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Involuntary excess reserves, thereserve requirements and credit rationing in China Vu Hong Thai Nguyena, Agyenim Boatengb,*and David Newtonc aInternational University, Vietnam National U

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Applied Economics

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Involuntary excess reserves, the reserve requirements and credit rationing in China

Vu Hong Thai Nguyena, Agyenim Boatengb & David Newtonc a

International University, Vietnam National University, Ho Chi Minh City, Vietnam b

Glasgow School of Business & Society, Glasgow Caledonian University, Glasgow, UK c

Nottingham University Business School, University of Nottingham, Nottingham, UK Published online: 07 Jan 2015

To cite this article: Vu Hong Thai Nguyen, Agyenim Boateng & David Newton (2015) Involuntary excess reserves, the reserve

requirements and credit rationing in China, Applied Economics, 47:14, 1424-1437, DOI: 10.1080/00036846.2014.995362

To link to this article: http://dx.doi.org/10.1080/00036846.2014.995362

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Involuntary excess reserves, the

reserve requirements and credit

rationing in China

Vu Hong Thai Nguyena, Agyenim Boatengb,*and David Newtonc

aInternational University, Vietnam National University, Ho Chi Minh City,

Vietnam

bGlasgow School of Business & Society, Glasgow Caledonian University,

Glasgow, UK

c

Nottingham University Business School, University of Nottingham,

Nottingham, UK

Using a sample of 95 banks that covers the period 2000–2011, this article

examines Chinese banks’ credit lending behaviour in response to the

changes in the reserve requirement ratio in the presence of involuntary

excess reserves (IERs) in the banking system The studyfinds that Chinese

banks with positive IERs one period after a reserve requirement shock

experience a significantly increased credit supply in response to an

increase in reserve requirement ratio However, the reserve requirements

have no significant impact on the credit supply in Chinese banks that have

negative IERs one period after a reserve requirement shock This article

sheds lights on the effectiveness of Chinese monetary policy, which uses

reserve requirements as the primary tool to sterilize excess liquidity and

restrain credit expansion

Keywords: involuntary reserve; credit rationing; Chinese banks; reserve

requirement

JEL Classification: E51; E52; E58

I Introduction

Prior literature examining the liquidity effect of

reserve requirements on the credit supply indicates

that an increase in reserve requirement ratio drains

liquidity and reduce the credit supply (Bernanke and

Blinder, 1988; Takeda et al., 2005; Cargill and

Mayer, 2006; Mora, 2009; Gunji and Yuan, 2010)

Romer (1985) points out that increasing the reserve

requirement ratio does not only drain banking liquid-ity but also imposes a tax on deposits by increasing the deposit costs for banks Deposit cost affects credit lending and other investment alternatives that are available to banks such as government securities investments (Thakor,1996) While the funding cost for credit lending includes both a deposit cost and a capital requirement cost (Basel Accord), the cost of funding for government securities investments

*Corresponding author E-mail: agyenim.boateng@gcu.ac.uk

Applied Economics, 2015

Vol 47, No 14, 1424–1437, http://dx.doi.org/10.1080/00036846.2014.995362

consists of only a deposit cost (Thakor, 1996) An

increase in the deposit cost reduces the capital

requirement cost’s proportion in the cost of funding

for credit lending In other words, upon the increase

in the deposit cost, the cost of funding for credit

lending falls relative to the cost of funding for

gov-ernment securities investments As a result, banks are

induced to direct investment funds from government

securities into credit lending, that is increase the

credit supply (Thakor,1996)

