ESSAYS ON BANKING REGULATION AND RESTRUCTURING THE CASE OF INDONESIA

ESSAYS ON BANKING REGULATION AND RESTRUCTURING: THE CASE OF INDONESIA RASYAD A. PARINDURI NATIONAL UNIVERSITY OF SINGAPORE 2006 ESSAYS ON BANKING REGULATION AND RESTRUCTURING: THE CASE OF INDONESIA RASYAD A. PARINDURI (ST (EE)), ITB; (MA (Econ)), Michigan A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY DEPARTMENT OF ECONOMICS NATIONAL UNIVERSITY OF SINGAPORE 2006 i Acknowledgements I thank my supervisor, Dr. Yohanes Eko Riyanto, for his support and encouragement without which I would not be able to …nish this thesis on time. I thank the members of my graduate committee, Prof. Shandre M. Thangavelu and Prof. Julian Wright, and those of my thesis examiners, Prof. Hans Degryse of Tilburg University, Dr. Hur Jung, Dr. Changhui Kang and Prof. Basant Kapur, for their comments and suggestions. I also thank the audience in my Pre-submission Seminar for their relentless questions. Pondering their critiques helps me sharpen my analyses and improve this thesis. I acknowledge the award of the NUS Research Scholarship which had supported me during my three and a half years of research at National University of Singapore. Special thanks go to my colleague, Eni Vimaladewi, who introduces me to the sta¤s of Bank Indonesia’s Department of Banking Statistics. Without her help, I may not get the dataset I extensively use in this thesis. I also thank Juda Agung, Dian Oktariani, Riza Haryadi and Makin Toha of Bank Indonesia for providing the dataset. I am indebted to my wife for her love throughout the years. Last but not least, I thank my mom and dad for always keeping me in their prayers. I dedicate this work to them. ii Contents List of Tables viii List of Figures x Introduction Does Capital Requirement Induce 2.1 Introduction . . . . . . . . . . . . 2.2 Related Literature . . . . . . . . 2.3 Capital Requirement in Indonesia 2.4 Methodology . . . . . . . . . . . 2.4.1 Model Speci…cation . . . . 2.4.2 Main Hypothesis . . . . . 2.4.3 Method of Estimation . . 2.5 Data . . . . . . . . . . . . . . . . 2.5.1 Capital and Risk . . . . . Banks to . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Limit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Risk-taking? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 10 11 11 15 16 17 18 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 20 21 21 2.6.2 Risk Equation . . . . . . . . . . . . . 2.6.3 CAR as Dependent Variable . . . . . 2.6.4 Controlling for Capital and Risk . . . 2.6.5 Allowing heterogeneous responses . . 2.6.6 Interpretation . . . . . . . . . . . . . 2.7 Robustness . . . . . . . . . . . . . . . . . . 2.7.1 More Homogeneous Samples . . . . . 2.7.2 Non-linearity in Regulatory Pressure 2.7.3 Di¤erent Speed of Adjustment . . . . 2.7.4 Other Robustness Check . . . . . . . 2.8 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 25 27 27 31 32 32 32 34 35 36 2.5.2 Regulatory Pressure 2.5.3 Control Variables . . 2.6 Results . . . . . . . . . . . . 2.6.1 Capital Equation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii Does Selling Developing Countries’ Banks to vestors Improve Banks’Performance? 3.1 Introduction . . . . . . . . . . . . . . . . . . . 3.2 Related Literature . . . . . . . . . . . . . . . 3.3 Strategic Sale of Indonesian Banks . . . . . . 3.4 Methodology . . . . . . . . . . . . . . . . . . 3.4.1 Identi…cation . . . . . . . . . . . . . . 3.4.2 Heterogeneous Treatment E¤ects . . . 3.4.3 Main Hypothesis . . . . . . . . . . . . 3.5 Data . . . . . . . . . . . . . . . . . . . . . . . 3.5.1 Dependent Variable . . . . . . . . . . . 3.5.2 Strategic Sale Dummy . . . . . . . . . 3.5.3 Cost Function . . . . . . . . . . . . . . 3.5.4 Other Control Variables . . . . . . . . 3.6 Results . . . . . . . . . . . . . . . . . . . . . . 3.6.1 Basic Results . . . . . . . . . . . . . . Strategic Foreign In. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 37 39 41 43 44 46 48 48 49 50 50 50 51 51 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 53 55 56 3.7 Robustness . . . . . . . . . . . . . . . . . . . . . . 3.7.1 Evolution of Treatment E¤ects . . . . . . . 3.7.2 Matching with other Performance Measures 3.7.3 More Homogenous Samples . . . . . . . . . 3.7.4 Using Frontier Analysis . . . . . . . . . . . . 