Discussion Papers In Economics
And Business

Credit Crunch in East Asia: A Retrospective
Masahiro ENYA, Akira KOHSAKA and Mervin POBRE

Discussion Paper 04-04

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANCredit Crunch in East Asia: A Retrospective
Masahiro ENYA, Akira KOHSAKA and Mervin POBRE

Discussion Paper 04-04

March 2004

この研究は財団法人学術振興野村基金研究プロジェクト「金融危機と東アジアの金融政策波及メカニズム」の成果の一部である。

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANCredit Crunch in East Asia: A Retrospective

Masahiro ENYA∗, Akira KOHSAKA∗∗ and Mervin POBRE∗∗∗

February 2004

Abstract
In this paper, we explore the issue on credit crunch from a comparative
perspective. Utilizing longer time series data, we investigate the existence of credit
crunch in selected crisis-hit economies in East Asia over the period 1980-2002. We
detected some episodes of credit crunch both before and after the Asian economic crisis.
These episodes after the Crisis are somewhat different from those detected by previous
studies on the issue. We, then, review the credit-crunch episodes in the broad
macroeconomic context in order to assess our results in the longer-run perspective.
We are well aware that financial liberalization has changed the financial environmentsof these countries more or less in due course. Even so, the mixed results we obtainedon the existence of credit crunch do not suggest that the impact of the austerity
programs on financial intermediation after the Asian Crisis was ambiguous. On thecontrary, they implied that the impact of the programs were so severe that credit crunchor supply retrenchment was overwhelmed by a sharp fall in credit demand because ofreal and expected persistent overall economic depression.
JEL Classification: E5, O11, O53
Keywords: credit crunch, East Asia, Asian Economic Crisis, disequilibrium analysis∗

Lecturer, Faculty of Politics, Economics and Law, Osaka International University.
E-mail: enya@pel.oiu.ac.jp∗∗
Corresponding author. Professor, Osaka School of International Public Policy, Osaka University, 1-31
Machikaneyama-cho, Toyonaka, Osaka 560-0043, Japan.
E-mail: kohsaka@osipp.osaka-u.ac.jp∗∗∗
Research Fellow, Japan Society for the Promotion of Science, Osaka School of International Public
Policy, Osaka University. E-mail: mlpobre@osipp.osaka-u.ac.jp

I. Introduction
Financial intermediation plays a significant role in economic development. In
fact, the East Asian Miracle has been supported by financial intermediation rather thanby capital markets. The degree of financial intermediation, e.g. measured by broadmoney as a ratio to GDP, has been distinguishable from those in the other developing
economies and regions. In the rapidly growing East Asian economies, the corporate
sector depends heavily on external sources of funds, especially from financial
intermediaries given their limited internal funds to finance their vigorous investment
needs. We should note that this high debt-dependency of the corporate sector is theresult of rapid economic growth and stable macroeconomic environment and not viceversa.
In the aftermath of the 1997 Crisis, however, the economic recovery in East
Asia has not been supported by recoveries of domestic investment as well as bankcredits. Credit slowdown was apparent across all the Asian economies. Triggered by
the IMF-prescribed monetary tightening policy, it is believed that reduced credit supply
further contributed to the already weakening economic activities in these countries.
This mechanism is consistent with the pattern of causality between credit and real
economic activity as proposed by the credit view (Bernanke and Gertler, (1995)).
The basic tenet of this view is that banks’ supply of credit plays a significant
role in the transmission of monetary policy to the real sector of the economy. This isbased on the assumption that assets in the form of loans are equally relevant as bankliabilities in the transmission of monetary shocks to the real sector of the economy.
With a tighter monetary policy, banks respond by curtailing their supply of loans. This
arises from information asymmetries in the financial market as well as the imperfect
substitutability between financial assets. Thus, according to this mechanism, theleftward shift of the supply of loans explains the observed credit slowdown in the crisis
period. Put differently, the credit slowdown can be the manifestation of a credit
crunch which may come from monetary tightening.
Whether the high-interest rate policy led to a credit crunch in the 1997 crisis
period has attracted interests among policy makers and researchers. A number of
empirical studies have examined whether and to what extent East Asian countries havebeen suffering from a credit crunch in the 1997 crisis period. The basic approach is tomodel a demand and supply for loans and identify whether the observed data on loans isdemand or supply determined. A brief review of these studies is presented in thefollowing section.
In this paper, we analyze the issue on credit crunch from a comparative-1-