The behaviour above contradicts the bank lending

channel’s argument and implies that credit supply

tends to increase in response to the increase in reserve

requirement ratio It suggests that the liquidity effect

and the cost of funding effect of the reserve

require-ments operate in opposing ways, thereby making the

impact of reserve requirements on credit supply

undetermined Despite this, the extant literature

lar-gely neglects the cost of funding effect and focuses

mainly on the liquidity effect of reserve

require-ment shocks (Takeda et al., 2005; Cargill and

Mayer,2006; Mora,2009) While a recent study by

Nguyen and Boateng (2013) examined the impact of

involuntary excess reserve (IER) on monetary policy

transmission in China, it is important to point out that

they did not analyse the impact of reserve

require-ments on the credit supply Yet several studies such

as Anderson (2009), Conway et al (2010), Ma

et al (2011) indicate that the large excess reserves1

in the Chinese banking system is one of the reasons

behind the employment of reserve requirements by

the People’s Bank of China (PBC) as a monetary

policy tool to manage excess liquidity in China

The main objective of this article is to examine the

Chinese banks’ credit lending behaviour in response

to the changes in the reserve requirement ratio, where

IERs which are defined as the excess reserves beyond

precautionary levels are present in the banking

sys-tem (i.e the excess liquidity situation) Examining

this behaviour is significant because credit supply is

the primary funding source in China, which drives

Chinese economic growth (Hansakul et al., 2009;

Liu and Zhang,2010) This article therefore

contri-butes to the bank lending literature in two important

ways First, this study identifies the liquidity effect

and the cost of funding effect of reserve requirements

on the credit supply, which is important because

these two effects provide conflicting predictions of credit supply’s response The failure to capture the cost of funding effect may result in an unexpected credit supply expansion after the PBC increases the reserve requirement ratio Second, this study sheds lights on the impact of reserve requirements on credit lending behaviour of Chinese banks in the context where IERs are present This is important because the presence of IERs may attenuate the liquidity effect of reserve requirements

This studyfinds that Chinese banks with positive IERs one period after a reserve requirement shock significantly increase the credit supply in response to

an increase in reserve requirement ratio However, the reserve requirements have no significant impact

on the credit supply in Chinese banks that have negative IERs one period after a reserve requirement shock

The remainder of the study is organized as follows:

Section II presents the theoretical background,

Section IIIdiscusses the methodology and data ana-lysis; Section IV interprets the estimation results Additional analyses and robustness tests are provided

inSection V Finally,Section VIconcludes the study

II Theoretical Background The credit rationing theory contends that banks decline to screen a credit application if the net loan benefit (the difference between loan return and credit lending cost) fails to cover the credit screening cost (Thakor,1996) A higher credit lending cost reduces the net loan benefit, which leads to an increase in the probability of credit rationing Thakor (1996) identi-fies the cost of funding and the opportunity cost relative to alternative investments (e.g government securities’ return) as two components of credit lend-ing cost (the cost to supply credit) Although the cost

of funding for credit lending includes a deposit cost and a capital requirement cost (the cost to hold required capital to back up bad loans), the cost of fund for investing in government securities consists

of the only deposit cost (Thakor,1996) An increase

in the reserve requirements raises the deposit cost because a higher reserve requirement ratio reduces 1

The aggregate excess reserves beyond statutory requirements in Chinese banking system stood at an average of 10% of deposit base in the 1990s and the early 2000s (Anderson, 2009 ), although the ratio gradually fell to 2.3% in 2011, but compared to banks in the US and Euro-zone countries, it is considered high.