3.8 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 59 62 63 63 64 3.6.2 3.6.3 3.6.4 3.6.5 Matching and Di¤erence-in-di¤erence Non-parametric Matching . . . . . . Interpretation . . . . . . . . . . . . . Common Time Trend Assumption . . . . . . The E¤ectiveness of Capital Requirement when Regulator does not Observe Bank’s Capital and Investment Decision 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Related Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 The Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.1 The Case of Symmetric Information . . . . . . . . . . . . . . . 4.3.2 Pure Adverse Selection . . . . . . . . . . . . . . . . . . . . . . 4.3.3 Pure Moral Hazard . . . . . . . . . . . . . . . . . . . . . . . . 4.3.4 Adverse Selection and Moral Hazard . . . . . . . . . . . . . . 4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 66 67 69 72 73 76 77 79 80 iv Banks’E¢ ciency and Types of Ownership 5.1 Introduction . . . . . . . . . . . . . . . . . . . . 5.2 Related Literature . . . . . . . . . . . . . . . . 5.3 Bank Ownership in Indonesia . . . . . . . . . . 5.4 The Methodology . . . . . . . . . . . . . . . . . 5.4.1 Panel Stochastic Frontier Models . . . . 5.4.2 How They Di¤er from Standard Models 5.4.3 Introducing Banks’Types of Ownership 5.5 Data . . . . . . . . . . . . . . . . . . . . . . . . 5.5.1 Arguments of the Cost Function . . . . . 5.5.2 The Cost Function . . . . . . . . . . . . 5.5.3 Type of Ownership Dummies . . . . . . 5.6 Results . . . . . . . . . . . . . . . . . . . . . . . 5.6.1 Basic Results . . . . . . . . . . . . . . . 5.6.2 Properties of the Cost Function . . . . . 5.7 Robustness . . . . . . . . . . . . . . . . . . . . 5.7.1 Heterogeneity in Cost Function . . . . . 5.7.2 Averages of Ine¢ ciency Terms . . . . . . 5.8 Concluding Remarks . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 82 83 84 85 85 87 88 89 90 90 91 91 91 93 94 94 95 97 98 Bibliography 100 A Summary Statistics 107 v Summary The …rst essay examines the impact of capital requirement on banks’risk taking in Indonesia. Using dynamic panel data models, we …nd that there is some evidence that banks increase their capital or reduce risk when their capital adequacy ratio (CAR) is lower than, or approaching, the eight percent regulatory minimum. The statistical signi…cance of our results, however, is low. Second, when we allow banks to respond to the capital requirement heterogeneously, we …nd that only large private-domestic banks respond to regulatory pressure properly. This essay’s contribution is to o¤er some insights into how capital requirement may a¤ect banks’ risk taking in developing countries. Second, we address common improper econometric methods in this line of literature, i.e. the estimation of nonautonomous system of two equations using simultaneous equation approach. Third, using dynamic panel data models we could deal with the two key unobserved variables (banks’internal capital- and risk targets) better, and take other unobserved banks’ heterogeneity more explicitly into account. In the second essay, we examine whether selling banks that were bailed out and recapitalized by the Government of Indonesia to strategic foreign investors improves banks’performance. This banking industry overhaul costs government budget severely. By the end of 2000, the government has to service debts and to …nance a budget de…cit which are more than, respectively, 100 percent and percent of GDP. vi Facing this large …scal de…cit, the government simply has to sell those private banks. Using di¤erence-in-di¤erence models and matching estimators, we …nd that strategic sale of banks in Indonesia does improve banks’performance. On average, strategic sale is associated with about 15 percent cost reduction or more. The focus of this essay is on overcoming problems in treatment evaluation. First, we never observe counterfactuals and therefore they have to be estimated. Second, investors may "cherry pick" the most promising banks, the government may sell only the best banks to maximize revenue, and these choices may not be orthogonal to unobservable factors that a¤ect banks’ performance. The structure of our data, to some extent, reduces this potential source of bias. Second, to control for timeinvariant unobservable banks’ characteristics that may confound identi…cation, we use panel data and di¤erence-in-di¤erence models. Further, to address some potential biases in these latter models, we use matching estimators. The third essay is a short theoretical paper that looks