cicilia harun, banking restructuring process in indonesia 1997 - 2003

Banking Restructuring Process in Indonesia 1997 - 2003: To save or to freeze?‡

Cicilia Harun†

This version: April 5, 2005 Preliminary and Incomplete, comments are welcome

Abstract

This paper observes the pattern of banking restructuring process in Indonesia at the aftermath of financial crisis in 1997-1998. The absence of suspension of convertibility and deposit insurance scheme provided caused banking panic to occur. The banking restructuring process limits the competitive nature of deciding to stay or exit the market. With objective to improve the banking system performance, government and central bank had to decide how much effort to put in a particular bank, which lead to the decision to save or to freeze the bank. The size of the bank was a major factor in this decision. Once the authority decided to save a bank, the worse the bank’s earning and management, the more effort put to restructure the bank.

‡ Although this paper is supported by data from Bank Indonesia, the central bank of Indonesia, the evaluation, opinion and analysis expressed in this paper are of the author only and do not reflect the policy and stance of Bank Indonesia. Author’s e-mail address: [email protected]. † Boston University and Bank Indonesia.

Cicilia Harun - Banking Restructuring in Indonesia 1997 – 2003 / 2

1. Introduction The financial crisis in Indonesia in late 1997 provides an interesting condition for

economic research. The crisis was accompanied by the collapse of the national banking system. Kaminsky and Reinhart (1999) described this type of crisis as “the twin crises”, where banking crisis precedes currency crisis and currency crisis deepens banking crisis creating vicious spiral. The currency crisis in East Asia was a result of widespread contagion effect triggered by an attack to Thai currency Baht in June 1997. The impact of the crisis in Indonesia was more devastating than the rest of crisis countries in the region since it also severely hurt the heart of the financial system.

Throughout the banking restructuring process, a number of banks went under restructuring program. The government was able to take control of almost the entire national banking system as a result of the liquidity support extended through the central bank during the banking panic period. By June 1998, the liquidity support reached approximately 50% of GDP. At the time, injecting the liquidity to the banking system was considered a good policy compared to letting the entire financial market collapse. Under the restructuring process, banks were frozen (closed down), taken over, recapitalized or merged to other banks by the government. In the midst of the decisions to save or to freeze the banks, there were considerations whether the banks were “big enough”1 so that closing them down could be very costly to the economy. Therefore, it was expected that certain wisdom applied when deciding which banks to freeze and to save. State-owned banks and regional banks (owned by provincial government) were expected to be saved. Some of them were recapitalized and merged. The dilemma appeared on deciding whether to save or to freeze a private bank that has had a significant share in the banking system in terms of asset value. This type of bank along with state-owned banks counted for more than 60% share of the entire banking system.

Being an institution that is heavily regulated, a bank is required to submit financial reports to the central bank (as banking supervisory authority) and periodically visited by bank examiners. The bank supervisors and examiners assess the bank health by evaluating the CAMEL (Capital, Asset Quality, Management, Earning, and Liquidity) of the bank. We would assume that the decision to freeze or to recapitalize a bank will heavily depend on this evaluation. However, the cost of saving (by recapitalizing and/or merging) or liquidating (by fulfilling the claim of customer's deposit and liquidating the bank's assets) is also considered. During the restructuring process, these 2 factors CAMEL or “performance indicator” and cost consideration or “melting factor” provides dilemma in the decision whether to save or to freeze a bank.

This research is aimed to look at the pattern of the banking restructuring process in Indonesia. Although theoretical model on banking panic and banking crisis have been

1 At the time, the term used by the Indonesian government and central bank for big banks is “systemically important banks”. Since most literatures used only the term “big banks”, this term will be used in this paper.


around for a long time, including the influential work of Diamond and Dybvig (1983), theoretical framework and empirical research on banking restructuring process was hard to find. What makes this paper unique is the fact that not many countries experienced relatively massive banking restructuring process the way Indonesia did. This paper provides documentation on the dilemma a government has to face during banking restructuring process. Using panel data of Indonesian banks from January 1997 to December 2003 provided by Bank Indonesia, this paper tries to answer at least the question: given the characteristics of a bank, how much effort did the authority put for the bank during restructuring process? How big the effort will lead to the decision to save or to freeze the bank. It is interesting to realize although the framework looks like a firm exit model, this paper is different from other research in that type of model2. While other models focus on firm’s decision whether to stay in or exit the market, the model of bank exit in this model is illustrating the central planner problem in restructuring the entire banking system. This means the firms, or in this case the banks, have very little influence on the decision to stay in or exit the market. The authority in this model focuses on the objective of improving the banking system through restructuring process.

The empirical evidence shows that the size of the bank overwhelmed the concern over the performance indicators. The larger bank, the more effort put by the government to restructure or keep the bank in the system. In fact, the worse the management and earning indicators, the more restructuring effort put to the bank, once the decision to keep the bank in the system was made.

The rest of the paper will be arranged as the followings. Section 2 will provide theoretical background on bank panic and banking restructuring. This section will be dedicated to provide background on understanding what possibly happens during banking panic and bank restructuring. Stylized facts about Indonesian banking system especially during the crisis will be presented in section 3. Section 4 describes the data and methodology used in this empirical research, while the findings and discussions will be presented in section 5. And finally section 6 concludes.