the fraction of deposits that banks can use tofinance

loans (Romer,1985; Vargas et al.,2011) In line with

the argument of Thakor (1996), it is assumed that

reserve requirement changes do not greatly affect the

loan return rate, the capital requirement cost and

government securities’ return (opportunity cost) in

the short run These assumptions are argued to hold

in the Chinese banking market because the reserve

requirements in China are primarily used as a tool to

moderate excess reserves, do not reflect the monetary

policy stance of the PBC (Anderson, 2009) and

appear to have an insignificant effect on the interbank

market rate (Chen et al., 2011) An increase in

deposit cost and unchanged capital requirement cost

reduces the capital requirement cost’s proportion in

the cost of funding for credit lending In other words,

the cost of funding for credit lending falls relative to

the cost of fund for government securities

invest-ments However, the opportunity cost and loan return

do not change greatly Therefore, banks are induced

to direct investment funds from government

securi-ties to credit lending

The primary limitation of the credit rationing

the-ory is that it does not consider the liquidity cost Bank

credit tends be a long-term commitment and costly

to liquidate at short notice (Brunnermeier and

Pedersen,2009) In contrast, government securities

can easily be converted into cash, which make them

ideal for liquidity contingency Therefore, banks face

a higher illiquidity risk (which is equivalent to a

higher liquidity cost) when they direct resources to

credit lending instead of government securities For

this reason, it is argued that the cost of credit lending

includes not only the cost of funding and the

oppor-tunity cost as proposed by Thakor (1996), but also

the liquidity cost compared with the cost of investing

in government securities Because a rise in the

statu-tory reserve requirements drains liquidity from the

banking system and curtails banks’ ability to raise

deposits (Bernanke and Blinder, 1988), the

likeli-hood of a liquidity shortage increases under this

circumstance, and the liquidity cost also increases

Indeed, the probability of credit rationing increases

because of a higher liquidity cost, and banks tend to

reduce the credit supply in response to the increase in

reserve requirement ratio

An increase in the reserve requirement ratio leads

to two conflicting effects: although the cost of

funding for credit lending falls relative to the cost

of funding for government securities investment caused by an increase in the deposit cost, the liquidity cost increases Because a decrease in the cost of funding for credit lending augments the credit supply and an increasing liquidity cost discourages the credit supply, the effect of an increase in reserve require-ments on the credit supply is undetermined However, in the presence of IERs, the cost of funding effect may dominate the liquidity effect Ceteris par-ibus, an increase in reserve requirement ratio reduces the IERs (Agénor et al.,2004) The presence of the IERs one period after a reserve requirement shock indicates that the increase in reserve requirements fail

to eliminate unwanted liquidity in the banks Therefore, the increase in reserve requirements may have an insignificant impact on the liquidity of the banks If the amount of IERs is positive one period after an increase in the reserve requirement ratio, the fall in the cost of funding dominates the increasing liquidity cost, which results in a greater credit supply However, if the amount of IERs is negative one period after a reserve requirement shock, both the liquidity effect and the cost of funding effect are at work in opposing ways, and the impact of reserve requirement shocks on the credit supply remains undetermined

Under the credit rationing theory, banks ration credit applications because the information asymme-try between banks and potential borrowers may lead

to moral hazards and excessive credit risks to the banks (Thakor, 1996) In the context of China, the problem of information asymmetry between banks andfirms is severe because of the poor credit history

of the private sector (Firth et al.,2009) In addition, the majority of privatefirms in China are small and medium enterprises (SMEs) (Allen et al.,2009), and the asymmetric information that exists with respect to SMEs arises from the lack of transparency, less infor-mation disclosure, an informal accounting system and weak internal control and governance systems (Berger and Udell,2006)

In Thakor’s (1996) model, government securities

do not involve capital back-up (Basel I), although this pattern does not hold for Basel II2and Basel III frameworks, which require banks to take interest-rate risks from the securities that they hold into account as a part of the capital requirement

2

The Basel Committee on Banking Supervision, Principles for the Management of Interest Rate Risk, September 1997.

Furfine (2001) argues that loans are considered

more risky than securities and that the loans

there-fore require a higher percentage of equity to reflect

their larger risk weight In other words, the cost of

funding for credit lending always bears an

addi-tional capital requirement cost compared to the

cost of funding for securities investment In

addi-tion, the Chinese bond-market capitalization is very

small relative to the credit volume, and the majority

of the bond market consists of central-bank bills

(Hansakul et al., 2009) whose size is too small to

sterilize the excess liquidity in the Chinese banking

market (Conway et al., 2010) Moreover, Chinese

commercial banks are not allowed to engage in trust

investment or stock broking (PRC, 1995, Article

43), which limits the securities investment

opportu-nities of Chinese banks Therefore, in the Chinese

banking market, the credit rationing theory is

ana-logous to credit lending versus hoarding IERs,

rather than versus investing in securities As IERs

are not subject to capital requirement regulations,

the capital requirement cost is only present in credit

lending Regarding the opportunity cost, the PBC

maintains the interest on excess reserves at afixed

rate below deposit benchmark rate; indeed, there

were only two adjustments in the period 2000–

2011 (Anderson,2009; Laurens and Maino,2009;