on whether capital requirement and audit policy could prevent banks from taking excessive risk when regulator does not observe banks’capital and investment decision. Banks may be of two types: high- or low capitalized; and have two investment choices: risky or prudent assets. We explore how capital requirement and audit policy may induce banks to be well behaved. We show that, if regulator does not observe banks’capital or investment decision, then regulator must audit banks to enforce the capital requirement. The fourth essay looks at the relationship between banks’e¢ ciency and types of ownership in Indonesia. Literature suggests that ownership matters. In particular, researchers argue that state-owned banks are less e¢ cient than private banks and foreign-owned banks. Taking Indonesian banking industry as a case study, we investigate the relationship between banks’ types of ownership and bank’s performance. vii We use Greene (2002, 2005)’s “true”panel data stochastic frontier models to take unobserved banks’heterogeneity more explicitly into account. We …nd that state-owned banks are the least e¢ cient banks, and joint-venture banks are the most e¢ cient ones. viii List of Tables 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 Capital Equation . . . . . . . . . . . Risk Equation . . . . . . . . . . . . . CAR Equation . . . . . . . . . . . . Controlling for Capital and Risk . . . Heterogenous Responses . . . . . . . Homogeneous Samples . . . . . . . . Non-linearity in Regulatory Pressure Di¤erent Speed of Adjustment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 24 26 28 30 33 34 35 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 Indonesia’s Bank Restructuring . . . . . . . Change in Ownership of Banks, 2000-2005 . Basic Results . . . . . . . . . . . . . . . . . Di¤erence-in-di¤erence and Kernel Matching Common Time-trend Assumption . . . . . . The E¤ect of Strategic Sale Overtime . . . . Matching with Other Performance Measures More Homogenous Samples . . . . . . . . . Stochastic Frontier Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 49 51 54 58 61 62 63 64 4.1 Types of Assets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 5.1 5.2 5.3 5.4 . . . . 84 92 96 97 Key Variables Used in Chapter . . . . . . . . . . . . . . . . . . . . Key Variables Used in Chapter . . . . . . . . . . . . . . . . . . . . Key Variables Used in Chapter 5: All Banks . . . . . . . . . . . . . . Key Variables Used in Chapter 5: State- and Regional Development Banks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A.5 Key Variables Used in Chapter 5: Private National Banks . . . . . . A.6 Key Variables Used in Chapter 5: Joint Venture and Foreign Banks . 107 108 108 A.1 A.2 A.3 A.4 Ownership of Banks, 2001 . . . . . Basic Results . . . . . . . . . . . . Heterogeneity in Cost Function . . The Averages of Ine¢ ciency Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 110 111 96 Dependent variable: ln(cost) Bank types Large private national Small private national Regional development Joint venture Foreign owned Cost Function Loans Securities Gov't bond Other assets Wage Interest Observations Pooled Random effect (1) (2) 0.11 (0.03) 0.20 (0.04) 0.12 (0.04) -0.15 (0.03) -0.06 (0.04) -0.36 (0.19) -0.34 (0.20) 0.07 (0.21) -0.88 (0.19) 0.10 (0.24) 0.47 (0.01) 0.02 (0.00) 0.02 (0.00) 0.50 (0.01) 0.26 (0.01) 0.67 (0.01) 2,393 0.25 (0.00) 0.02 (0.00) 0.01 (0.00) 0.36 (0.00) 0.16 (0.00) 0.52 (0.00) 2,393 Note: "Random effect" is Greene's "true" random effect stochastic frontier model. Standard errors are in parentheses Table 5.3: Heterogeneity in Cost Function 97 Pooled (1) Random effect (2) State owned Large private Small private Regional Joint venture Foreign owned 0.33 (0.25) 2.51 (0.89) 0.24 (0.18) 1.58 (0.57) 0.25 (0.18) 1.01 (0.27) 0.21 (0.10) 1.22 (0.26) 0.21 (0.27) 0.94 (0.25) 0.46 (0.88) 1.34 (0.31) Note: "Random effect" is Greene's "true" random effect stochastic frontier model. "Regional" stands for regional-development bank. Standard errors are in parentheses Table 5.4: The Averages of Ine¢ ciency Terms 5.8 Concluding Remarks We have examined the relationship between banks’type of ownership and banks’ e¢ ciency using Greene’s "true" panel data stochastic frontier model. We …nd that, even after taking unobserved heterogeneity more properly into account, state-owned banks in Indonesia seem to be the least e¢ cient banks and jointventure banks