perspective. To do this, we cover a longer time period to investigate different periodsof credit crunch in East Asia.
In this study, we cover the period from 1980 to 2002.
We detected some episodes of credit crunch both before and after the Asian economiccrisis. These episodes after the Crisis are somewhat different from those detected byprevious studies on the issue. We, then, review the credit-crunch episodes in broad
macroeconomic context in order to assess our results in the longer-run view.
This approach has the following merits. Studies on the credit crunch in East
Asia focused on the most recent experience when financial liberalization was at its peak.
We are well aware that financial liberalization has changed the financial environmentsof these countries in the last two decades. Covering longer periods, we will be able tocompare the similarities and/or differences in the nature and causes of the most recent
credit crunch with previous experiences. Then, we may be able to understand thedifferential impacts of credit crunch in different financial environments.
The rest of the paper is organized as follows. The next section briefly reviews
the literature on credit crunch in the East Asian countries after the Asian economic crisis.
The third section explains the analytical framework that we adopt in the course of theanalysis. The fourth section presents the estimation results of the study. The fifth
section discusses the findings comparing recent credit crunch with previous experiences.
The last section concludes the paper.
II. Review of the Literature
Several studies investigated whether credit crunch occurred in the East Asian
countries in the 1997 crisis period as a result of the high interest rate policy. Ding,
Domac and Ferri (1998) examined whether and to what extent East Asian countries
have been suffering from a credit crunch. They examined the evolution of several
macroeconomic variables including monetary and credit aggregates as well as the
spread between bank lending rates and rates on risk-free assets in identifying whether
demand or supply is the binding constraint in the observed credit slowdown. This
framework has been applied to Indonesia, Korea, Malaysia, the Philippines andThailand.
The main finding is that these East Asian countries suffered from an overall
credit crunch although the situation differs considerably across the countries. In Korea
and Malaysia, the credit squeeze has been rendered more through a wider wedge
between lending rates and risk-free asset yields. In all countries, they also foundevidence of a flight to quality, a situation where banks shift towards less risky assets(government securities).

-2-

Meanwhile, Ghosh and Ghosh (1999) used a disequilibrium framework toinvestigate a possible credit crunch in Indonesia, Korea and Thailand during 1997-1998.
They used macroeconomic variables in their analysis with monthly observations from1992:1 to 1998:6. Their main finding is that there is little evidence of quantity
rationing at the aggregate level in all three countries. Credit slowdown was mainly
due to lower demand for credit and that the rising real interest rates and weakening
economic activity were the main factors that lowered credit demand.
In Thailand, although both credit supply and credit demand fell in real terms,
estimated credit demand has generally fallen faster. It is only in January 1998 did theyfind evidence of a credit crunch. In Korea, excess credit demand in the first half of1997 became very small at the onset of the crisis then finally contracted sharply in late1997 and early 1998. In Indonesia, there is evidence of a credit crunch only in
November-December 1997. Thereafter, there is no evidence of credit rationing.
In the study of single country cases, Kim (1999) also estimated adisequilibrium model of the bank loan market to identify the characteristics of the credit
crunch and its intensity during the crisis in Korea. The results of his study reveal thatthe loan market is characterized by a state of predominantly excess demand for loans.
Additionally, he found that there has occurred a credit crunch right after the financial
crisis in December 1997. This finding is based on monthly data from January 1993
through May 1998. His findings support the idea that the marked decline in theaggregate bank credit is driven by a sharp decline in loan supply attributable topervasive and stringent regulation on bank capital.
Baek (2002) expanded Kim’s (1999) data set to cover from January 1992 to
May 2001. Using the same disequilibrium framework, he found the evidence of three
periods of credit crunch prior to the crisis and a two periods of credit crunch after thecrisis period. The three credit crunch periods prior to the crisis are 1992:2-1995:4,
1996:1-1996:2 and 1997:1-1997:2 while the two credit crunch periods after the crisis
are 1997:12-1998:3 and 2001:2-2001:4. Higher credit risk and remaining uncertainty
in the loan market were cited as the main causes of the recent credit crunch in his study.
In the case of Malaysia, Beng and Ying (2001) estimated the extent of excess
demand for loans during the recent currency crisis using the Johansen Cointegration
technique. Their empirical results identify the period from July 1997 to March 1998 as
the credit crunch period with the tight monetary policy and erosion of banking
institutions’ capital base as the main factors responsible for the retrenchment of loan
supply. Finally, Ito and Da Silva (1999) looked for empirical evidence of credit crunchin Thailand using a specially designed survey for commercial banks and their results-3-confirm the existence of a credit crunch. Their study analyzed the developments in
Thailand’s credit market during the 1997-1998 currency and financial crisis.
III. Framework
In this paper, we define credit crunch as a situation where banks curtail their
supply of loans at prevailing interest rates, resulting in a decline in the level of actual
lending and an excess demand for loans. Bernanke and Lown (1991) define a bankcredit crunch as a significant leftward shift in the supply curve for bank loans, holding
constant both the safe real interest rate and the quality of potential borrowers.
Similarly, the Council of Economic Advisors (1991) defines credit crunch as a situationin which the supply of credit is restricted below the range usually identified withprevailing market interest rates and the profitability of investment projects (quoted from
Ding, Domac and Ferri (1998)). Two points can be inferred from the above definitions.
First, credit crunch is primarily a supply phenomenon. Second, it may be understoodas a disequilibrium situation. An abrupt change in the lending behavior of banks alters
the relationship between credit availability and interest rates.
A change in the lending behavior of banks may be generally due to factors
affecting their ability to make loans and their willingness to supply loans. In theformer, regulations from the government play a significant role. Such regulations may
come in the form of higher cost of borrowing, higher reserve requirement ratio and/or
capital adequacy requirements. In the latter, expectations of overall economy andperceived credit risk of borrowers may significantly affect the reluctance of banks to
supply loans.
Banks may then adjust their lending rates more rapidly and asignificant widening of the wedge between the lending rate and a risk-free asset such as
the government securities occurs. Furthermore, their preference for more liquid assets
may cause them to shift towards these less risky assets resulting in a further decline in
the availability of loans. In any of these cases, the relationship between the lending
rate and the supply of loan is altered.
Based on the preceding perspectives, we employ a disequilibrium framework toanalyze the behavior of the demand and supply of loans. We characterize the credit
market by the following equations