2. Banking Panic and Bank Restructuring Calomiris & Gorton (1991) defined banking panic as the event when bank debt

holders (depositors) at all or many banks in the banking system suddenly demand that banks convert their debt claims into cash (at par) to such extent that the banks suspend convertibility of their debt into cash or, in certain case, act collectively to avoid suspension of convertibility. In the case of the US, banks can collectively issue clearing-house loan certificates. Banking panic requires most banks - not only one - to experience massive withdrawal of fund, which distinguish it with the event of bank run.

2 See for example Deily (1991) or Lieberman (1990) on firm exit models in competitive environment.


Diamond and Dybvig (1983) provides a framework for bank runs with two types of consumers: the patient and the impatient. Bank functions as provider of insurance against different consumption levels through time. The demand deposit contract guarantee the depositors to be able to withdraw their fund at time T = 1 or T = 2 satisfying sequential service constraint, or first-come-first-served basis. Bank places fund on production with non-risky technology that takes 2 periods (from T = 0 to T = 2). Deposits are collected by bank at time T = 0. The patient depositors/consumers prefer to consume early and will withdraw their deposits (at T = 1) before their fund could be used to its full capacity as production capital. Thus, they withdraw their fund at par. The patient consumers would rather consume later and wait until their fund grow as a result of production, thus earning more (at T = 2) than what the impatient consumers received (at T = 1). Under full information, the optimal risk sharing or the “good” equilibrium is achieved. The “bad” equilibrium involves bank run, and happens when fraction of patient/impatient consumers is stochastic and all consumers are panic and try to withdraw their funds at T = 1, thus interrupting all production.

The assumption of sequential service constraint is important for bank run to happen in Diamond and Dybvig model. As soon as consumers have a reason to believe that there will be many withdrawals then they will withdraw their funds from banks. In this model, panics are due to random withdrawals cause by self-fulfilling beliefs. This is posed as a problem in the model by Calomiris and Gorton (1991). First, the justification for sequential service constraint is unclear. If this assumption is dropped – say, consumers have limits in the amount of fund they can withdraw at one time - then there will be no panics. Second, it is hard to find example of events that can cause a change of belief that leads to banking panic. The banking crisis in Indonesia is a good example of this type of event. This will be discussed in section 3.

The second framework of banking panics is based on asymmetric information model pioneered by Akerlof (1970). Banks can raise fund based on consumers’ trust. Depositors have very little information on where their fund is invested, thus this is asymmetric information between depositors and bank managers. Bank run - a massive withdrawal on only one bank - happens when depositors lose confidence on the ability of banks to fulfill the demand deposit contract. Because of this asymmetric information and the terms in contract, bank run served as mechanism for depositors to monitor the performance of the bank [Calomiris and Kahn 1991]3. In an extension of Diamond and Dybvig model, Jacklin and Bhattacharya (1988) formulated an information-based runs characterized by two-sided asymmetric information: unobservable consumption preference of the depositors by bank managers and unobservable bank asset quality by depositors. In this set up, bank run happens because of new information on bank asset quality rather than fear of other

3 Calomiris and Kahn (1991) provides framework on how demand deposit contract has important advantage as part of an incentive scheme for disciplining bankers. The maturity mismatch embedded in banking business and the ability of depositors to make early withdrawals provide incentives for depositors to monitor the bank.


depositors’ actions. In the presence of asymmetric information, depositors are forced to use aggregate information to assess a bank’s performance [Gorton 1988]. There are three non-mutually exclusive causes of banking panics: 1) extreme seasonal fluctuations, 2) unexpected failure of large - typically financial – corporation, and 3) major recessions4. Asymmetric information problem can be the source of banking panics because depositor rush to make withdrawals from solvent as well as insolvent banks since they cannot distinguish between them [Mishkin 1991].

In the event of banking panic, if convertibility is not suspended, as long as the deposit withdrawal lasts, the lender of the last resort needs to provide liquidity support. The US savings and loan debacle in …. is one example of this type of crisis. At the time, the Federal Reserves Bank has to step in and provide liquidity in order to avoid further widespread effect on the US financial system. Widespread banking panic can cause liquidity problem to even a solvent banks. It is also important to know that liquidity support is usually accompanied by high interest rates. Unless a troubled bank comes up with a brilliant portfolio management and/or manages to provide additional capital to correct its balance sheet, then it is very likely that the bank will become insolvent. To make things worse, there usually exist regulations and legal limits on banks that are allowed to continue to operate.

The arguments to save or to freeze bank(s) seem to be conflicting over the time. Theoretically, under perfect competition framework, banks should be allowed to fail, just like any firms in other industries. However, the nature of business of a commercial bank makes this institution, even more than any other financial institutions, socially fragile. This is precisely the reason bank is heavily regulated. Freezing or liquidating a bank means to revoke its license to operate, sell its assets and pay off its liabilities. However, in practice, the cost of liquidating a bank is not small. Using US data on bank failures from 1985 through mid-year 1988, James (1991) comes up with an average number of 30% of the failed bank’s assets of loss when a bank is liquidated. This cost is including direct costs of resolution (i.e. administrative and legal expenses), which counts for 10% of the bank’s assets and larger than the direct cost of bankruptcy of non-financial firm. The rest of the loss comes from selling the assets at recovery value5. Tussing (1970) argues that there are two important linkages that make it hard to freeze banks. The first one is the linkage between the failure of unsound or poorly managed bank and the failure of other banks6. As it was discussed before, because depositors have very little information about the financial condition of banks, depositors may exhibit herding behavior by withdrawing fund from solvent banks. The second linkage is called the customer linkage. This linkage involves the

4 Gorton (1988), pp. 224. 5 This recovery value is discounted from the book value. It is unavoidable since the objective of the sale of failed bank’s assets is to recover the asset value as fast as possible to be able to pay off the bank’s liabilities. Also, the client – bank relationship is embedded in bank’s non-fixed assets, which makes them hard to value at par. 6 The popular term for this linkage is “domino effect”.


harmful effects on clients, customers, businesses that benefited from the relationship with the failed bank, but are not responsible for the failure of the bank.

