***

(2.76) (3.52) (1.93) (2.85)

Earnings volatilityi 4.709**

6.099* 3.013 3.893

*

(2.02) (1.66) (1.58) (1.90)

Loan concentrationit 1.503* 0.755 2.611

** 1.377

*

(1.81) (0.51) (2.53) (1.66)

Relationship intensityijt 0.014 -1.433 0.917 0.032

(0.02) (-1.41) (1.34) (0.06)

Recessionijt 4.984***

4.351***

8.199 4.670***

(3.75) (3.07) (1.28) (3.48)

Constant Yes Yes Yes Yes

Firm fixed effects Yes Yes Yes Yes

Loan year fixed effects Yes Yes Yes Yes

00 Post SOEsPostH : = （p -value） 0.000

N 9,656 3,112 6,544 9,656

R2 0.449 0.437 0.467 0.450

45

Table 5. State ownership and cost of bank loans around IPOs: Alternative samples

This table presents the fixed-effects estimation of the regression of interest rate spread on explanatory

variables in two alternative samples. The first alternative sample includes loans during only one year

before the IPO and one year post-IPO and the results are displayed on the left. The second alternative

sample includes loans during three years before and three years after IPO but only from banks that lend to

the firm both pre- and post-IPO. The dependent variable is Spread, the percentage interest rate spread

relative to the benchmark interest rates set by the PBOC. PostIPO is equal to one for a loan taken after the

firm’s IPO, and zero otherwise. SOE is equal to one a firm whose ultimate controlling shareholder is a

local or central government. Loan, firm, and bank relationship characteristics as well as time variables are

controlled for. In parentheses t statistics are reported. *,

**, and

*** mark statistical significance at the 10, 5,

and 1 percent levels, respectively.

1 year pre- and 1 year post- IPO

pre- and post-IPO loans from the

same banks

SOE Non_SOE All SOE Non_SOE All

(1) (2) (3) (4) (5) (6)

PostIPOijt -3.597***

-1.417**

-1.594***

-3.450***

-0.917 -1.072*

(-2.88) (-2.24) (-2.63) (-2.84) (-1.44) (-1.73)

SOEit -8.967 -8.944

(-1.38) (-0.66)

SOEit×PostIPOijt -0.972* -1.602

*

(-1.91) (-1.85)

Maturityijt -1.185***

-0.270* -0.656

*** -1.035

*** -0.434

** -0.700

***

(-5.97) (-1.82) (-5.55) (-5.21) (-2.32) (-5.38)

Amountijt -2.074***

-0.462 -1.130***

-2.795***

-1.190**

-1.697***

(-2.85) (-0.85) (-2.61) (-3.21) (-2.03) (-3.50)

Securedjt 5.702***

2.138***

3.000***

5.420***

1.376**

2.570***

(5.68) (4.19) (6.55) (5.06) (2.36) (4.99)

Ageit-1 2.767**

-1.942***

-0.348 2.147**

-2.167***

-0.619

(2.55) (-2.77) (-0.59) (2.00) (-2.98) (-1.04)

Sizeit-1 1.911* 7.881

*** 1.027

*** 9.704

* 1.527 5.245

***

(1.92) (3.51) (5.68) (1.91) (0.81) (2.99)

ROAit-1 -2.066 -1.409* -1.653

** 7.205 -0.282 0.225

(-1.41) (-1.77) (-2.08) (0.45) (-0.06) (0.05)

Tangibleit-1 -1.502***

4.713 -0.955 -6.267* 5.800

* 2.632

(-3.69) (1.40) (-0.46) (-1.82) (1.93) (1.30)

Leverageit-1 1.129***

4.100 1.924 8.869**

2.638 3.182

(2.64) (1.18) (0.86) (2.06) (0.86) (1.47)

Earnings volabilityi 3.588**

2.861***

3.477***

2.890 1.202* 1.224

**

(2.06) (3.24) (3.75) (1.28) (1.93) (2.39)

Loan concentrationit 1.004 -3.954***

-4.532***

4.127**

2.839**

3.036***

(0.45) (-2.58) (-3.75) (2.26) (2.40) (3.10)

Relaitonship intensity ijt 0.042 -0.440 -0.262 2.728* 0.274 0.832

(0.04) (-0.56) (-0.40) (1.87) (0.34) (1.17)

Recessionijt 3.928**

5.072 3.813***

6.216***

1.656 6.221***

(2.46) (0.88) (2.67) (3.56) (0.24) (4.03)

