Determinants of banking crisis in developing countries

UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THE HAGUE VIETNAM THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS DETERMINANTS OF BANKING CRZSIS 4N DEVELOPING COUNTRIES A thesis submitted in partial fulfillment of the requirements for the degree of Master of Arts in Development Economics By Luirng Duy Quang ACKNOWLEDGEMENT To be able to complete this thesis, I have been received a great support from many people First of all, I would like to thank my supervisor, Dr Nguyen Van Phuc for his valuable guidance, comments, advice and encouragement during my completion of this thesis I would like to express my deep appreciation and sincere thanks to Assoc Prof Le Bao Lam for his valuable orientations in the first days of searching further education I am grateful to my all teachers and staffs of the Vietnam-Netherlands Program, particularly, Assoc Prof Nguyen Trong Hoai for his assistance during the first days I study in this program Many thanks are respectfully sent to my manager, Mr Nguyen Tu Han, and my colleagues for their encouragement and support during my writing thesis duration Finally, I am indebted to my family, especially my parent and others who give me great encouragement and support for my study -2 - CHAPTER V: WELL-KNOWN CRISES AND PREDICTION POWER OF THE MODEL LIST OF TABLES Table 1: Regression results of banking crisis determinants: the panel eliminating observations following end year of crisis .31 Table 2: Regression results of banking crisis determinants: the panel eliminating observations following first year of crisis - - - - - - - - - - 33 Table 3a: Average marginal effects of determinants case for those countries without jj pi35j jjj jjr;jjj y t jjq 37 Table 3b: Average marginal effects of determinants case for those countries with deposit insurance system 37 Table 4: Banking indicators before the crisis erupt (December 31,2001J -41 • LIST OF FIGURE Figure 1: Breakdown structure of liability side of Uruguay banking system before crisis erupt 41 Figure 2: Peso/US$ exchange rate and evolution of US$ reserves 43 Figure 3: Predicted probability of banking crisis in Uruguay and Thailand with specification 5a 44 -4- CHAPTER I: INTRODUCTION 1.1 Problem statement: The banking crises that erupted in US in 2007 are the latest in a series of such episodes that have been experienced by economies in various regions of the world in recent years In the 1990s, banking crises have occurred in Europe (the 1992—93 crises in the European Monetary System’s exchange rate mechanism), Latin America (the middle of 1990s), as well as in East Asia (the 1997—98 crises in Indonesia, Korea, Malaysia, the Philippines, and Thailand) These crises have been costly in varying degrees both in lost output and in the fiscal expense to rescue financial sectors Through financial system, their significant spillovers spreads internationally and in a number of situations international financial assistance have required to mitigate their severity, costs and spillovers to other countries In the wake of these recent crises, issue of what determinants affecting to occurrence of banking crisis has been a hot topic for economists in both developed and developing countries around the world Accessing this question is quite significant because it does not merely help authorities have confident base for their policies making process but it also is necessary to build up early warning system so that the crisis can be prevented beforehand However, understanding determinants of banking crisis is not an easy task Financial innovations and the increased integration of global financial markets are driving forces that make us harder to deal with this issue Such factors push financial system to evolve rapidly and generate new risks for financial system One critical thing is that development of financial market seems so complicated and exceeds our knowledge and prediction What we can as crisis comes is waiting for crisis wave and witnessing its effects on our life Thus, widely spreading influence of financial crisis, especially in banking sector, and its possible consequences obviously show important role of researches about determinants of banking crisis Even though this issue is not new, it always deserves to pursue -5 - This paper, thus, will focus on examining theoretical paths that lead to occurrence of banking crisis From that place, early warning system is developed to deal with crisis However, because of data limitation, the scope of study narrows in developing countries during period 1974-2002 1.2Research objectives: Some objectives of the thesis are to identify: (i) Determinants of banking crisis (ii) Threshold values of determinants which minimize banking crisis occurrence probability (iii) Suggest policies recommendation to prevent banking crisis 1.3 Research question: (i).What are determinants of banking crisis? (ii) How determinants affect on probability of banking crisis occurrence? (iii) How is marginal effect of each variable on probability of banking crisis evaluated? 