Ma et al., 2011) For this reason, reserve

require-ment shocks do not affect the opportunity cost In

the presence of IERs in the Chinese banking market,

it is argued that an increase in reserve requirement

ratio does not affect the loan return, opportunity cost

or the liquidity cost, but it reduces the relative cost

of funding for credit lending This is because the

rising deposit cost renders the (additional) capital

requirement cost less significant Consequently,

Chinese banks tend to expand the credit supply in

response to the increase in reserve requirements In

light of the above discussion, for banks that have

positive IERs one period after a reserve requirement

shock, it is expected that the credit supply has a

positive relationship with the change in the reserve

requirement ratio However, for banks with negative

IERs one period after a reserve requirement shock,

the liquidity effect and the cost of fund effect

operate in opposing ways; hence, the impact of

reserve requirement shocks on the credit supply is

undetermined

III Methods and Data Analysis Data and the measure of IER

Banking data covering the period from 2000 to 2011 are collected from Bankscope-Fitch’s International Bank Database Only commercial banks whose data are available for at least three consecutive years are considered Other types of banks (i.e policy banks, cooperative banks and investment banks) are not included because they may have different objectives rather than profitability The final sample consists of 95 banks and 552 annual observations Monetary policy data are collected from the PBC website Furthermore, other macro data (e.g national and provincial growth rates of the real GDP) are collected from the China Securities Market and Accounting Research database and the China Statistical Yearbook (the National Bureau of Statistics of China)

Following the studies of Agénor et al (2004); Nguyen and Boateng (2013), we decompose IERs from precautionary excess reserves IERs ratio is the difference between the ratio of actual excess reserves to deposit and the ratio of the estimated precautionary excess reserves to deposit Excess reserves are defined as the current account hold-ings of banks, with the central bank that are beyond the required amount of reserves (Bindseil

et al., 2006) Aikaeli (2011) modifies the precau-tionary-excess-reserves model by arguing that banks tend to demand more excess reserves to buffer the credit risk Following Agénor

et al (2004), Aikaeli (2011), and Nguyen and Boateng (2013), we model the demand for precau-tionary excess reserves, and the estimation resi-dual is recorded in the form of IER

ERit ¼ τ þ α1ERi ;t1þ α2ðLÞLR þ α3ðLÞCASH

þ α4ðLÞYR þ α5ðLÞARRR þ α6ðLÞR

þ α7YEARtþ εit

(1)

where τ is a constant term, εit is a well-behaved error term andαjð Þ are lag polynomials, which areL defined as follows:

αj¼ 1 þ αj1Lþ þ αjpLp; j  2 (2)

ER is the ratio of excess reserves to deposits ER

is measured as the ratio of the difference between a

bank’s current account balance with the central

bank and the required reserve3over the total

custo-mer deposit Following Aikaeli (2011), the

loan-return volatility (LR) is used to capture the credit

risk that may trigger deposit withdrawals; LR is

measured as the absolute value of the deviation of

loan interest income from its trend, which is

identi-fied by the filter method that was developed by

Hodrick and Prescott (1997) Loan interest income

is the ratio of interest income on loan to total

cus-tomer deposit In addition, the Hodrick–Prescott

filter (HP) is a standard method for removing

trend movements in the business cycle literature

(Ravn and Uhlig, 2002) CASH reflects the

cash-holding preferences of depositors, which are

mea-sured based on the volatility of the ratio of vault

cash to total customer deposit by HPfilter YR is the

ratio of real GDP growth rate to its trend (HPfilter),

which captures the demand for cash Moreover,

ARRR and R are the average reserve requirement

ratio set by the PBC within a certain year and the

refinance interest rate, respectively; the latter term

is the rate that the PBC charges when lending to

financial institutions for short-term liquidity

sup-port (20-day call loan rate) and reflects the penalty

cost if a bank falls short of the required amount of

reserves The summary on the statistics and the

results on the unit-root tests for the variables of

precautionary excess reserve estimation are

pro-vided in Appendices 1 and 2 The model is

esti-mated by a System Generalized Method of

Moments (SGMM), which was developed by

Arellano and Bond (1991), Arellano and

Bover (1995) and Blundell and Bond (1998) The

number of lags is based on the Aikaike Information

Criteria The error termεitwhich is free of unit-root

and serial correlations is collected to index the IER

ratio The estimation results inTable 1show that the

demand for precautionary excess reserves has a

significantly positive relationship with the credit

risk, which confirms the evidence from the study

of Aikaeli (2011)