Because of the possibility of the loss incurred when a bank is liquidated, the larger the bank’s assets, the more costly it is to liquidate the bank. Therefore, it is justified to consider that large banks have less probability to be liquidated. Aside from the reason of the liquidation cost, it is also believed that large banks are not likely to have bad management and less susceptible to face major financial problem over a single incident of misappropriation of funds or poor judgment [Horvitz 1975]. However, this view was only shared a long time ago (early 1960s within the US banking system). As the financial market developed and opportunity to gain profits is bigger then large banks start to take more risk, and push the boundary of the allowable activities by a conventional bank. This development raised the probability of a large bank to fail by bad management.

Nevertheless, there was always different treatment in handling a small bank failure and a large bank failure. In general, there are three different ways of handling a bank failure. First, the authority can take over the bank’s asset. It will liquidate the assets and pay off the liabilities (including to depositors) from the fund recovered from the assets liquidation. Second, authority can force the bank to be acquired by or merged to another bank. This way is more desirable if the cost of liquidating the bank is higher than saving it. The third way is to provide temporary loan to the failed bank to overcome its problem, assuming that the problem is not systemic. The decision among these three ways is easier if the failed bank is small. The administrative cost to verify which way is the best will be bearable so that it is likely that the authority will make the right decision. The cost is much higher when the failed bank is significantly large. The complexity of the business transactions, the number of accounts, and the magnitude of the liabilities may be too costly to verify. This all could lead to the only alternative: to save the bank.

Historically, it is expected that small and large banks received different treatments. Liquidation of large banks is perceived to be a bad idea since the cost of verifying all the accounts involved will be a lot higher than providing temporary relief fund while looking for a better way to save the bank [Mayer 1975]. The different treatment also supports the previous discussion. Even before taking into account the social cost coming from the customer linkage, the cost of liquidating large bank is already higher than small bank. Therefore even though it is theoretically considered inefficient since it eliminates the potential positive externality generated by liquidating the unhealthy large banks and may even cause moral hazard behavior of the management of large bank, liquidating large banks are always avoided.

3. Banking System in Indonesia during the Crisis: Stylized Facts By the end of 1996, financial system of Indonesia is dominated by commercial

banks. Commercial banks’ assets count for …..% of the entire financial system, which includes insurance companies, savings and loans, and investment brokerage. The banks are


divided in five different groups depending on the ownerships: state-owned (owned by central government), regional (owned by provincial government), private-forex (privately owned and allowed to conduct foreign exchange transactions), private-non-forex (privately owned and not allowed to conduct foreign exchange transactions), joint-venture (privately owned by foreign banks and domestic investor), and foreign (subsidiary of foreign banks). These banks, capital requirements fulfilled, can open branch offices in any cities in the country. The market power of commercial bank is segmented. By 2001, the ten biggest banks count for almost 60% of the entire banking system7. Six out of a total of seven state-owned banks in the system are among these top ten banks.

Before the crisis, Indonesia enjoyed free capital mobility on a managed floating exchange rate regime. The predictability of the exchange rate combined with the freedom to borrow from abroad and interest rate differential made it possible for the financial system to somewhat gain arbitrage profit by borrowing foreign fund and lending it to local business either in foreign currency or local currency (Rupiah) at higher interest rates. However, high degree exposure of banks’ liabilities in foreign currency made the financial system vulnerable to sharp depreciation of the exchange rate.

As some other emerging markets that were experiencing balance of payment problem, the Indonesian government turned to IMF for relief fund and economic restructuring program. At the time, IMF thought that financial reform was the key to resolve the problem8. Having made the commitment to the IMF for serious restructuring on the financial system that was heavily dominated by commercial banks, Indonesian government decided to liquidate 16 private banks on November 1, 1997. At the time, it was considered a breakthrough effort since there had never been any significant efforts to provide some sort of "punishment" to moral hazard behavior in the banking system. However, on the other side of the story, it was also considered a reckless execution, since it was not supported by a clear procedure on how to guarantee the public claim on the liquidated banks. A move that was expected to be a signal of serious restructuring effort turned out to cause banking panics. People had very little information about the real financial conditions of their banks because of the lack of transparency and knowledge in banking financial reports. Out of fear that their banks would also be closed down, the public rushed the banks, withdrawing their fund. At the time, government did not suspend the convertibility, prompting massive withdrawal of fund from mostly private banks. Some of the fund was moved to state-owned banks or foreign banks (‘flight to safety’), and some was “kept under the pillow”9. Under the central bank regulation, suspension of convertibility can be imposed toward a bank in the way of banning the bank from