1.4 Research hypothesis: (i) Key determinants such as real interest rate, deposit insurance, and exchange rate would have positive effects on probability of banking crisis (ii) Margial effect of each variable on probability of banking crisis is helpful to reduce risk of crisis 1.5 Structure of the thesis: The thesis includes chapters: Chapter 1: The introduction of the thesis Chapter 2: Literature review Fundamental components of banking crisis such as key definition, conceptual framework, theories and empirical studies will be discussed in this chapter Firstly, definition of banking crisis is clarified Then, theoretical part will review the relationship between dependent variable (banking crisis) and explanatory -6 - variables (financial variables, institutional variables, macro-economic variables) Empirical work is the last one mentioned in this chapter , Chapter 3: Model specification and data The top priority of this chapter is to develop necessary steps to obtain the best unbiased model for data analysis steps later on To fulfill this purpose, advantages and disadvantages of potential models is initially analyzed Next, strategy to obtain model specification from originally conceptual one will be presented Other important issues relating to data such as data filler process, data sources are also mentioned in this chapter Chapter 4: Regression result and testing hypothesis In chapter 4, second objective of this paper will be fulfilled The chapter begins with analytic steps to access the best unbiased model Then, hypotheses relating to sources of banking crisis are tested to provide basis of policy recommendation The last part of the chapter will investigate marginal effects of the most significant variables on probability of banking crisis From that place, policymaker could proceed to maximize effectiveness of macro-policies on , frequency of the crises Chapter 5: Well-known crises and prediction accuracy of model In the first part of chapter 5, two shrinking cases of banking crisis (Thailand and Uruguay) in Latin America and Asia will be reviewed For convenient purpose, events are arranged in chronology with two obvious parts: prior to the crisis and afterwards the crisis Then, prediction power of model will be discussed after analyzing results and information collected from prediction model and descriptive evidences of crisis Chapter 6: Conclusion and policy recommendation -7 - CHAPTER 2: LITERATURE REVIEW This chapter initially focuses on clarifying the definition of banking crisis Then, various theories of banking crisis are introduced in order to get hypotheses on how determinants affect on probability of banking crisis occurrence Finally, the chapter closes with empirical studies of banking crisis that help the readers understand how other researchers have approached their research questions, what dataset they have collected as well as which models and statistical methods they have used 2.1Key Definition: Banking crisis: Effects of banking crisis is always huge and costly to resolve Despite economies may experience different kinds of crisis, one thing ruled out is that if the collective effects of financial collapse is large enough, the government is forced to intervene Therefore, Ergungor and Thomson (2005), as cited by Caprio and Klingebiel (1996b), suggest that when central bankers think that a particular shock to the financial system could develop into systemic problem, and the monetary authorities begin to respond, banking system is considered as crisis In other words, banking crisis can be defined in terms of behaviors of central banks Kaminsky and Reinhart (1996) share this perspective in his study by clarifying two policies of central bank in crisis period Under this view, banking crisis links closely with two types of events (1) bank runs that lead to the closure, merging, or takeover by the public sector of one or more financial institutions (as in Venezuela in 1993); and (2) if there are no runs, the closure, merging, takeover, or large-scale government assistance of an important financial institution (or group of institutions) that marks the start of a string of similar outcomes for other financial institutions As discussed by Kaminsky and Reinhart (1996), this approach is not without drawbacks It could date the crises too late, because the financial problems usually begin well before a bank is finally closed or merged; it could also date the crises too early because the worst of crisis may come later Moreover, data o(banking crisis in -8 - terms of their approach is available at limited level Kaminsky and Reinhart (1996) just list system banking crisis of more than 20 countries This makes it harder for researchers to expand the scope of study Given data limitation, this paper, thus, follows definition of banking crisis developed by Caprio and Klingebiel (1996b) Accordance with his perspectives, banking crisis is the case in which the net worth of the banking system has been almost or entirely eliminated This creates something contradiction as banking problem in many nations is still to appear when a banking system has positive net worth However, Caprio and Klingebiel (1996b) indicate that the problem would be much easier if definition of banking crisis above linked with insolvency of banking system More obviously, that is, bad loans are strong enough to “blow out” system’s capital Based on data of banking sector in developing countries during 1980s, Caprio and Klingebiel (1996b) suggest that nonperforming loans must account for at least percent of total loans so that loan loss would be sufficient to wipe