LDt =α 1+α 2it + α 3 X tD + ε tD(1)

LS t = β 1 + β 2 it + β 3 X tS + ε tS

(2)

Qt = min (LDt , LS t )

(3)

-4-

where LDt is the real loan demand in period t, LSt is the real loan supply in period t, it is
the real lending rate in period t, XDt and XSt are sets of explanatory variables affecting theloan demand and supply respectively, αi and βi are the parameters to be estimated and
εDt and εSt are the error terms of the loan demand and supply functions, respectively.
Using this framework, we proceed to develop the model by incorporating the different
factors that affect the demand and supply of loans. In this study, we specify the loan
demand and supply functions as follows,

LDt = α 1 + α 2 it + α 3 IPt −1 + α 4 IPGAPt −1 + α 5π t + α 6 Lt −1 + α 7 Dt + ε tD(6)

LS t = β 1 + β 2 it + β 3 Rt + β 4 IPt −1 + β 5 LC t + β 6π t + β 7 GSECt −1 + β 8 Lt −1 + β 9 Dt + ε tS

(7)
where IP is the industrial production index, IPGAP is the deviation of current IP from its
long-run trend, π is inflation rate defined as the percentage change in the consumer
price index (CPI) over the previous year, R is the spread between the lending rate and arisk free asset (government securities), LC is the real lending capacity defined as total
assets minus statutory reserves, GSEC is the real holdings of risk free assets and D is adummy variable that controls for possible structural changes brought about by financial
liberalization1. Finally in the following we also include an interaction term between
the dummy variable and the real interest rate in the estimated model, and some
additional period dummies to cope with discontinuous changes in actual loans, whichwere the result from the fact that non-negligible amounts of Non Performed Loans weretransferred to asset management corporations in the crisis period after 1997. All realvariables are deflated by the CPI. Further details on the variables used in this study areprovided in the data appendix.
A number of points with regard to the above specification of the demand andsupply functions merit some explanations. First, in the demand function, we include
the IPGAP variable to capture the borrowers’ need for additional working capital intimes of adverse shocks in their production. That is, when production temporarily falls
relative to its long-run trend, the demand for credit may increase (Ghosh and Ghosh(1998)). We measure the long-run trend of production using the Hodrick-Prescottfilter method. IPGAP is therefore computed as current IP less its long-run trend.
Second, we specify the production index and the IPGAP variables with one period lag to

1

The dummy variable takes on the value of unity from June 1996 in Korea, from February 1991 in Malaysia andfrom June 1992 in Thailand. These periods account for changes in the interest rate policy in each country.

-5-account for the simultaneity problem in the model.2 Third, we include a risk variable(R) in the supply function to account for the bank’s perceived credit risk of borrowers.
Even with high lending rates, lenders may still be reluctant to lend because of high risk.
Fourth, since both the borrowers and lenders make decisions simultaneouslywith other financial and portfolio decisions, a list of other stock variable that plays as analternative to bank loans should also be included (Ito and Ueda (1981)). In the supply
function, we include government securities held by banks (GSEC) to account for theirpreference for risk-free assets as a major alternative asset in their portfolio decisions.
This variable is also specified with one period lag to account for simultaneity problems.
However, in the demand function, data availability do not permit us to do the same.
Finally, we include the observed level of loans for the preceding period (Lt-1) as apredetermined variable. This is consistent with the short-run optimal level in thepresence of adjustment. The variables are specified in log-levels except for the interest
rates and the IPGAP.
We estimate the parameters of the model using the likelihood function and the
maximum likelihood methods proposed by Maddala and Nelson (1974).3 We then use
the parameters derived from this estimation technique to estimate the loan supply anddemand and compare it with the observed level of credit. By doing so, we will be able
to determine whether the loan demand or supply is the binding constraint in theobserved credit slowdown and identify whether credit crunch has occurred. We apply
the above analysis to Korea, Malaysia and Thailand using monthly data over the sample
period 1980 to 2002 (1983-2002 for Thailand).
IV. Estimation Results
Table 1 reports the parameter estimates of the loan demand and supply
functions for each of the three countries. In the credit supply function, the coefficientsof almost all the variables have their expected signs. The real interest rate (i) has the
expected positive sign, with an elasticity of 0.007 for Thailand, 0.005 for Malaysia but
only 0.003 for Korea. In all countries, the coefficient of the real interest rate issignificant4. The lending capacity (LC) is also robust across the three countries. It is2