7 The ten biggest banks count for 59.35% of the entire banking system, which consists of 239 banks by the end of 1996. 8 Various publications of IMF around the time of the East Asian Crisis suggested that IMF thought the common problems in Indonesia, Korea, and Thailand (three countries which were hurt the most by the currency crises) was the fact that their financial systems were below the standard. 9 “keeping money under a pillow or mattress” is the expression used in Indonesia for keeping money at home and not taking advantage of the fact that interest can be earned if the money was kept in a bank account.


participating in the clearing process. Furthermore, when the situation gets worst, the central bank can revoke the bank’s license. However, out of fear of losing public confidence toward the entire banking system, the government decided not to suspend the convertibility. The rush continued and only slowed down when the government announced explicit guarantee on public deposit on January 26, 1998. However, the bank panic already caused severe financial bleeding for most banks. Because of the terms on bank contract over third party deposits, even a healthy bank will suffer after experiencing massive withdrawal.

Because the government decided not to suspend the convertibility, banking panic forced most banks to become insolvent. Banking panic then became banking crisis. Diamond and Dybvig model provides two measures to prevent bank run. They are suspension of convertibility and deposit insurance. None of these two measures appeared to support the banking system in Indonesia at the time. During the banking panic, the Indonesian government was also directly providing deposit insurance through the role of the central bank as lender of the last resort. In a way, Indonesian banking crisis provides the stylized fact that matched the assumption of the Diamond and Dybvig model10. Thus this resolves the problem in Diamond and Dybvig model posed by Calomiris and Gorton (1991). Indeed there is such a condition in the economy could create the appropriate shock that caused bank panic, just as described in Diamond and Dybvig model. In this case it was the currency crisis that hurt most banks because of the high exposure on the foreign currency liabilities. However, Diamond and Dybvig also concluded that if the production technology is risky, the lender of the last resort can no longer be as credible as deposit insurance. In the case of Indonesia, the production technology also became risky, since the real sector was also highly exposed to foreign currency liabilities. Banking panic was unavoidable even though the role of the lender of the last resort was present.

In January 1998, the Indonesian Banking Restructuring Agency (IBRA) was created to be a government agent responsible for implementing banking restructuring process in Indonesia. This responsibility includes selling and recovering assets of frozen banks, paying off the liabilities of frozen banks and implementing the banking recapitalization program. The central bank continued the role of bank supervision. By May 1999 it also resumed the role of banking licensing authority. IBRA is in charge in supervision of banks that are taken over and recapitalized by the government. By …, … % of the entire banking system was controlled by the government. The liquidity support from the central bank during the banking panic period has made this possible as most banks then had large balance of liabilities to the government. If the government and the central bank decided to save the bank, these liabilities then converted into government ownership. Otherwise, the bank’s license was revoked and IBRA would be in charge to recover the value of the assets through the bank’s assets sale and pay off the bank’s liabilities. Further restructuring process on a saved bank could also involve merging it to another bank.

10 Recall that Diamond and Dybvig model requires an event when all consumers/agents change their belief on the banking system so that they all decided to withdraw at T = 1.


The banking recapitalization program was announced in March 1999 based on the due diligence process conducted earlier (August – October 1999) and a joint-decree of the Minister of Finance and Bank Indonesia Governor dated February 8, 1999 on “Commercial Bank Recapitalization”. In the due diligence, banks are categorized in 3 groups based on the capital condition. Category A is for banks that have CAR of 4% or more. Category B is for banks that have CAR between -25% and 4%. And category C is for banks that have CAR below -25%. Ironically, all 7 state-owned banks and as many as 4 regional banks were under category C at the time. 8 other regional banks were in category B. In the recapitalization program, the shareholders of banks in category B and C were required to add at least 20% of additional capital needed to reach the 4% CAR requirement. According to the decree, if banks fell in category B or managed to add capital to satisfy category B requirement within 30 days, they are eligible for recapitalization program. The decree also mentioned that all 7 state-owned banks and 12 regional banks will be included in the recapitalization program for reasons of the significance of those banks in the market, especially for small to medium business. 20 private banks which include 13 banks that already taken over before went under the recapitalization program. 7 of these banks fell under category C as reported in due diligence. The recapitalization program had significant impact on the financial conditions of the banks included in the program, since the injection of government capital changed the entire financial structure of the banks. The restructuring process further went on merging some of the recapitalized banks. After mergers in 1999 to 2002, only 3 out of 20 private banks involved in this program have maintained their own identities. 1 bank was merged into a state-owned bank. The rest were merged into 2 different private banks11. The following Table 1 illustrates the development of the number of banks at the end of the years in review according to the bank groups. Table 2 listed the events during banking restructuring process. And Figure 1 illustrates the end of year position of total assets.

Table 1.