Constant Yes Yes Yes Yes Yes Yes

Firm fixed effects Yes Yes Yes Yes Yes Yes

Loan year fixed effects Yes Yes Yes Yes Yes Yes

00 Post SOEsPostH : = (p -value） 0.000 0.002

N 1,511 3,116 4,627 1,853 4,022 5,875

R2 0.539 0.596 0.564 0.493 0.438 0.451

46

Table 6. Excluding SOEs with major declines in state ownership

This table presents the fixed-effects estimation of the regression of interest rate spread on explanatory variables

in the sample excluding SOEs with a major decline in state ownership after IPO. The dependent variable is

Spread, measured as a percentage difference in loan interest rate from the benchmark interest rate. PostIPO is

equal to one when firm’s loan is taken after the IPO, and zero otherwise. SOE is equal to one a firm whose

ultimate controlling shareholder is a local or central government. Loan, firm, and bank relationship

characteristics as well as time variables are controlled for. In parentheses t statistics are reported. *,

**, and

***

mark statistical significance at the 10, 5, and 1 percent levels, respectively.

SOEs w/ state

ownership

change > -20%

SOEs w/ state

ownership

change < -20%

All firms except for

SOEs w/ state

ownership

change < 0

SOEs w/ state

ownership change

< -20%

PostIPOijt -4.083**

-3.253**

-0.857 -1.230**

(-2.47) (-2.31) (-1.64) (-2.08)

SOEijt -5.779 15.296

(-0.35) (0.90)

SOEijt×PostIPOijt -5.209***

-2.961**

(-3.13) (-2.37)

Maturityijt -0.472* -1.550

*** -1.121

*** -0.922

***

(-1.80) (-8.35) (-9.34) (-8.30)

Amountijt -1.484 -2.444***

0.852* 0.481

(-1.38) (-3.03) (1.89) (1.12)

Securedijt 8.617***

3.833***

3.273***

3.066***

(5.57) (3.86) (6.93) (6.60)

Ageit-1 0.736 3.606***

-0.790 -0.648

(0.71) (3.26) (-1.57) (-1.41)

Sizeit-1 24.413***

-4.757 0.061 3.048*

(3.92) (-0.80) (0.04) (1.95)

ROAit-1 -8.277 34.538* -3.176 -5.513

*

(-0.48) (1.92) (-0.96) (-1.66)

Tangibleit-1 -42.673***

7.005**

6.446**

1.319

(-5.25) (2.09) (2.39) (0.50)

Leverageit-1 -6.502 6.644 5.651**

3.039

(-0.81) (1.46) (2.04) (1.13)

Earnings volabilityi 23.735 3.886 12.356 14.290*

(1.05) (0.16) (1.50) (1.84)

Loan concentrationit -4.080 2.620 1.599 0.679

(-1.56) (1.41) (1.64) (0.72)

Relaitonship intensity ijt -0.007 -1.529 1.213* 0.683

(-0.00) (-1.32) (1.82) (1.05)

Recessionijt 12.601***

3.038**

9.460 13.268***

(3.06) (2.10) (1.48) (4.07)

Constant Yes Yes Yes Yes

Firm fixed effects Yes Yes Yes Yes

Loan year fixed effects Yes Yes Yes Yes

N 1163 1949 7003 7707

R2

0.480 0.437 0.478 0.459

47

Table 7. State ownership and cost of bank loans around IPOs: Matched Sample

This table presents the fixed-effects estimation of the regression of interest rate spread on explanatory

variables in the sample including IPO firms and their matched private firms. The private firms are

identified using radius matching and only loans made during one year before and one year after the IPO

are included. Results based on one-to-one matching is displayed on the left, and those based on one-to-N

matching is on the right. The dependent variable is Spread, the percentage interest rate spread relative to

the benchmark interest rates set by the PBOC. PostIPO is equal to one for a loan taken after the firm’s IPO,

and zero otherwise. Treatment is equal to one for an IPO firm and zero for a match firm. SOE is equal to

one a firm whose ultimate controlling shareholder is a local or central government. Loan, firm, and bank

relationship characteristics as well as time variables are controlled for. In parentheses t statistics are

reported. *,

**, and

*** mark statistical significance at the 10, 5, and 1 percent levels, respectively.

Matched firms One to one matching One to N matching (1) (2) (3) (4) PostIPOijt 1.447 2.743