We experimented including longer lags of the IP and IPGAP variables in the model. However, results from thisexperiment do not significantly improve the findings when only one period lag is included.
3 While we recognize that the presence of unit root in the variables that we consider in this study may pose someproblems in the estimates, unavailability of an appropriate technique at this point in time to handle nonstationary
variables in disequilibrium analysis limit us to follow the conventional practice in the literature of specifying thevariables in levels.
4 The coefficient is statistically significant at the 1 % and 5 % level of significance in Malaysia and Korea,
respectively. In the case of Thailand, the coefficient is barely significant at the 10% level.

-6-

positive and it has an elasticity of as large as 0.79 in Thailand, while 0.07 in Malaysia
and 0.07 in Korea. The interest rate spread (R) which stands for the banks’ credit risk
perception of borrowers has its expected negative sign. However, it is statisticallysignificant in Korea and Malaysia, while not necessarily so in Thailand. The
government security variable (GSEC) is statistically significant in all three cases.
>>Table 1 around here
For the other variables, we obtain some mixed results. The industrial
production index (IP) has the expected positive sign in Malaysia and Thailand andstatistically significant, while in Korea it has the wrong sign (but statisticallyinsignificant). The inflation rate is positive and statistically significant in all cases.
Finally, the dummy variables for financial liberalization have significant effects in theloan supply functions in Malaysia and Thailand. For these two countries, theinteraction term between the dummy variable and the real interest rate does yieldsignificant effects. This suggests that with smaller semi-elasticities of interest rates theloan supply has become less sensitive to interest rate changes. Finally we detected a
few significant discontinuous changes in loan supply in Thailand over the post-crisis
period, which appears to reflect the above-mentioned asset transfers.5
In the loan demand function, we obtained very robust results on the realinterest rate and the inflation rate across the three countries. The estimated coefficientsof real interest rate (i) are negative and statistically significant. They are 0.007 in
Korea, and 0.008 in Malaysia and 0.005 in Thailand. The inflation rate (π), which
stands for macroeconomic stability, also has a statistically significant negative
coefficient. The elasticity is 0.006 for Korea, 0.007 for Malaysia and 0.004 for
Thailand.
The industrial production (IP), though statistically significant across the three
economies, has a positive coefficient in Korea and Thailand but a negative one in
Malaysia. This may reflect two opposing effects. A higher production level may
reflect good economic conditions and therefore greater need for credit to meet
expansion in business enterprises. On the other hand, when production is higher,
companies may have better cash flow and therefore may curtail their demand for credit.
The output gap (IPGAP) has a significantly negative sign in Korea and Thailand andpositive but statistically insignificant in Malaysia. The dummy variable for financial
Our estimated coefficients of lagged dependent variables almost as large as one suggest very slow
adjustment in both loan demand and supply. Accordingly, while our independent variables are
statistically significant, a large part of variations in loan demand and supply are explained by theirown lagged variables.

5-7-

liberalization is significant in all three cases. Only in Thailand the interaction term
between the dummy variable and the real interest rate is significantly positive, whichsuggests that the loan demand has also become less interest-rate sensitive.
Overall, in comparison with the previous studies using the disequilibrium
framework, our estimates yield more significant and theoretically correct signs in thevariables of the loan demand and supply functions. In Ghosh and Ghosh (1999), theirestimates for the interest rate in the loan supply function yield a negative and significant
coefficient in Thailand and positive but insignificant coefficient in Korea. In the loan
demand function, their estimates for the interest rate in Thailand yield a negative but
insignificant coefficient. Similarly, Kim (1999) obtained a negative and significant
sign for the interest rate in his loan supply function for the case of Korea. Beng andYing (2001) also obtained a negative and insignificant coefficient for the interest rate intheir loan supply function for the case of Malaysia.
Figure 1 plots the estimated loan supply and loan demand as well as the actual
loan to the private sector. Generally, our estimates yield a good correspondence
between the actual loan to the private sector and the minimum of the contemporaneous
loan supply and demand. According to our estimates, Korea was mostly in excess
demand for loans in the 1980s, and then mostly in excess supply since the mid-1990s
onward. Malaysia was generally in excess demand for loans except for the period
1991-95. Thailand was mostly in excess supply of loans in the years of 1994-97 andthen persistently in excess demand after the Crisis.
>>Figure 1