out banking system Under this definition, Caprio and Klingebiel (1996b) track more than 80 systemic banking crises around the world This dataset is updated through , various studies including Caprio and Klingebiel (1996c) and Honohan et a1 (2005) 2.2 Banking crisis theory: A number of studies have been developed around the world to provide insight view as well as explain logic behind crisis trouble in banking sector' One interesting thing is that there is no hope to find full theory in any study The theory is supplemented across study to study For that reason, in order to catch whole picture of banking crisis, it is quite useful to examine various studies This paper, thus, introduces banking crisis theory mainly referring to works of Ergungor and Thomson (2005) and Demirguc-Kunt and Detragrache (l998a) because they are the most updated and easily understandable version At the beginning, the theory starts -9 - , ' Eichengreen and Arteta (2000) list more than ten studies about systemic banking crisis, See Eichengreen and Arteta (2000) page 39 -9 - banking sector problem still receives top priority Although this technique can " provide empirical evidences for our hypothesis, prediction power of our model is likely to be reduced considerably, especially as handling crises originating from balance of payment crisis - 45 - CHAPTER 6: CONCLUSION AND POLICY RECOMMENDATION Since early 1980s, the need to identify determinants of banking crisis becomes more urgent than ever before The systemic banking sector problems emerged frequently around the world and its contagious effects always come up with budgetary burden, output loss as well as long-run depression Various studies about banking crisis show that despite the crisis in banking sector varies across the countries and overtime, some factors play an important role in explaining the logic behind many crises In this study, multivariate logit model is applied to identify these factors The author finds that banking crisis is more likely to erupt as macro-conditions are weak, especially when economy experience low growth GDP Our regression results also indicate that high rate of inflation increases the risk of banking sector problems As mentioned in theoretical section, inflation increases risk of crisis through nominal interest rate that makes bank system more difficult to serve its maturity transformation function Therefore, restrictive monetary policies are quite necessary in the context of inflationary economy One thing should pay more attention is that an inflation stability program is likely to cause volatile rise on real interest rate Empirical evidences previous chapter tells us that increased risk of banking sector could be result of high real interest rate Hence, inflation control program should be designed carefully, particular in balancing between effectiveness and a cost of possible banking crisis In our specifications, a sharp devaluation of exchange rate is also associated with banking volatility The risk of crisis due to a sharp depreciation of domestic currency is particularly high when bank‘s liability side dominated with foreign deposits In such case, exchange rate risks will double debt obligations’ banks with depositors and turn the banks to be insolvent One new findings in this paper is that implementing absolute value to evaluate contribution of exchange rate to risk of banking crisis is seems unsuitable Descriptive statistic evidences show that although - 46 - exchange rate risk is acknowledged as one of the key factors causing banking fragility in Thai banking system, almost influences of exchange rate on banking crisis are reflected less correctly in Thai case In other words, the model tends to ignore effect of exchange rate when absolute value of exchange rate are low and vice versa Unlike Demirguc-Kunt and Detragrache (l998a), our findings suggest that there is no empirical evidence to confirm real likelihood of explicit insurance and banking fragility As discussed above, scientific studies are inconsistent with each other to explain effect of deposit insurance scheme on probability of banking crisis Demirguc- Knut and Detragrache (l998a) find that crises are more likely in countries with a deposit insurance system take place while Hagen and Ho (2003) not recognize any persuasive evidence in their research This contradiction not only comes from implementing different approaches in their studies, but it also belongs quite much to what determinants they use For instance, Demirguc-Kunt and Detragrache (l998a) use dummy variable and quality of law enforcement to test effect of deposit insurance scheme, but Hagen and Ho (2003) use dummy variable only Moreover, experts also believe that deposit insurance variable may be insignificant as amount of bail-out package are quite low for each depositor Thus, under so many unreliable conditions it seems impossible to address likelihood of deposit insurance and banking fragility Further researches are required to have more confident results Another important determinant expected to have great contribution to volatility of banking sector is liberalization Theory of banking crisis has shown that effect of liberalization program is likely to spread banking sector through at least three channels including financial system opening, interest rate deregulation, and bank loan