** 7.585

* -0.237

(1.01) (1.96) (1.87) (-0.04) PostIPOijt × Treatmentit -2.425

* -2.536

* -8.906

** -0.125

(-1.70) (-1.80) (-2.14) (-0.02) SOEit -4.933

* 6.037

(-1.81) (1.52) SOEit×PostIPOijt -0.264 2.596 (-0.15) (0.44) SOEit×PostIPOijt × Treatmentit -1.746

* -4.923

**

(-1.94) (-2.59) Maturityijt -0.397

*** -0.336

*** -0.979

*** -0.608

***

(-2.87) (-3.60) (-4.00) (-4.04) Amountijt 0.307 -1.206

*** 3.546

*** -0.237

(0.60) (-3.48) (3.14) (-0.34) Securedijt 3.628

*** 1.818

*** 3.427

*** 1.998

**

(6.62) (4.92) (2.66) (2.51) Ageit-1 -2.319

*** -2.065

*** -6.494

*** -3.569

***

(-3.11) (-4.11) (-3.72) (-3.32) Sizeit-1 3.649 1.297 1.820

*** 1.365

***

(1.09) (1.12) (3.25) (3.25) ROAit-1 -0.139 -0.368 -1.906 -6.906 (-0.02) (-0.08) (-1.11) (-1.15) Tangibleit-1 -1.009 0.281 -3.880

* -2.593

(-0.41) (0.17) (-1.84) (-1.30) Leverageit-1 0.752 2.261 -7.069 -8.285 (0.28) (1.23) (-1.63) (-1.08) Earnings volabilityi 1.402 1.014 4.873 4.439 (1.27) (0.90) (1.55) (1.34) Loan concentrationit 8.519

*** 5.249

*** 1.441

*** 6.301

***

(6.07) (5.56) (3.50) (3.44) Relationship intensity ijt -0.242 -0.936 -2.760

* -2.078

(-0.34) (-1.58) (-1.84) (-1.26) Recessionijt 7.923

*** 5.278

*** 19.149

*** 8.130

***

(4.77) (4.61) (4.46) (2.83) Constant Yes Yes Yes Yes Firm fixed effects Yes Yes Yes Yes Loan year fixed effects Yes Yes Yes Yes

N 1,282 1,282 3,045 3,045 R

2 0.565 0.578 0.532 0.538

48

Table 8. Interest rate liberalization and cost of bank loans around IPOs

The dependent variable is Spread, the percentage difference in loan interest rate from the benchmark

interest rate. PostIPO is equal to one for a loan taken after the firm’s IPO, and zero otherwise. SOE is equal

to one a firm whose ultimate controlling shareholder is a local or central government. Liberalization I is

equal to one when firm’s loan is taken from 2004/10/29 to 2013/07/19, and zero otherwise; Liberalization

II is equal to one when firm’s loan is taken after 2013/07/20, and zero otherwise (see Table A3). Loan, firm,

and bank relationship characteristics as well as time variables are controlled for. In parentheses t statistics

are reported. *,

**, and

*** mark statistical significance at the 10, 5, and 1 percent levels, respectively.

All SOE Non_SOE All (1) (2) (3) (4)

PostIPOijt -1.472***

-3.131***

-0.878 -1.037*

(-2.90) (-3.09) (-1.45) (-1.82) SOEijt -18.603 (-1.34) SOEijt×PostIPOijt -1.547

**

(-1.97) Liberalization Iijt 2.281 5.145

* -0.815 2.254

(1.23) (1.85) (-0.32) (1.21) Liberalization IIijt 0.158 7.735

* -3.508 0.166

(0.07) (1.65) (-1.17) (0.07) Maturityijt -1.080

*** -0.981

*** -1.173

*** -1.088

***

(-11.41) (-6.43) (-9.36) (-11.49) Amountijt -0.232 -1.927

*** 0.727 -0.205

(-0.61) (-2.94) (1.55) (-0.54) Securedijt 3.097

*** 4.238

*** 2.599

*** 3.075

***

(7.40) (5.05) (5.37) (7.34) Ageit-1 -0.173 1.803

** -1.263

** -0.160

(-0.40) (2.40) (-2.41) (-0.37) Sizeit-1 3.397

** 8.561

** 0.282 3.017

**

(2.33) (2.15) (0.17) (2.04) ROAit-1 -4.840 -0.966 -3.789 -4.285 (-1.51) (-0.08) (-1.13) (-1.33) Tangibleit-1 4.251