about here

Now we are going to identify periods of credit crunch. As said in the
beginning of Section III, to identify credit crunch, the following three conditions must
be met simultaneously:i) a decline in an estimated supply of loans,ii) a decline in the level of actual lending andiii) an existence of an estimated excess demand for loans.
Therefore, by definition, there would be no credit crunch in Korea after the Crisis and in
Thailand in the 1980s, where we detected excess supply of loans. With this criterion,
we can identify several months of credit crunch in each country as shown in Table 2.
>>Table 2 around here
In Korea, we identify two credit-crunch periods in the 1980s and four in theearly 1990s, but none after 1997. In Malaysia, two credit-crunch periods are identified-8-

in the latter half of the 1980s and several periods after the Crisis, while in Thailand a
few credit crunch periods in the 1980s and the early 1990s, and several after the Crisis.
Finally, Table 3 summarizes the results on the existence of credit crunch in East Asia
after the Asian Crisis including ours.
>>Table 3 around here

V. Discussion
In this section we will discuss the episodes of our identified credit crunch inbroader macroeconomic context. Figure 2 provides macroeconomic and monetary
developments in Korea, Malaysia and Thailand to facilitate mapping our estimatedresults onto the actual macroeconomic situations. Note, that, according to somecriterions in previous studies, Malaysia and Thailand experienced banking crises in1986 and 1983, respectively, but Korea barely escaped from it in the 1980s.6 Naturally,
these banking crises have much to do with credit crunch, because banks would become
more cautious in providing loans in the crises, part of which can be seen in our crunch
episodes both before and after the Asian Crisis.
>>Figure 2

around here

As shown in Figure 2.a, in Korea, the first episode of credit crunch in the early
1980s corresponds to the harsh adjustment after the negative growth in 1979 and its
aftermath, when the economy transitioned from rapid, inflationary growth to mild,
stable one. Nominal credit growth showed a persistent decline along with loweringinterest rates in this period. The second episode of credit crunch in the early 1990s
also witnessed a persistent decline in nominal credit growth with decreasing interest
rates, reflecting the macroeconomic downturn, while the third episode occurred during
the relative stalemate of credit and real economic growth in 1993. Immediately after
the Asian Crisis, we can see a sharper decline in credit growth, particularly in 1998,
than these credit-crunch episodes. Note, however, that this decline in credit growth
was accompanied by a severe fall of real economic growth, which has never been thecase in the previous episodes.
The first episode of credit crunch in Malaysia was in the late 1980s, which

6
A “banking crisis” is defined as a period in which significant segments of the banking system become illiquid or
insolvent (Kaminsky and Reinhalt (1999), Caprio and Klingebiel (1997) and (2003)). Large scale bank failures,
enactment of emergency measures by the government (deposit freezes, nationalizations, deposit guarantees, bankrecapitalization plans), reports of significant depositor runs, the level of nonperforming loans and the costs of thebailout are among the evidences for a banking crisis.

-9-roughly corresponds to the period of their banking crisis. We can see persistentslowdowns of credit growth and a sharp fall of interest rates with economic slowdown(Figure 2.b). We also see an acute fall of credit growth in the early 1990s, but without
decreases in interest rates and economic activity, when we did not detect credit crunch.
Then comes the credit crunch after the Asian Crisis with a persistent and sharp fall andthen a stalemate of credit growth, along with lowering interest rates and negative
economic growth.
If we presume that credit crunch is likely to occur together with a persistentand/or sharp decline of credit growth and falling interest rates, Thailand appears to have
had potential credit crunch i) after their banking crisis in 1983, ii) in the early 1990s andiii) even before the Asian Crisis (Figure 2.c). Actually, we detected credit crunch inthose periods except for the pre-crisis period, when we identified excess supply of loans.
In fact, while we see some falls of interest rates but little slowdown of economic
activity in the three cases before the Crisis, in contrast, the fall of interest rates and real
economic activity was unprecedented after 1997.
VI. Conclusion
Even within the framework of disequilibrium analysis of credit markets, we
appeared to have witnessed mixed results in the literature as to the existence of credit
crunch across economies in East Asia in the recent decades (See Table 3).7 Our result
suggests that during the crisis period of 1997 through 1999, credit crunch appeared toexist in Malaysia and Thailand, but that is not the case in Korea. How can wereconcile with these apparent contradicting results? Recognizing the important role offinancial intermediation as emphasized at the beginning of this paper, we suggest thefollowing interpretations of our fact finding.
First of all, it is obvious that Thailand was the most hard hit on the financial
intermediation and then came Malaysia by the Asian Crisis among the three economies
under our study. The financial intermediary shrank on both the asset side and liability
side in the two economies, which is contrasting to the Korean case after 1997 as well as
to their own experiences of credit crunch or near-crunch before the Crisis. The impactof the Crisis on the financial intermediation in Korea appears to be at least relatively
mild to the other two and to its own experiences in the recent past. Therefore, we

7 One might point out the limits of the analytical framework of disequilibrium analysis of credit markets here. It is
well known that these economies in East Asia have had a long history of financial repression, or at least financial
restraint, where the price mechanism did not play a due role in fund allocation because of government regulations andother institutional rigidities until the early 1990s. Partly, for example, coefficients of market interest rates are notnecessarily stable in prior studies referred in this paper.