Number of Banks by Bank Group End of Year 1996 1997 1998 1999 2000 2001 2002 2003 State-owned 7 7 8 5 5 5 5 5 Private-Forex 85 79 77 48 38 38 36 36 Private-Non Forex 79 65 64 45 43 42 40 40 Regional 27 27 27 26 26 26 26 26 Joint-venture 31 34 34 30 29 24 24 20 Foreign 10 10 10 10 10 10 10 11 Total 239 222 220 164 151 145 141 138

Source: Commercial Banks Monthly Report and Bank Indonesia Annual Reports

11 Although one of these 2 new banks resume the old name of one of the banks merged.


Table 2 List of Events during Banking Restructuring (1997 – 2003)

Date Events Nov 1, 1997 Feb 14, 1998 Apr 4, 1998

May 29, 1998 Aug 21, 1998 Oct 31, 1998 Mar 13, 1999 Apr 21, 1999 May 28, 1999 Jul 23, 1999 Jul 31, 1999 Aug, 1999 Dec 20, 1999 Jan 29, 2000 Jun 30, 2000 June 30, 2000 Mar – Jul 2000 Oct 20, 2000 Feb 5, 2001 Mar 27, 2001 Sep 7, 2001 Sep 28, 2001 Oct 30, 2001 Oct 28, 2002

16 private banks liquidated. 3 private banks taken over. 7 private banks frozen, 3 private banks and 1 state-owned bank taken over♠. 1 private bank taken over. 3 private banks (taken over in Apr 4, 1998) frozen. Due diligence of banks (started in Aug 1998) completed. 37 private banks, 1 joint-venture bank frozen, 7 private banks taken over. This date also marked the announcement of the due diligence results which determined the requirements of banks that would be eligible for recapitalization program (or would be eligible to be saved by the government). All state-owned banks and 12 regional banks are included in this program. 1 private bank taken over, 7 other private banks recapitalized. 12 regional banks recapitalized. 1 private bank taken over. 1 private bank (recapitalized in Apr 21, 1999) merged into a state-owned bank. 4 state-owned banks (including the one taken over on Apr 4,1998) merged. 2 private banks (taken over on Apr 4, 1998) merged, 2 joint-venture banks merged. 1 private bank frozen♣. 8 private banks merged into 1 private bank (merged earlier in Dec 20, 1999). This new merged bank then recapitalized. 2 private banks (taken over in Apr 21, 1999 and July 23, 1999) recapitalized. 4 state-owned banks recapitalized. 2 private banks frozen. 1 joint-venture bank frozen. 2 joint-venture banks merged. 2 joint-venture banks merged. 3 joint-venture banks merged. 1 private bank frozen. 5 private banks (4 recapitalized on Apr 21, 1999, 1 on June 30, 2000) merged.

♠ Taking over a state-owned bank means the supervision of this bank is put under IBRA. ♣Data for this bank is incomplete and excluded from the observations Source: Bank Indonesia


Figure 1.

Total Asset by Bank Group*

0

100

200

300

400

500

600

700

1996 1997 1998 1999 2000 2001 2002 2003

Bill

ions

of R

upia

hs

State-owned Private - Forex Private - Non Forex Regional Joint Venture Foreign

* End of year position. Source: Commercial Banks Monthly Report, Bank Indonesia

During the restructuring program, all banks were required to submit

comprehensive business plan to improve their performance. Although considered healthy, the banks that fell into category A still need to improve their performance. This is because the authority was aiming to have 8% CAR requirement by the end of 2001. This requirement is in accordance to the recommended standard set by Bank of International Settlements.

4. Data and Methodology

4.1. General description of the data

The data used in this paper is provided by Bank Indonesia, the central bank of Indonesia, which is also the banking authority of Indonesia12. This set of data was acquired when author stayed with the bank for five weeks during summer 2004. Data was gathered from monthly financial report of banks to the central bank since January 1997 to December 2003 (7 years). Once a bank submitted a monthly financial report, it is not necessarily the final version of the report. Bank is obligated to make revision on its financial report if some

12 As banking authority, Bank Indonesia issues banking regulations (based on Banking Act), conduct off site and on site supervision of bank operation, imposes sanctions on violation of banking regulations. However, only since May 1999 under a new central bank act, the central bank assumed the authority of banking licensing. Before that, the ministry of finance granted and revoked the bank licenses. The new central bank act also guaranteed the independence of the central bank (monetary, banking and payment system) policies from the government.


discrepancies or errors were found during the supervision or examination sessions. Moreover, the numbers reported monthly to the central bank are the numbers calculated using estimated tax payment. The official tax payment is reported on the annual financial report that has to be audited by certified public accountant.

Change in reporting format was noted on September 2000. This resulted change in how the data items were arranged, thus change in the database structure. However, the clear description of each format allowed for smooth transition from old format to the new one, so that the variables needed for the econometric model could be maintained throughout the series.

The main part of the financial report consists of balance sheet and profit and loss statement. In addition to that, a bank also submits some detailed reports as required by the banking regulation, such as its portfolio and its off-balance-sheet activities. As some banks which were closed down before the series ended would have stopped sending report to the central bank, the panel data is not balance.

Some of the series in the data are missing. This is possible since a bank could miss submitting a report in a certain month for various reasons. Administrative fine is imposed on negligence to submit monthly report if indeed it is the bank’s fault13. However, this bank might not be hold accountable to still submitting the financial report of the particular month, as long as the report for the subsequent month is submitted. This is possible since the bank supervisors may already obtained the hard copy of the missing report.