deregulation Its usual consequences are sharp rise of real interest rate and credit booming in countries where financial liberalization program introduced In our study, both real interest rate and credit booming are used to verify how closely financial liberalization program connects with probability banking crisis Even though there is a - 47 - strongly empirical evidence to confirm relationship between banking crisis and real interest rate, it can not conclude that financial liberalization program facilitate crisis in banking sectors as real interest rate could be a consequence of group of factors other than financial liberalization programs However, our regression results indicate that (not very strong) liberalizing financial system could generate more risks for credit market In that case, credit booming will deteriorate real sector, and afterwards, spread to banking sector Using absolute value of exchange rate found is unappropriate technique to access banking sector problem, particularly relating to prediction power of model The model tends to ignore crises that originating surge in exchange rate In other words, the prediction probability is high in countries which domestic currency is low to US dollar and vice versa Thus, implementing percent rate is obviously good alternative solution Surely, this is not great finding, but it is worth for noting in further researches There are some shortcomings existing in our study Firstly, our definition of banking crisis is probably not precise Hagen and Ho (2003) show that such definition could define the crisis too late because the crisis may star before it plagues banking system Secondly, our study also ignores some errors in timing crisis periods Lastly, exploiting annual data to explain banking crisis is obviously a limitation when evidences in Thailand and Uruguay during crisis periods indicate that crisis could be accessed in month This, thus, leaves a question about accurate time of the crisis Dataset of banking crisis episodes used in this study is mainly developed by Caprio and Klingebiel Even though most of banking crisis periods in this dataset receive support of many experts, this dataset can not - 48 - avoid shortcomings as the most useful rule for banking crisis is liquidity in banking system, not experts’ opinion - 48 - APPENDIX: Demirguq-Kunt et a1 ( Garcia (1999), International asso insurance (2008 Dummy variable for presence of explicit insurance, it takes when a full or partial insurance for depositors is introduced otherwise it Dummy Ambiguous Deposit equals Credit growth Ratio of credit to private sector to GDP Ratio GDP- IFS line 99B M2 is money + qu M2/Reser ve Realinterest M2 to money foreignIFS-line exchange 34+35, reserves come from IFS-line Id.d IFS-line Ratio reserves of central bank WDI 2007 Nominal interest rate minus inflation rate % National Ex Inflation TOT currency over US Change of exchange rate dollar Rate change of consume price index Change in terms of trade - 49 - IFS-line ae IFS- line 64.x Ratio WDI 2007 SAMPLE COMPOSITION N.o 10 11 12 13 14 15 16 17 18 , 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 Name of Countries Albania Bangladesh Barbados Belize Benin Bhutan Botswana Burkina Faso Burundi Cambodia Cameroon Cape Verde Central African Rep Chad Chile Colombia Congo, Republic of Costa Rica Cf›te d'Ivoire Cyprus Dominican Republic Egypt Equatorial Guinea Estonia Ethiopia Fiji Ghana Grenada Guatemala Guinea-Bissau Guyana Honduras in India Indonesia Israel Jordan Kazakhstan Kenya Kuwait Lao People's Dem.Rep N.o Name of Countries 41 42 43 44 45 46 47 Latvia Lesotho Libya Malawi Mali Mauritania Mauritius 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 Mexico Moldova Mongolia Morocco Mozambique Myanmar Niger Nigeria Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Rwanda Saudi Arabia Senegal Seychelles Sierra Leone Solomon Sri Lanka Sudan Swaziland Thailand Togo Trinidad and Tobago Tunisia Uganda Uruguay Venezuela, Rep Bol Yemen, Republic of Zambia AVERAGE MARGINAL EFFECTS OF DETERMINANTS ON PROBABILITY OF BANKING CRISIS OCCURRENCE 012 012 010 ôã 008 " 008 006 " 004 - 004 -120 -80 -40 40 -20 80 -10 10 20 30 GROWTH REAL_INTEREST_RATE • For countries without deposit insurance For countries with deposit insurance • 000105 For countries without deposit insurance • For countries with deposit insurance 012 010 - 000080 "• • 000070 ,, 4000 8000 12000 Exchange Rate For countries with deposit insurance For countries without deposit insurance 000 — -40 40 ‹ 80 120 160200 INFLATION For countries without deposit insurance For coutries with deposit insurance Macroeconomic variables and prediction probability of banking crises in Thailand and Uruguay Thailand crisis 1997 Prediction Probability and Exchange Rata Growth Rate and Prediction Inliati0n and Probability Real interest rate and prediction probability Probability Gr‹x#th rate +- Pr‹›bability iri4akon +- probability Reel interost rete +-prob8bIity 10 0.15 0.1 ’ 0.05 1996 1997 1998 1999 2000 2001 2002 -15 1896 1997 1998 1990 20J0 2DI1 2002 Years Uruguay Case (1996-2000) Ezcktrjsrzk zrJ PeJi‹tion Probability RetlhtzestRtk zsd Fedirtior Pob‹tilitj inReal irterat late +-PDl›abIty 0.1 -52- REFERENCE Allegret, J., P., Courbis, B., & Dulbecco, Ph., 2003, Financial Liberalization and - 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