- 10 -

conclude that it is no surprise for us to detect credit crunch in Malaysia and Thailand,
but none in Korea after the Crisis.
Second, or more importantly, our results point to the overwhelming role ofaggregate demand depression in generating significant declines of credits in East Asia.
In fact, we should note that so-called V-shaped recoveries of East Asian economies
since the Crisis were not achieved by the resurgence of endogenous private demands (i.e.
consumption and investment), but barely supported by that of exogenous demands (i.e.
world exports and fiscal stimulus). Even as late as in 2002, we cannot witness thesignificant resurgence of domestic credits to the private sector in Malaysia andThailand.
Thus, the mixed results on the existence of credit crunch do not necessarily
suggest that the impact of the austerity programs on financial intermediation was
ambiguous. On the contrary, they implied, the impact of the programs might be sosevere that even enormous supply retrenchment could be overwhelmed by an evermore
sharp fall in credit demand because of real and expected persistent overall economic
depression as in the case of post-crisis Korea. Because credit crunch is, by definition,a mere difference between retrenchments of both loan supply and demand in thefinancial crisis.
Lastly, as a caveat, we must be aware that there is a basic difficulty in
identifying credit crunch within an aggregate macroeconomic framework, because there
we cannot distinguish between good and bad borrowers and/or loans. We cannot tellwhether observed retrenchments in credit supplies were due to credit rationing against
good borrowers or bad ones. One way to amend this difficulty is to take amicroeconomic approach such as a questionnaire survey of individual borrowers/
creditors as in Ito and da Silva (1999). Even with microeconomic surveys, however,
without reliable market risk premiums, we would not be able to have objective
judgments on the existence of credit crunch.

- 11 -

References
Baek, Ehung (2002), “A Disequilibrium Model of the Korean Credit Crunch,”ICSEAD Working Paper No. 2002-22.
Bernanke, Ben and Cara Lown (1991), “The Credit Crunch,” Brookings Papers on
Economic Activity, Vol. 2, pp. 205-247.
Bernanke, Ben and Mark Gertler (1995), “Inside the Black Box: The Credit
Channel of Monetary Policy Transmission,” Journal of Economic Perspectives,
Vol. 9, No. 3, pp. 27-28.
Beng, Gan Wee and Soon Lee Ying (2001), “Credit Crunch during a Currency
Crisis”, ASEAN Economic Bulletin, Vol. 18, No. 2, pp. 176-192.
Caprio, Gerard Jr., and Daniela Klingebiel (1997), “Bank Insolvency: Bad Luck, Bad
Policy, or Bad Banking?” Annual World Bank Conference on Development
Economics 1996.
------------ (2003), “Episodes of Systemic and Borderline Financial Crises,” World Bank
FinanceResearch(http://econ.worldbank.org/programs/finance/datasets/data?id=23456).

Reports

Ding, Wei, Ilker Domac and Giovanni Ferri (1998), “Is There a Credit Crunch in
East Asia,” World Bank Working Paper, No. 1959.
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after the Crisis in East Asia,” Paper Presented at the 8th International Conventionof the East Asian Economic Association, Kuala Lumpur, Malaysia, November 4-5,
2002.
Ghosh, Swati and Atish Ghosh (1999), “East Asia in the Aftermath: Was There a
Crunch,” IMF Working Paper, No. WP/99/38.
Ito, Takatoshi and Kazuo Ueda (1981), “Tests of the Equilibrium Hypothesis in
Disequilibrium Econometrics: An International Comparison of Credit Rationing,”- 12 -

International Economic Review, Vol. 22, No. 3, pp. 691-708.
Ito, Takatoshi and Luiz Pereira da Silva (1999), “The Credit Crunch in Thailand
During the 1997-1998 Crisis: Theoretical and Operational Issues with the JEXIM
Survey,” EXIM Review Vol. 19, No. 2, pp. 1-40.
Kaminsky, Graciela L. and Carmen M. Reinhart (1999), "The Twin Crises: The Causesof Banking and Balance-of-Payments Problems," American Economic Review, June,
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- 13 -

Table 1: Parameter Estimates of the Loan Demand and Supply Functions

KOREA
Supply Function
Variable

Expected

ConstantiR
IP [-1]LC
πGSEC[-1]

+
−
+
+
−
−

LOAN[-1] 0<<1D
D*i
DC9803*IP[-1]DC

Coefficient-0.494 **0.003 **
-0.002 ***-0.0020.074 ***
0.002 *
-0.018 *0.979 ***
-0.0100.000