4.2. CAMEL variables

A common way to assess a firm’s health is by evaluating its capital, asset quality, management, earning and liquidity (CAMEL). Capital ratio is usually used as measurement to assess the capital adequacy of a bank. A bank with higher capital-to-asset ratio is protected against operating losses more than a bank with a lower ratio, although this depends on the relative risk of loss at each bank. There are several standard measures of capital adequacy: 1) Risk-adjusted capital ratio; 2) Total capital to total assets ratio; 3) Leverage ratio; and 4) Total risk-adjusted capital ratio14. By Bank for International Settlement’s standard, the last measure is used for banks and so is in Indonesia. However, calculation of this capital adequacy ratio (CAR) requires risk-weighted assets (RWA) data. Author also attempted to get the CAR numbers calculated by the central bank. By the time this paper was written, neither sets of data (RWA and CAR) were completely acquired. Therefore, ratio of total capital to asset is used as proxy. This capital measure is reported in bank’s balance sheet. It includes core capital, reserves, retained earning, and profits. Risk-weighted asset will still be incorporated in a different way. This will be taken care of in the asset quality measure.

13 The non-existence of some series of monthly report could also be caused by technical errors from the central bank’s database, which the author considered irrecoverable. However, none of the missing series are in consecutive order, and it is less than 0.3% of the entire series. 14 See Fitch 2000, pp. 76 for complete definitions of these measures of capital adequacy.


Asset quality is measured by collecting the data on the category of earning assets of each bank for every month. Before March 1998, earning assets (credits, bonds, inter-bank placements, stocks, trade financing transactions and derivative transactions) were categorized in four levels according to the degree of performing (“4” being “non-performing”). Beginning March 1998, there are five levels. For simplicity, and also confirmed by the banking regulations, level 1 and 2 after March 1998 and level 1 before March 1998 are categorized as “performing earning assets”. Levels 3 and 4 before March 1998 and levels 3, 4, 5 after March 1998 are categorized as “non-performing earning assets”15. The measure of asset quality will be represented by the fraction of “performing earning assets” within total earning assets.

Among the CAMEL variables, only management is hard to quantify. In Indonesia, the evaluation of management is mostly done qualitatively16. One measure that is relevant to management is the ratio of operational revenue to operational expenditure (OPR_RATIO). This ratio is also used in representing the earning variable. Assuming that how good the management is correlated with this ratio, this ratio is used to represent the management measure.

Earning variable will be represented by the return on asset (ROA) or ratio of profit (net income) to asset. Another ratio relevant to the earning variable is the ratio of operational revenue to operational expenditure. However, as it was stated before, this measure will be used as management variable. Two other measures are also used in assessing earning. They are net interest margin (NIM) and return on equity (ROE) or ratio of profit to capital. Net interest margin is the difference between interest revenue and interest payment. To normalize this measure, NIM will be divided by total earning asset.

Liquidity variable is represented by the ratio of liquid asset to total liabilities. In Indonesia, liquid assets of banks consist of the bank’s position in cash (in vault) and central bank placement (including reserve requirement and excess reserves stored in checking account at the central bank and the central bank CD17). The liquidity ratio measured how bank manage to fulfill its obligation to provide fund whenever it has to pay off its liabilities, including deposit withdrawal.

4.3. Methodology

Unlike other panel data research on firm exit models, which are mostly within competitive market, monopoly, or oligopoly frameworks, this is a central planner problem.

15 The earning asset quality levels after March 1998 are “performing” (1), “under special attention” (2), “less performing” (3), “questionable” (4), and “non-performing” (5). Level 2 “under special attention” was not part of the system before March 1998. 16 Bank supervisor will have a set of questions for the management to answer. The bank’s management performance is determined by the answers to this questionnaire. 17 Until recently, Indonesia does not have a risk free money market instrument besides central bank CD. Domestic government bond only started to be traded after the financial crisis, although the market is still very thin. The central bank CD (called SBI, short for Sertifikat Bank Indonesia) is considered liquid since it can be used as collateral to go for discount window (borrowing from central bank) or interbank borrowing.


During the banking restructuring process, banks do not make decision whether they want to shut down their operation, merge with other banks, or inject more capital to resolve their financial problems. The central bank and the government make those decisions. A bank could exit the market because they have become insolvent and could not fulfill the requirements of capital ratio imposed by the authority. Insolvent bank could also stay in the market if the authority chose to save it. The following is the function representing the central planner’s effort on the restructuring process18.

itkititit ZXY εδγβ +++= ''* (1) where

Y* : The restructuring effort on the particular bank X : vector of CAMEL variables or performance indicator Z : vector of melting factors δ : time dummies i : index for individual bank t : index for month and year k : index for year

The model is estimated using ordered probit specification. The dependent variable Y

is an ordered qualitative variable related to the unobserved effort given by the authority on bank restructuring Y*, such that

Y = {

1 2 3

if Y* < λ0 for liquidated/fozen banks (2)if λ0 < Y* < λ1 for healthy banks if Y* > λ1 for restructured banks

with λ’s being the estimated threshold value representing the effort the authority put for banks receiving different treatment. Restructured banks are banks that received restructuring treatment from the authority. They were taken over by the authority and/or received injection of capital or recapitalized and/or merged to other banks. Healthy banks are the banks that were diagnosed healthy and did not participate in the recapitalization program. The liquidated/frozen banks were the banks that were diagnosed unhealthy. Although there was justification, the authority had the discretionary in deciding whether to save or freeze these banks. Appendix A provide a more detailed algorithm on how to set up the ordered dependent variables based on the events listed on Table 2.