** -2.499 6.552

** 4.051

**

(2.28) (-0.87) (2.36) (2.17) Leverageit-1 5.530

*** 1.160

*** 5.459

* 5.706

***

(2.80) (3.58) (1.90) (2.88) Earnings_volabilityi 1.101

** 2.844 1.084 1.294

*

(2.07) (1.65) (1.58) (1.96) Loan_concentrationit 1.482

* 0.791 2.558

** 1.358

(1.79) (0.54) (2.48) (1.63) Relaitonship intensity ijt -0.006 -1.437 0.900 0.012 (-0.01) (-1.42) (1.31) (0.02) Recessionijt 4.975

*** 4.271

*** 8.281 4.668

***

(3.74) (3.01) (1.29) (3.47) Constant Yes Yes Yes Yes Firm fixed effects Yes Yes Yes Yes Loan year fixed effects Yes Yes Yes Yes

00 Post SOEsPostH : = (p-value) 0.000

N 9,656 3,112 6,544 9,656 R

2 0.450 0.438 0.467 0.450

49

Whole period Pre-IPO Post-IPO t statistic

All loans Big Four 0.293 0.656 -0.165 -0.821 ** -2.159

Non-Big Four -0.719 1.61 -2.774 -4.384 *** -7.089

SOE loans Big Four -0.021 1.45 -2.597 -4.047 *** -5.738

Non-Big Four -1.937 0.061 -4.285 -4.346 *** -3.75

Non-SOE loans Big Four 0.519 -0.068 1.111 1.179 *** 2.774

Non-Big Four -0.333 2.203 -2.377 -4.581 *** -6.281

Difference

Table 9. Loan Interest Rates for the Big Four vs. Other Banks

This table compares the changes in loan interest rate spread around borrower IPOs for loans made by the

Big Four banks and non-Big Four banks, in the full sample, as well as in the subsamples of SOE loans and

non-SOE loans. The Big Four refers to the four largest, state-owned commercial banks in China. Spread is

measured as a percentage spread relative to the benchmark interest rates set by the People's Bank of China.

In each sample, the difference in the mean pre-IPO spread and post-IPO spread is reported and tested

using a t-test. *,

**, and

*** mark statistical significance at the 10, 5 and 1 percent levels, respectively.

50

Table 10. Big Four banks and cost of loans around IPOs

The dependent variable is Spread, measured as a percentage difference in loan interest rate from the

benchmark interest rate. PostIPO is equal to one when firm’s loan is taken after the IPO, and zero

otherwise. Big4 is equal to one for loans made by the four largest commercial banks. Loan, firm, and bank

relationship characteristics as well as time variables are controlled for. In parentheses t statistics are

reported. *,

**, and

*** mark statistical significance at the 10, 5, and 1 percent levels, respectively.

(1) (2) (3)

SOE Non-SOE All firms

PostIPOijt -1.457 -2.805***

-2.685***

(-1.10) (-3.85) (-4.27)

Big4ijt 0.865 -2.012***

-1.253***

(0.96) (-3.71) (-2.68)

Big4ijt×PostIPOijt -3.014**

3.357***

1.802***

(-2.39) (4.78) (2.94)

Maturityijt -1.055***

-1.144***

-1.107***

(-6.88) (-9.11) (-11.64)

Amountijt -1.959***

0.623 -0.295

(-2.96) (1.32) (-0.77)

Securedijt 4.578***

2.630***

3.235***

(5.42) (5.41) (7.70)

Ageit-1 1.872**

-1.399***

-0.353

(2.52) (-2.72) (-0.84)

Sizeit-1 7.774* 0.259 3.716

**

(1.94) (0.16) (2.54)

ROAit-1 -6.772 -3.312 -5.157

(-0.57) (-0.99) (-1.60)

Tangibleit-1 -2.734 7.282***

4.453**

(-0.95) (2.62) (2.39)

Leverageit-1 2.692***

6.199**

5.728***

(3.72) (2.16) (2.90)

Earnings volabilityi 6.373* 11.538 5.733

**

(1.68) (1.40) (2.16)

Loan concentrationit 0.826 2.887***

1.870**

(0.55) (2.80) (2.24)

Relaitonship intensity ijt -1.251 1.073 0.182

(-1.20) (1.53) (0.31)

Recessionijt -8.014 5.492***

5.006***

(-0.54) (3.98) (3.55)

00 Post PostBig4H : = (p-value) 0.000 0.410 0.115

N 3,085 6,525 9,656

R2

0.439 0.469 0.450

51

Table 11. Controlling for Residual Information Asymmetry

The dependent variable is Spread, measured as a percentage difference in loan interest rate from the

benchmark interest rate. PostIPO is equal to one when firm’s loan is taken after the IPO, and zero

otherwise. SOE is equal to one a firm whose ultimate controlling shareholder is a local or central

government.Big4 is equal to one for loans made by the four largest commercial banks.