PValue-2.267 0.023

T-Stat

2.352

0.019

-2.9040.004-0.106

0.916

2.967

0.003

1.804

0.071

-1.8990.058

31.179 0.000-0.6490.516-0.0220.983

0.005 **

2.553

0.011

-0.015-1.4220.155

MALAYSIA
Supply Function
Coefficient

T-Stat0.433 ***
0.005 ***
-0.004 ***0.034 ***
0.066 ***
0.002 ***
-0.013 ***0.889 ***
0.049 ***
-0.004 **5.500

PValue
0.000

4.670

0.000

-5.017

0.000

3.294

0.001

3.295

0.001

0.004 *
-0.023 **DC9803*R
DC9803

2.643

0.008

-2.6340.008

43.314 0.000
3.714

0.000

-2.470

0.014

1.882

0.060

-2.2770.023

DC9802DC0009DC0106DC0202

Variable

Expected

Constant

Demand Function

Demand Function

PCoefficient T-StatValue
1.636 *** 3.831 0.000

PCoefficient T-Stat
Value-0.511 *** -2.816 0.005-0.007 **−IP [-1] + / −0.214 ***IPGAP[-1] −-0.002 ***π-0.006 ***−i

LOAN[-1] 0<<1D
D*i
DC9803Sample Periods-2.148

0.032

3.293

0.001

-2.7880.005-2.7940.005

0.790 *** 13.575 0.000
0.046 *** 2.561 0.010
1.051 0.293
0.003-1.673 0.094-0.014 *1980:08-2002:09-0.008 **-0.068 **0.000-0.007 **1.080 ***
-0.064 **0.001-2.5460.011-2.008

0.045

0.302

0.763

-2.0760.038

37.459 0.000-2.457

0.014

0.488

0.626

1980:01-2002:09

THAILAND
Supply Function
PValue-1.139 *** -10.151 0.000

Coefficient

T-Stat0.007 ***
-0.0030.116 ***
0.797 ***
0.005 ***
-0.011 ***0.259 ***
0.056 **
-0.006 **

3.738

0.000

-1.433

0.152

3.669

0.000

18.838 0.000
3.468

0.001

-4.215

0.000

7.094

0.000

2.138

0.033

-2.0690.039-0.148 *** -8.522-0.089 *** -12.238-0.056 *** -9.414
0.068 *** 10.334

0.000
0.000
0.000
0.000

Demand Function
PValue-0.256 *** -4.609 0.000

Coefficient

T-Stat-0.005 ***0.152 ***
-0.004 ***-0.004 ***0.961 ***
-0.075 ***0.004 **

-7.139

0.000

8.736

0.000

-8.6860.000-4.3010.000

96.1110.000-5.194

0.000

2.316

0.021

1983:11-2002:12

Note: D, DC, DC---- are dummy variables. D controls for changes in the interest rate policy in each country. D
takes on the value of unity from June 1996 for Korea, from February 1991 for Malaysia, and from June 1992 for
Thailand. DC controls for the effect of severe crisis. DC takes one from March 1998 to October 1998. DC---controls for the structual changes in nominal outstandings of bank loan after financial crisis. For example, DC9803
takes one from March 1998. *, **, *** indicate statistical significance at 10%, 5%, 1% level, respectively.

Table 2: Credit Crunch Periods

1980s

1990s

Year
1981
1986
1987
1988
1989
1990
1991
1992
1993
1994
1998
1999
2000
2001
2002

KOREA
6-7

THAILANDMALAYSIA
Nov.
5-6

Mar.

1-3
Aug.
Oct.
Apr.
May

Mar.
Mar.
Mar.
Jan.
3-6 Dec.
1-4 11-12
Jan.
Feb.

2-4
Jun.
Feb.
Feb.
May

Jul. 9-11
Oct. Dec.
Jun. 8-9 Dec.
5-6 9-10 Dec.

Table 3: Summary of Results on Credit Crunch in East Asia in the 1997 crisis period
Ding, Domac and Ferri (1998)
Beng and Ying (2001)
Ito and Da Silva (1999)
Ghosh and Ghosh (1999)
Kim (1999)
Baek (2002)
Enya, Kohsaka and Pobre (2003)

Korea
Yes
----none
Yes
Yes
none

Malaysia
Yes
Yes--------Yes

Thailand
none
--Yes
none
----Yes

Data Frequency
monthlymonthlymonthlymonthlymonthlymonthly

Coverage Period
1997-1998
1993-1999
1997-1998
1992-1998
1993-1998
1992-2001
1980-2002

Methodology
Observation on Macro variables
Cointegration
SurveyDisequilibrium framework
Disequilibrium framework
Disequilibrium framework
Disequilibrium framework