We can think of Y* as the amount of money or man-hours the authority is willing to spend to deal with a bank. The authority has very little interest to put effort on keeping the

18 This framework set up follows Deily 1991.


liquidated/frozen banks. The healthy banks are obviously kept by the authority since they were performing adequately in the banking system. In general, during banking restructuring system all banks are required to submit business plan to improve their performance. Even if effort needs to be put on improving healthy banks, it will be less than the effort on restructuring the unhealthy banks.

The performance indicators are the CAMEL variables explained in the previous sub section. Meanwhile, there are two different melting factors used in the model. The first one is DSTATE, which is a dummy variable of whether a bank is government-owned or not. The second one is LASSET, which is the log of total asset. The estimation also includes annual time dummies to capture the effects of the differences in economic and political conditions surrounding the restructuring process every year. Individual effect was not considered since this is a non-competitive environment. Banking panic and tight monetary policy also left very little room for the idiosyncratic behavior of the bank. Even when bank attempted to attract the market with competitive financial products, consumers still considered safety first. This has been the mood of the banking system throughout the banking restructuring process.

It is interesting to observe the outcome of β coefficients. The authority decided the eligibility of a bank to participate the banking restructuring program based on its CAR. This variable is represented in this paper by Capital-Asset ratio (CA_RATIO) and Asset Quality (ASSET_QUALITY). ROA, ROE, NIM and Operational Ratio (OPR_RATIO) can be considered as the variables representing the business side of the bank19. And we can consider liquidity is an indicator of prudential banking behavior of the bank. However, we should expect γ to be positive, since the bigger the value of the melting factors, the more willing the authority to try to keep the bank. Our aim is also to see whether the β coefficients are more significant than γ coefficients.

5. Results and Discussion The ordered probit regressions results on different combinations of CAMEL

variables representing earning are presented in Table 3. The first thing that we noticed is that LASSET is always significant. Coefficient for LASSET is always more significant than any other coefficients. This confirmed how much the authority care about the size of the banks. The bigger the bank, the more the authority is willing to put effort in restructuring the bank. Surprisingly, the coefficient for DSTATE is not significant. The decision on including all troubled government-owned banks was overwhelmed by the fact that these banks mostly had big asset.

CA_RATIO and ASSET_QUALITY gave positive relationship over the restructuring effort. This is not surprising, since these two variables are proxy to the CAR, which is the indicator used by the authority on deciding the eligibility of banks for banking

19 Recall that banks were required to submit business plan to show their plan in improving their performance.


recapitalization program. Higher CAR represents better solvency of bank, which makes it more desirable for the authority to be restructured/saved or to be kept in the system. LIQUIDITY basically gave the same effect for authority. This variable represents how much the bank can satisfy withdrawals or pay off its short-term liabilities. The better the bank’s liquidity condition, the authority is more interested in putting more effort to restructure the bank or to keep the bank in the system.

Table 3

Determinants of Restructuring Effort

(1) (2) (3) (4) (5) (6) (7)

CA_RATIO 0.0008

(0.0005) 0.0009** (0.0004)

0.0008 (0.0005)

0.0009** (0.0004)

0.0008 (0.0005)

0.0009** (0.0004)

0.0008 (0.0005)

ASSET_ QUALITY

0.2217 (0.1414)

0.2123 (0.14099)

0.2123 (0.1410)

0.2216 (0.1414)

0.2123 (0.1410)

0.2217 (0.1415)

0.2217 (0.1414)

OPR_RATIO -0.3822 (0.1014)

-0.3862*** (0.1025)

-0.3862*** (0.1024)

-0.3822 (0.1014)

-0.3862*** (0.1024)

-0.3822*** (0.1014)

-0.3822 (0.1014)

NIM -2.8E-05

(3.04E-06) -2.8E-05*** (3.07E-06)

-

-

-2.8E-05*** (3.07E-06)

-2.8E-05*** (3.05E-06)

-

ROA -6.3E-05 (0.0001)

-

-6.6E-05 (0.0001)

-

-6.6E-05 (0.0001)

-

-6.3E-05 (0.0001)

ROE -0.0037** (0.0017)

-

-

-0.0037** (0.0017)

-

-0.0037** (0.0017)

-0.0037** (0.0017)

LIQUIDITY 0.0002* (0.0001)

0.0002* (0.0001)

0.0002* (0.0001)

0.0002* (0.0001)

0.0002* (0.0001)

0.0002* (0.0001)

0.0002* (0.0001)

DSTATE 0.0395

(0.0406) 0.0403

(0.0405) 0.0398

(0.0406) 0.0401

(0.0406) 0.0398

(0.0406) 0.0400

(0.0406) 0.0396

(0.0406)

LASSET 0.2149*** (0.0111)

0.2148*** (0.0111)

0.2149*** (0.0111)

0.2148*** (0.0111)

0.2150*** (0.0111)

0.2148*** (0.0111)

0.2149*** 0.0111

YEAR1998 0.3610*** (0.0603)

0.3585*** (0.0601)

0.3584*** (0.0601)

0.3611*** (0.0603)

0.3584*** (0.0601)

0.3611*** (0.0603)

0.3610*** 0.0603

YEAR1999 1.1543*** (0.0809)

1.1500*** (0.0808)

1.1501*** (0.0808)

1.1543*** (0.0809)

1.1501*** (0.0808)

1.1543*** (0.0809)