SGMM and variable definitions Following Gambacorta (2005), Gunji and Yuan (2010), and Nguyen and Boateng (2013), the follow-ing dynamic model is used to examine the impact of reserve requirement shocks on the credit supply in the presence of IERs in China Since the sample covers a relatively short period, only thefirst lag of dependent variable is considered, and this is in line with prior studies (see Altunbaş et al.,2002; Tabak

et al.,2010)

LOANit ¼ αiþ β1LOANi ;t1þ β2LIQi;t1

þ β3SIZEi ;t1þ β4CAPi ;t1

þ β5IERi ;t1þ β6NIMi ;t1þ β7IPt 1

þ β8Yt 1þ β9RRRt 1þ β10DIERit

þ β11DIERit RRRt 1þ εit

(3)

where αi is a constant term and εit is a well-behaved error term

Table 1 SGMM estimation for precautionary excess reserves

Dependent Variable: ER

Number of observations 457

Number of instruments 68

Second order Arellano–Bond test p-value 0.101 Notes: ** denotes statistical significance at 1% level Robust SE are reported in parentheses.

3

The required reserve is measured as the product of the total customer deposit and the reserve requirement ratio for domestic currency deposits Because the reserve requirement ratio for foreign currency deposits is smaller than what is required for Renminbi (RMB) deposits, the total estimated required reserves is slightly higher than the actual value However, a comparison with this actual value (where available) shows that the real and estimated required reserves are very close because foreign currency deposits account for a very small fraction of the total customer deposit.

The IER is obtained as the residuals from the

estimation of precautionary excess reserves

Following Gambacorta (2005), we include

bank-spe-cific characteristics, namely, liquidity (LIQ), bank

size (SIZE), capitalization (CAP) and net interest

margin (NIM) to control for the bank lending

chan-nel Following Gambacorta (2005), the size (SIZE) is

normalized not just with respect to the mean over the

whole sample period but also with respect to each

single period to remove unwanted trends because

size is measured in nominal terms As IER is

obtained as regression residuals whose sample

mean equals zero, IER will not be normalized

Following Gambacorta (2005), other bank-specific

variables (LIQ, CAP and NIM) are normalized

using the mean of the sample as follows:

SIZEit ¼ log Ait

PN

i ¼1log Ait

Nt

(4)

LIQit ¼Lit

Ait X

T

t ¼1

PN

i ¼1Lit=Ait

Nt

!

CAPit ¼Cit

Ait  X

T

t ¼1

PN

i ¼1Cit=Ait

Nt

!

NIMit¼ NIMRit X

T

t ¼1

PN

i ¼1NIMRit

Nt

!

where N and T are the numbers of observations

and years, respectively Moreover, L denotes liquid

assets as defined by BankScope, which includes

cash, government bonds, short-term claims on other

banks (including certificates of deposit) and, where

appropriate, the trading portfolio C and A refer to

equity (capital) and total assets, respectively

Because an increase in reserve requirements is

con-sidered to be a tax on the banks, if the banks fail to

completely pass this tax onto their borrowers (in the

form of higher lending rates) or depositors (in the

form of lower deposit rates), the banks’ net interest

margin will shrink, thereby reducing the credit

sup-ply (Romer, 1985) The model includes net interest

margin (NIM) to take the tax effect into account In

line with Bankscope’s definition, the net interest margin ratio (NIMR) is measured as the ratio of net interest revenue to total earning assets

Credit supply (LOAN) is defined as the change in the natural logarithm of gross loan (Δln(grossloan)), where grossloan is the total amount of credits that a bank issues during a particular year Interest rate policy (IP) is included to control for the impact of monetary policy stance on credit supply (see Borio and Zhu, 2012) Liu et al (2009) and He and Wang (2012) argue that the open market operation rate (the rate at which the central bank sells or buys government bonds on the open market) in China does not signal the monetary policy stance of the PBC The monetary policy interest rate in China (IP) is proxied by the change in the one-year deposit bench-mark (ceiling) rate (DB) because the policy deposit ceiling rates are strictly binding and signal a market-clearing equilibrium in China, but the lending bench-mark rate is not (Anderson, 2009; Porter and