2002.Nv

2001.Nv

2000.Nv

1999.Nv

1998.Nv

2002.Ja

2001.Ja

2000.Ja

1999.Ja

1998.Ja

1997.Ja

Loan Supply

1996.Ja

1995.Ja

1994.Ja

1993.Ja

1992.Ja

2002.Au

2001.Au

2000.Au

1999.Au

1998.Au

1997.Au

1996.Au

1995.Au

1994.Au

1993.Au

1992.Au

1991.Au

Loan Supply

1997.Nv

Loan Supply

1996.Nv

1995.Nv

1994.Nv

1993.Nv

1990.Au

1989.Au

1988.Au

1987.Au

1986.Au

1985.Au

1984.Au

Loan Demand

1991.Ja

1990.Ja

1989.Ja

1988.Ja

1987.Ja

1986.Ja

Loan Demand

1992.Nv

Loan Demand

1991.Nv

1990.Nv

1989.Nv

1988.Nv

300,000

1985.Ja

1983.Au

1982.Au

1981.Au

1980.Auin billions of Won
4000001984.Ja

1983.Ja

1982.Ja

1981.Ja

1980.Jain billions of Ringgit
4500001987.Nv

6,000

1986.Nv

1985.Nv

1984.Nv

1983.Nvin billions of Baht
Figure 1.a. Actual Loan and Estimated Loan Demand and Supply:
Korea

500000

Actual Loan

35000030000025000020000015000010000050000

0Figure 1.b. Actual Loan and Estimated Loan Demand and Supply:
Malaysia

350,000

Actual Loan

250,000

200,000

150,000

100,000

50,0000

Figure 1.c. Actual Loan and Estimated Loan Demand and Supply:
Thailand

7,000

Actual Loan

5,000

4,000

3,000

2,000

1,000

0

Figure 2.a. Monetary development: Korea
40
30
20
10

2002Q1

2001Q1

2000Q1

1999Q1

1998Q1

1997Q1

1996Q1

1995Q1

1994Q1

1993Q1

1992Q1

1991Q1

1990Q1

1989Q1

1988Q1

1987Q1

1986Q1

1985Q1

1984Q1

1983Q1

1982Q1

1981Q1-10

1980Q1

0-20

GDP growth
CPI inflation

Money Market Ratecredit growth (private)

Government Bond Yield
M2 growthFigure 2.b. Monetary development: Malaysia
40
30
20
10

2002Q1

2001Q1

2000Q1

1999Q1

1998Q1

1997Q1

1996Q1

1995Q1

1994Q1

1993Q1

1992Q1

1991Q1

1990Q1

1989Q1

1988Q1

1987Q1

1986Q1

1985Q1

1984Q1

1983Q1

1982Q1

1981Q1-10

1980Q1

0-20

GDP growth
Treasury Bill Rate
M2 growth

IP growth
CPI inflation

Money Market Ratecredit growth (private)

Figure 2.c. Monetary development: Thailand
40
30
20
10

-20

GDP growth
CPI inflation

Money Market Ratecredit growth (private)

Government Bond Yield
M2 growth

2002Q1

2001Q1

2000Q1

1999Q1

1998Q1

1997Q1

1996Q1

1995Q1

1994Q1

1993Q1

1992Q1

1991Q1

1990Q1

1989Q1

1988Q1

1987Q1

1986Q1

1985Q1

1984Q1

1983Q1

1982Q1

1981Q1-10

1980Q10Data Appendix
Data are monthly and cover the period 1980:1to 2002:8 for Korea, 1980:1 to 2002:9 for
Malaysia and 1983:10 to 2002:12 for Thailand.
iReal lending rate.
Lending rate deflated by CPI inflation(computed as year on year
percentage change in CPI)

Nominal lending rate
CPI

Industrial Production, logarithm.

Industrial Production
66, IFSFor Thailand, IP indices before For Thailand, Bank of Thailand
Jan 1987 are calculated using Monthly
Bulletin,variousproduction unit data. ∗1issues

IP

IPGAP

π
L

R

LC

GSEC

Current Industrial Production less its Industrial Production
long-run trend.
(estimated by the Hodrick-Prescott filter
method)Consumer price inflation rate.
CPI(computed as year on yearchange in CPI
Actual real loans.
Loans on Private Sector(Loans on Private Sector by DMB deflated CPI
by CPI)
Risk.
Nominal lending rate
Lending rate minus interest rate on risk- Treasury Bill Rate
free asset (government security)
Real lending Capacity, logarithm.
Total Assets(Total Assets minus statutory reserves, (Commercial Banks)
deflated by CPI)
Statutory Reserves(Commercial Banks)

Real holdings of Government securities, Treasury bills holdings
logarithm.
(Commercial Banks)(deflated by CPI)
Government Securities holdings(DMB) for Korea

60p, IFS
64, IFS

66, IFS

64, IFS

22d, IFS
64, IFS
60p, IFS
60c, IFSBank Negara Malaysia, Monthly
Statistics Bulletin, various
issuesThe Bank of Korea, Monthly
Bulletin, various issues
Bank of Thailand, Monthly
Bulletin, various issues
Bank Negara Malaysia, Monthly
Statistics Bulletin, various
issues.
The Bank of Korea, Monthly
Bulletin, various issues.
Bank of Thailand, Monthly
Bulletin, various issues.

*1: Weights computed as a proportion of the commodity to total unit of production were used
to estimate IP indices before January 1987.