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
52
Embed
State ownership and banks information monopoly rents ...fmaconferences.org/SanDiego/Papers/Draft 20180115.pdf · monopoly rent. This paper, focusing on China’s banking market where
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
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
2
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
3
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
4
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.
5
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.
6
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.
7
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.
8
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,
9
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.
10
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.
11
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).
12
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.
13
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
14
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.
15
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 ]
16
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’
17
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,