Xu,2009) The real GDP growth rate (Y ) is used to capture the credit demand Regarding reserve requirement shock index (RRR), the average of all

of the reserve requirement ratios (ARRR) within a certain year is taken; then the reserve requirement ratio shock (RRR) is defined as the change in the average reserve requirement ratio (ARRR) from the previous year Previous studies in the area of mone-tary policy transmission (e.g Altunbaş et al.,2002; Gambacorta,2005) point out that the credit supply’s response to the change in the monetary policy rates rather than the monetary policy rate levels can cap-ture the monetary policy effectiveness For this rea-son, the change in reserve requirement ratio (RRR) instead of the reserve requirement level (ARRR) is used to reflect the policy shocks to the credit supply market DIER is a dummy variable with the value of

1 if IER is positive (IER > 0), and with the value of 0

if IER is negative (IER≤ 0) The coefficient of the interaction (β11) reflects the difference on credit lend-ing in response to reserve requirement shocks of the two groups, that is banks with positive IER versus banks with negative IER one period after the shock

A summary of the variable statistics is presented in

Table 2 The IER ranges from−22% to 33% of the total deposit and is positive in 43% of the observa-tions During the sample period, the average reserve requirement ratio (ARRR) has a mean of 12.1% and reaches a peak of 20.36% for the six largest banks and 18.36% for the other smaller banks (the PBC has

maintained a two-tier reserve requirement system

since 2008) From 2000 to 2002, the PBC kept the

reserve requirement ratio constant In contrast, from

2002 to 2011, the reserve requirement ratio was

increased every year except 2009

Table 3presents the panel unit-root tests results for

all variables Augmented Dickey–Fuller and

Phillips–Perron unit root tests (Fisher-type tests

Choi,2001) for panel data indicate that all variables

are stationary

Because OLS is biased in dynamic models,

‘System’ GMM estimator is used Arellano and

Bover (1995) and Blundell and Bond (1998)

devel-oped SGMM based on Arellano and Bond (1991)

‘difference’ GMM (DGMM) SGMM is able to deal

with the endogeneity and fixed effects in dynamic

models (Arellano and Bover, 1995); furthermore, it can overcome the weakness of ‘difference’ GMM, which is inconsistent in the estimations on unba-lanced panel data (Roodman, 2006) The lags of regressors are used as instruments IP, RRR and the interaction are treated as endogenous variables Y is treated as an exogenous variable Other variables are considered to be predetermined SGMM is imple-mented by comment xtabond2 in STATA The opti-mal model is selected based on the criteria suggested

by Arellano and Bond (1991) and Roodman (Roodman,2006,2009) inAppendix 3

IV Estimations Results and Discussion The results from the estimations are reported in

Table 4 (estimation 1), and the residuals are free of unit-root and serial correlation Regarding the control variables, IER significantly increases credit supply Bank size (SIZE) and capital (CAP) have positive impacts, while liquidity (LIQ) has a negative impact

on credit supply at significant level of 10% Net interest margin (NIM), monetary policy interest rate (IP) and GDP (Y) do not statistically affect credit supply

The IER dummy variable (DIER) is not statisti-cally significant, indicating that there is no difference

in credit supply between banks with positive and negative IERs, ceteris paribus The impact of reserve requirement shock on credit supply is measured as follows:

Table 2 Summary statistics for reserve requirement impact estimations variables

Note: *denotes the rejection of normal distribution at the 1% significance level.

Table 3 Unit root tests for reserve requirement impact

estimations variables

Variable Augmented Dickey–Fuller Phillips–Perron

Note: *denotes the rejection of the unit root hypothesis at

the 1% significance level.