Underwriter_reputationijt is measured by the market share of the lead underwriters. Analyst_coverageijt is

the log of one plus the firm’s average number of financial analysts following the firm after IPO years.

Information_disclosure_qualityijt is equal to one when firm is criticized or charged with information

disclosure regulations violations by the stock exchange or CSRC after IPO. Loan, firm, and bank

relationship characteristics as well as time variables are controlled for. In parentheses t statistics are

reported. *,

**, and

*** mark statistical significance at the 10, 5, and 1 percent levels, respectively.

(1) (2) (3) PostIPOijt -1.670

*** -1.348

** -3.173

***

(-3.11) (-2.27) (-4.79) SOEijt -15.632 (-1.57) SOEijt×PostIPOijt -1.251

*

(-1.66) Big4ijt -2.129

***

(-4.15) Big4ijt×PostIPOijt 2.506

***

(3.86) Underwriter_reputationijt -0.188 1.145 -0.187 (-0.18) (0.95) (-0.18) Analyst_coverageijt 0.780 -1.935 0.701 (0.13) (-0.34) (0.12) Information_disclosure_qualityijt 20.682

** 12.823 12.684

(2.10) (1.42) (1.01) Maturityijt -0.188 1.145 -0.187 (-0.18) (0.95) (-0.18) Amountijt 0.780 -1.935 0.701 (0.13) (-0.34) (0.12) Securedijt 20.682

** 12.823 12.684

(2.10) (1.42) (1.01) Ageit-1 -0.834

*** -0.842

*** -0.855

***

(-8.18) (-8.24) (-8.34) Sizeit-1 -0.411 -0.386 -0.445 (-1.01) (-0.94) (-1.08) ROAit-1 3.634

*** 3.611

*** 3.746

***

(8.33) (8.27) (8.56) Tangibleit-1 -0.162 -0.150 -0.154 (-0.35) (-0.33) (-0.34) Leverageit-1 4.490

*** 4.200

*** 4.827

***

(2.97) (2.74) (3.18) Earnings_volabilityi -3.572 -3.175 -3.810 (-1.10) (-0.98) (-1.17) Loan_concentrationit 1.484 1.373 1.587 (0.76) (0.71) (0.82) Relaitonship_intensity ijt 2.480 2.655 2.834 (1.20) (1.28) (1.37) Recessionijt 13.981

* 13.331

* 13.397

*

(1.93) (1.83) (1.85) Constant Yes Yes Yes Firm fixed effects Yes Yes Yes Loan year fixed effects Yes Yes Yes

N 8158 8158 8122 R

2 0.388 0.389 0.391

52

Figure 1. Loan interest rates and spreads over time in China’s credit market

7

The sample period is from May 1996 to December 2014. The vertical axis on the left is for the actual

and the benchmark interest rates, and the vertical axis on the right is for the interest rate spread,

measured as a percentage of the benchmark interest rate. Actual Intereast rate is averaged for all the

loans in a month. Benchmark interest rate is the base interest rate for the loans with a maturity of 1

year set by the People’s Bank of China (PBOC). The grey area depicts the floating ranges of loan

interest rates imposed by the PBOC. The process of loan interest rates liberalization in China (see

Table A3) can be divided into three subperiods: the 1st period from May 1996 to October 2004, the

2nd

period from October 2004 to October 2013(LibrilizationI) , and the 3rd

period starting from

October 2013(Liberilization II).

-6.00

-4.50

-3.00

-1.50

0.00

1.50

3.00

4.50

6.00

4.00%

6.00%

8.00%

10.00%

12.00%

14.00%

16.00%

May

-96

Nov

-96

May

-97

Nov

-97

May

-98

Nov

-98

May

-99

Nov

-99

May

-00

Nov-0

0

May

-01

Nov

-01

May

-02

Nov-0

2

May

-03

Nov

-03

May

-04

Nov-0

4

May

-05

Nov

-05

May

-06

Nov-0

6

May

-07

Nov

-07

May

-08

Nov-0

8

May

-09

Nov

-09

May

-10

Nov-1

0

May

-11

Nov

-11

May

-12

Nov

-12

May

-13

Nov-1

3

May

-14

Nov

-14

Actual Interest_Rate

Benchmark Interest_Rate(1Year)

Spread

1st 2

nd 3

rd