1.1543*** 0.0809

YEAR2000 0.9314*** (0.0660)

0.9381*** (0.0659)

0.9381*** (0.0660)

0.9315*** (0.0660)

0.9381*** (0.0659)

0.9315*** (0.0660)

0.9314*** 0.0660

YEAR2001 0.5327*** (0.0695)

0.5473*** (0.0696)

0.5471*** (0.0696)

0.5329*** (0.0695)

0.5471*** (0.0696)

0.5328*** (0.0695)

0.5327*** 0.0695

YEAR2002 -0.4201***

(.0657) 0.4273*** (0.0661)

0.4269*** (0.0661)

0.4206*** (0.0656)

0.4269*** (0.0661)

0.4205*** 0.0656

0.4202*** 0.0657

YEAR2003 -0.0175 (0.0454)

-0.0137 (0.0454)

-0.0147 (0.0454)

-0.0171 (0.0454)

-0.0144 (0.0454)

-0.0168 0.0454

-0.0178 0.0454

Pseudo R2 0.1861 0.1847 0.1848 0.1861 0.1848 0.1861 0.1861

*** significant at 1% level, ** significant at 5% level, * significant at 10% level Notes: numbers in parentheses are robust standard errors


The interesting effects came from the variables representing the management and earning of the bank: OPR_RATIO, NIM, ROA and ROE. It is important to realize that during the banking crisis, most banks had difficult time to maintain good financial indicators. For example, average ROE in December 1998 and December 1999 are -437.23% and -110.8% respectively. Again, this fact was overwhelmed by the authority’s concern over the cost of liquidating banks. Once the decision to safe a bank is made, the worse the earning and management of the bank, the more effort will be put by the authority to improve in the restructuring process. This represented how much the authority is willing to save a troubled bank once this bank is eligible (by the asset size) for restructuring process.

All yearly time dummies are significant. The ordered probit results also revealed that in all combinations of variables, the yearly time dummies are most significant in year 1999 and 2000. The coefficients are also the largest in year 1999 and 2000. These two years marked the heaviest recapitalization activities from the authority. It is interesting to see that in addition to the effects from the performance indicators and melting factors, the time dummies basically capture how big the restructuring effort put by the authority in those years.

[notes: need more discussions - linking the results to the theory - on this section]

6. Conclusion The stylized facts presented in Indonesia’s case provide an example on how

sequential service constraint without suspension of convertibility and deposit insurance could create chaos that caused banking panic and banking crisis in Indonesia. In effort to save the national banking system, the government had to perform the bank restructuring process. This paper also showed the dilemma experienced by the banking authority (government and central bank) in determining which banks to save or to freeze. It is not surprising that the authority care a lot about the size of the bank. The empirical exercise over panel data of bank’s monthly financial report provides evidence that the bigger the bank, the higher the effort put by the authority to improve the bank.


Notes for further work on this paper Do more analysis on the results using the partial derivatives of the regression results and the estimated threshold. Need to check how well the predictions of the estimated coefficients. Given the characteristics of the banks, will it be categorized as healthy, recapitalized or frozen? Do higher resolution analysis, semester per semester pattern observation, to see if there is any pattern change in the restructuring effort. Link this to the list of events. See if the effort is consistent. Acknowledgement

Author would like to acknowledge the support given by Bank Indonesia’s officials, researchers, and staffs during data research at Bank Indonesia’s headquarter, Jakarta in summer 2004. Ita Rulina, Dipa Pertiwi (at Bureau of Financial System Stability) and Yan Syafri (at Directorate of Banking Supervision) especially helped with lots of insights on Indonesian banking system and supervision. Indah Iramadhini, Dian Oktariani, and Eka Vitaloka (at Banking Data Department) provided tremendous support for the panel data. This paper also benefited from Professor Marianne Baxter’s and Professor Victor Aguirregabiria’s valuable advice and direction.


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APPENDIX A.

Algorithm of Setting Up the Ordered Dependent Variables

For each bank i:

*For Liquidated/Frozen Banks (Y = 1) If (t = date of liquidation announcement of bank i) then Yi,t-1 = 1

*For Recapitalized Banks (Y = 3)

trecap = March 1999 (the date the government announce the eligibility of banks for recapitalization)

If (Bank i is taken over at time t) then t1 = t

TakenOver = True If (Bank i is recapitalized at time t) then t2 = t

Recapitalized = True If (Bank i is merged at time t) then t3 = t

Merged = True If (Bank i is liquidated at time t) then t4 = t

Liquidated = True

If TakenOver and Liquidated then Yi,t = 3 for t = t1..(t4-1) If TakenOver and Recapitalized and (Not Merged) then Yi,t = 3 for t = t1..(t2-1) If (Not TakenOver) and Recapitalized and (Not Merged) then Yi,t = 3 for t = trecap..(t2-1) If (Not TakenOver) and Recapitalized and Merged then Yi,t = 3 for t = trecap..t3 If (Not TakenOver) and (Not Recapitalized) and Merged then Yi,t = 3 for t = t3 If (Not TakenOver) and Recapitalized and (Not Merged) then t5 = the date when bank i is announced healthy

Yi,t = 3 for t = trecap..t5 *For Healthy Banks (Y = 2)

If missing/undefined Yi,t then Yi,t = 2

cicilia harun, banking restructuring process in indonesia 1997 - 2003

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