For banks with negative IER one period after

reserve requirement shocks (the value of DIER

equals 0), the impact of reserve requirement shocks

on credit supply is reflected on β9, which is negative

and not statistically significant This supports the

argument that the liquidity effect and the cost of

fund effect operate in opposing ways in response to

reserve requirement shocks for banks with negative

IERs, and hence, the impact of reserve requirement

shocks on the credit supply is undetermined

For banks with positive IERs one period after

reserve requirement shocks (the value of DIER

equals 1), the impact of reserve requirement shocks

on credit supply is

@LOAN

@RRR ¼ β9þ β11¼ 0:92 þ 4:09 ¼ 3:17

The result shows that the coefficient of the

inter-action β11 is positive, statistically significant and

much greater than β9 The sum of β9 and β11 is

positive, indicating that banks with positive IERs

one period after reserve requirement shocks tend to

increase credit supply in response to increases in

reserve requirement ratio The model is further

estimated separately for two groups, that is banks with negative IERs (IERit ≤ 0) and positive IERs (IERit > 0) one period after reserve requirement shocks (RRRt−1) without the IER dummy and the interaction The results inTable 4(estimations 2 and 3) show that the impact of reserve requirement shock (β9) on credit supply is positive and statisti-cally significant for banks with positive IERs but not significant for banks with negative IERs This evidence supports the following argument: if an increase in the reserve requirement ratio fails to eliminate IERs completely (i.e if positive IERs remain one period after the hike in reserve require-ment ratio), banks tend to expand their credit supply

in response to this increase in reserve requirement ratio This finding contradicts the evidence from prior studies, which report the negative relationship between the reserve requirement ratio and the credit supply (e.g see Takeda et al., 2005; Cargill and Mayer, 2006; Mora, 2009) One possible reason for the difference is that the prior studies do not consider IERs and the cost of funding effect These studies therefore overestimate the liquidity effect and deduce that there is a negative relation-ship between the reserve requirement ratio and the credit supply However, this finding supports the study of Qin et al (2005) whofind that an increase

in reserve requirement ratio generates a small rise in GDP growth rate in China

Table 4 SGMM estimations for reserve requirement impact on credit supply (LOAN) Dependent variable: LOAN (1) IER > 0 (2) IER < 0 (3)

LOAN (lag1) 0.17** (0.08) 0.62*** (0.16) 0.08 (0.25)

Second order Arellano–Bond test p-value 0.851 0.278 0.616 Notes: ***, ** and * denote statistical significance at the 1%, 5% and 10% significance levels, respectively Robust SE are reported in parentheses.

V Additional Analysis and Robustness

Tests The robustness tests are reported inTable 5, and the

tests are summarized inTable 6 The PBC employs a

loan ceiling as a monetary policy tool to moderate the

credit supply; its primary target is the four state-owned commercial banks (SOCB) (Geiger, 2008) These loan limits may make the four state-owned commercial banks less responsive to reserve require-ment shocks To address the effect of loan limits, we exclude the four state-owned banks from the sample

Table 5 Estimation results for additional analysis and Robustness tests

Dependent variable: LOAN (4) (without SOCB) (5) (6) (7) (8) (9) (10)

(0.8) (0.41) (0.38) (0.25) (0.61) (0.37) (0.38)

(0.25) (0.08) (0.09) (0.09) (0.2) (0.09) (0.09)

(1.05) (1.22) (1.53) (1.01) (1.34) (1.08) (1.75)

(0.05) (0.04) (0.05) (0.04) (0.05) (0.05) (0.67) RRR × DIER 3.14** 3.97** 4.62** 3.15** 4.42** 2.69* 4.84*

(1.41) (1.91) (2.32) (1.48) (2.27) (1.55) (2.91)

(0.34) (0.26) (0.25) (0.26) (0.44) (0.24) (0.26)

(0.29) (0.25) (0.28) (0.19) (0.22) (0.3) (0.26)

(0.02) (0.02) (0.03) (0.03) (0.02) (0.03) (0.02)

(0.99) (0.26) (0.36) (0.51) (0.53) (0.35) (0.39)

(0.06) (0.04) (0.05) (0.04) (0.07) (0.04) (0.04)

(12.4) (6.34) (3.71) (10.7) (6.12) (6.33)

(8.03) (3.91) (3.36) (6.32) (3.45) (3.49)

(5.86)

(5.56)

(2.03)

(0.09)

(0.16)

(0.03)

(0.06)

Hansen p-value 0.661 0.824 0.621 0.862 0.469 0.882 0.496 Second order Arellano–Bond test

p-value

0.708 0.988 0.698 0.486 0.794 0.896 0.765

Notes: ***, ** and * denote statistical significance at the 1%, 5% and 10% significance levels, respectively.

Robust SE are reported in parentheses.