to be the most e¢ cient ones. 98 Chapter Conclusions In the …rst essay, we examine the impact of capital requirement on banks’ risk taking. We …nd some evidence that banks that are under regulatory pressure increase their capital or reduce risk to comply with the capital requirement. Because most of the statistical signi…cance of our results is low, our results are too weak to be general. Moreover, though banks that are under regulatory pressure tend to increase capital or reduce risk, di¤erent types of banks respond to the capital requirement di¤erently. Among undercapitalized banks, only large private-national banks that are under regulatory pressure that increase capital or reduce risk more than adequately capitalized banks. The second essay examines the impact of selling developing countries’bailed-out banks to strategic foreign investors on banks’ performance. We show that, after overcoming problems in treatment evaluation, we …nd that strategic sale of banks in developing country like Indonesia does lead to cost reduction. On average, depending on the speci…cation, strategic sale is associated with 10-20 percent lower costs. In the third essay we model how e¤ective capital regulation and audit policy are if regulator does not observe banks’capital and investment decision. We show that, if there is moral hazard problem or adverse selection problem, regulator have to audit 99 banks’balance sheet to enforce capital requirement. In the last essay, we examine the relationship between banks’type of ownership and banks’e¢ ciency. 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The MIT Press. 107 Appendix A Summary Statistics Capital Risk Weighted Assets CAR RegPCA RegProb Size Income Unit Observations Mean Standard Deviation Rp billion Rp billion 2,468 2,468 2,469 2,458 2,458 2,509 2,340 0.73 3.36 23.40 0.02 0.18 8.07 21.44 2.37 9.86 16.79 0.13 0.38 26.77 77.36 Rp billion Rp billion Table A.1: Key Variables Used in Chapter 108 Observations Mean Standard Deviation ln(Cost) 2,408 11.21 1.78 S ln(Loan) ln(Asset) ln(Deposit) ln(Price) 2,618 2,501 2,509 2,488 2,408 0.02 13.28 14.11 13.76 -3.96 0.14 1.88 1.78 1.86 0.78 Table A.2: Key Variables Used in Chapter All banks ln(Cost/W3) ln(Y1) ln(Y2) ln(Y3) ln(Y4) ln(W1/W3) ln(W2/W3) Observations Mean Standard Deviation 2,393 2,393 2,393 2,393 2,393 2,393 2,393 12.10 13.26 4.46 7.38 13.07 -3.88 -2.40 1.67 1.94 6.27 5.49 1.70 1.16 1.09 Table A.3: Key Variables Used in Chapter 5: All Banks 109 Observations Mean Standard Deviation State ln(Cost/W3) ln(Y1) ln(Y2) ln(Y3) ln(Y4) ln(W1/W3) ln(W2/W3) 90 90 90 90 90 90 90 15.41 16.86 11.40 15.16 16.16 -4.66 -2.48 2.55 1.09 5.95 5.28 1.22 1.81 1.23 Regional development ln(Cost/W3) ln(Y1) ln(Y2) ln(Y3) ln(Y4) ln(W1/W3) ln(W2/W3) 449 449 449 449 449 449 449 12.04 13.17 5.29 4.13 13.40 -3.58 -2.68 1.10 1.11 5.30 5.24 1.07 0.65 0.65 Table A.4: Key Variables Used in Chapter 5: State- and Regional Development Banks 110 Observations Mean Standard Deviation Large private ln(Cost/W3) ln(Y1) ln(Y2) ln(Y3) ln(Y4) ln(W1/W3) ln(W2/W3) 647 647 647 647 647 647 647 13.15 14.04 9.47 6.68 13.77 -3.86 -2.05 1.61 1.58 4.85 7.12 1.44 0.89 0.76 Small private ln(Cost/W3) ln(Y1) ln(Y2) ln(Y3) ln(Y4) ln(W1/W3) ln(W2/W3) 698 698 698 698 698 698 698 11.22 11.78 4.67 2.22 11.34 -3.16 -1.62 0.99 1.41 5.12 4.33 0.96 1.07 0.80 Table A.5: Key Variables Used in Chapter 5: Private National Banks 111 Observations Mean Standard Deviation Joint venture ln(Cost/W3) ln(Y1) ln(Y2) ln(Y3) ln(Y4) ln(W1/W3) ln(W2/W3) 331 331 331 331 331 331 331 11.09 13.64 8.97 1.81 13.26 -5.02 -3.50 1.02 1.30 4.05 4.18 0.95 0.82 0.76 Foreign ln(Cost/W3) ln(Y1) ln(Y2) ln(Y3) ln(Y4) ln(W1/W3) ln(W2/W3) 178 178 178 178 178 178 178 12.01 13.93 10.73 5.55 14.60 -5.03 -3.94 1.24 2.99 4.35 6.73 1.14 0.81 0.88 Table A.6: Key Variables Used in Chapter 5: Joint Venture and Foreign Banks Bank types State Large private national Small private national Regional development Joint venture Foreign Public Financial ratios ROA NPL NIM Observations Mean Standard Deviation 2,618 2,618 2,618 2,618 2,618 2,618 2,618 0.04 0.27 0.29 0.19 0.14 0.08 0.14 0.19 0.44 0.45 0.39 0.35 0.27 0.35 2,398 2,405 2,464 2.36 9.23 5.79 3.50 14.84 3.98 Table A.7: Summary Statistics of Other Variables [...]... failed-banks down Bogus accounting was the norm, and non-compliance was rarely penalized Besides, as some authors argue, Bank Indonesia then had yet to acquire experience and technical skills in banking regulation and supervision The turning point of bank regulation in Indonesia was the aftermath of the 1998 …nancial crisis Once again, Bank Indonesia forborne prudential regulation This time, however, many banks... in Indonesia On paper, capital requirement has been the backbone of Indonesia prudential s regulation since 1991 when Indonesia adopted the newly minted the Basel Accord The central bank, Bank Indonesia, which is also the regulator, requires banks to maintain capital at least eight percent of risk-weighted assets Along with other prudential regulation, regulator also imposes prompt corrective action... correction of covariance matrix derived by Windmeijer (2005) We also present the results of …xed e¤ect and OLS for basic regressions to see whether the coe¢ cients of lagged dependent variable of GMM estimators are too biased or not 2.5 Data We use the quarterly …nancial statement of Indonesian banking industry provided by the Bank Indonesia Department of Banking Statistics The dataset consists of s... restrictions in Column (1) does not reject the null hypothesis that our instruments are valid As we expect, the tests for serial correlation reject the null hypothesis of no …rst-order serial correlation of residuals of the …rst-di¤erenced equation, but do not reject the null hypothesis that there is no second-order serial correlation 2.6.2 Risk Equation Table 2.2 presents the risk equation: Regressions... di¤erence, Blundell and Bond (1998) further propose adding the lagged-di¤erences of endogenous and pre-determined variables as instruments For the same reasons above, we present the results of system GMM using two- and three lagged of endogenous variables and oneand two lagged of pre-determined variables only Because the two-step estimates of standard errors may be severely downward-biased, we use the …nite-sample... developing ones In most cases, it is imposed on all banks, 1 For an analysis of the recent East Asian …nancial crisis, see for example Radelet and Sachs (2002) 2 See Dewatripont and Tirole (1994) for an exposition of this accord Information on Basel Accord is available at the BIS’website, i.e http://www.bis.org/index.htm 2 not just the internationally active ones.3 Despite the convergence of bank regulation. .. complement the current empirical literature that primarily focused on banks in the developed countries Second, Indonesia experienced an arguably hasty liberalization in the late 1980s that leads to a sharp increase in the number of private banks without su¢ cient safeguard measures and regulation Thanks to a new central banking law recently enacted, Bank Indonesia the regulator of Indonesian banking. .. speed of adjustment— it measures how fast banks adjust their current capital and risk to the corresponding targets Researchers then derive regression models for observed changes in capital and risk from the capital equation, Equation (2.5), and risk equation, Equation (2.6) First, the banks’target capital Capitalit and risk Riskit are not observed, and have to be approximated Second, appealing to the theoretical... million on average, about 41 percent of the mean of all banks’capital The coe¢ cients of RegP ROB on the right panel, on the other hand, are close to zero and statistically insigni…cant The …rst set of estimates indicates that, to immediately comply with the eight percent capital requirement, an undercapitalized bank simply has to raise capital more than adequately capitalized banks The second set of. .. merged, and most others had 4 The PCA follows the 1991 US Federal Deposit Insurance Corporation Act 11 to recapitalize themselves to avoid closing More importantly, as a part of the IMF sponsored economic reforms, a new central banking law was enacted, and this law enabled Bank Indonesia to be a more independent central bank.5 Since then, Bank Indonesia has improved a number of prudential regulations, . ESSAYS ON BANKING REGULATION AND RESTRUCTURING: THE CASE OF INDONESIA RASYAD A. PARINDURI NATIONAL UNIVERSITY OF SINGAPORE. ESSAYS ON BANKING REGULATION AND RESTRUCTURING: THE CASE OF INDONESIA RASYAD A. PARINDURI (ST (EE)), ITB; (MA (Econ)), Michigan A THESIS SUBMITTED. increase in the number of private banks without su¢ cient safeguard measures and regulation. Thanks to a new central banking law recently enacted, Bank Indonesia the regulator of Indonesian banking