UNIVERSITY OF ECONOMIC INSTITUDE OF SOCIAL STUDIES HOCHIMINH CITY THE HAGUE VIETNAM THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS DETERMINANTS OF NONPERFORMING LOANS THE CASE OF VIETNAMESE BANKING SECTOR A thesis submitted in partial fulfillment of the requirements for degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By TRUONG NGOC THANH Academic Supervisor DR NGUYEN THI THUY LINH HO CHI MINH CITY, DECEMBER 2016 Determinants of nonperforming loans – The case of Vietnamese banking sector ABSTRACT The main purpose of this study is to examine the determinants of non-performing loans (NPLs) in the case of Vietnamese banking sector by analyzing the unbalanced panel data of 30 Vietnamese banks over the period of 2008 – 2012 Both of macroeconomic and bank-specific determinants are employed when modeling the regression of NPLs’ determinants Macroeconomic factors including Gross Domestic Product (GDP) growth rate, unemployment rate, real lending interest rate and sovereign debt are exogenous variables that effect on NPLs Besides that, the study examine the bank-specific determinants by analyzing relevant hypothesis such as ‘bad management’, ‘pro-cyclical credit policy’, ‘skimping’, ‘diversification’, ‘too big to fail’, ‘moral hazard’ hypothesis According these hypotheses, return on equity, inefficiency rate, proportion of non-interest income and leverage ratio are the endogenous variables which effect to NPLs In addition, credit growth rate is added into model to examine its effect on NPLs Moreover, the effects of government intervention and foreign investment on NPLs are also examined in this study by investigating the difference in NPLs of state-owned banks and fully foreign-owned banks The fixed effect of unbalance panel data is employed to test these hypotheses Regarding bank-specific factors, the inefficiency rate and credit growth rate statistically affect on NPLs However, return on equity, non-interest income rate, leverage ratio not statistically significant effect on NPLs According to regression result, it shows the negative and significant relationship between the inefficiency rate and NPLs that is consistent with ‘skimping’ hypothesis Moreover, the relationship between credit growth and NPLs is significant and negative As the regression result, all of macroeconomic determinants including GDP growth rate, unemployment rate, real lending interest rate and sovereign debt statistically significant affect on NPLs The regression shows the positive and significant relationship between the sovereign debt and NPLs which is consistent with hypothesis The increase in sovereign debt will reduce payment ability that increases the future NPLs However, the regression shows the positive relationship between GDP growth rate and NPLs and negative relationships between the unemployment rate, lending interest rate and NPLs that is not consistent with hypothesis Truong Ngoc Thanh – Class 19 Determinants of nonperforming loans – The case of Vietnamese banking sector Regarding the government intervention, the regression shows that return on equity and leverage ratio are affected in state-owned bank that lead to higher NPLs However, the effect of foreign investment in fully foreign-owned banks on NPLs is not supported in this study There are some policy implications based on the regression results Firstly, the sovereign debt should be strictly control in order to enhance the payment ability of debtors Secondly, the underwriting and monitoring loans process should be controlled to reduce NPLs expansion at bank level Finally, the operations of state-owned banks should be controlled to reduce NPLs expansion in state-owned banks Truong Ngoc Thanh – Class 19 Page ii Determinants of nonperforming loans – The case of Vietnamese banking sector TABLE OF CONTENT CHAPTER 1: INTRODUCTION 1.1 Overview of Vietnamese banking sector and non-performing loans 1.2 Research problem 1.3 Research objectives and research question CHAPTER 2: LITERATURE REVIEW 2.1 Non-performing loans definition 2.2 Bank-specific determinants of non-performing loans 2.3 Macroeconomic determinants of non-performing loans 12 2.4 Government intervention and foreign investment in banking system 16 CHAPTER 3: METHODOLOGY AND DATA 19 3.1 Methodology 19 3.2 Data 21 3.3 Estimation approach 23 CHAPTER 4: ANALYSIS RESULTS 25 4.1 Descriptive statistics 25 4.2 Economic results 27 4.3 Result discussion 30 CHAPTER 5: CONCLUSION 35 5.1 Main findings and policy implication 35 5.2 Limitation of the study 36 REFERENCES 38 APPENDIX 41 Truong Ngoc Thanh – Class 19 Page i Determinants of nonperforming loans – The case of Vietnamese banking sector LIST OF TABLE Table 1: Definition of variables used in modeling NPLs determinants 17 Table 2: Specific calculation of variables 22 Table 3: Methodology test 24 Table 4: Descriptive statistics 25 Table 5: The correlation matrix 26 Table 6: Summarize NPLs 27 Table 7: The regression result 28 Table 8: Regression result of dummy variables 29 Table 9: Empirical evidence for tested hypothesis 34 Truong Ngoc Thanh – Class 19 Page ii Determinants of nonperforming loans – The case of Vietnamese banking sector CHAPTER 1: INTRODUCTION 1.1 Overview of Vietnamese banking sector and non-performing loans There are three types of ownership in Vietnamese banking sector including state-owned commercial banks, joint stock commercial banks, foreign banks (Kalra, 2012) State-owned commercial banks play an important responsibility in international financial by lending to main sectors in Vietnamese economy In particular, loans of trade and industry sectors central is granted by Bank for Industry and Trade (ViettinBank) while foreign payments is in-charged by Bank for Foreign Trade (VietcomBank) In additional, loans of agriculture and fishing are supported by Bank for Agricultural Development (AgriBank) Concerning the bank market share, state-owned commercial bank account for large bank market share in 2010 (Kalra, 2012) Besides that, the growth of joint stock commercial banks also contributes in the banking sectors throughout their financial services In Vietnam, banking sector is under the control of government throughout the State bank operations Besides the financial responsibility, some duties of state-owned bank are expected In particular, loans of main sectors in the economy are financed by state-owned commercial banks In addition, money supply and demand are controlled by state bank by opening the market operation, reserve system, bank rate policy Moreover, all regulation as well as guideline of banking operations must be complied with state bank’s regulation The Vietnamese banking system is significantly impacted by the economic depression over the period of 2008 – 2012 which leads to NPLs expansion The main cause of bank problem is the deterioration of loan portfolio As the same situation with international banking system, Vietnam experienced with a period of the housing bubble and rapid growth in the stock market Allowing easy access to loans and rapid credit growth, Vietnamese banking sector had to face with the credit exposure when economy went down According to report of State Vietnamese Bank, the loan portfolio significant increased from 2005 to 2007 Specially, the credit growth rate was 52.42% in 2007 that doubly increases comparing with this in 2006 In addition, high unemployment rate in period of economic downturn strongly impact to the payment debt ability Moreover, the weakness of Vietnamese banking sector is one cause that expand the problem loans Excessive loans, loose credit policy assessment, less mortgage loans, lose control in loan monitoring are the problems of Vietnamese banking sectors Truong Ngoc Thanh – Class 19 Page Determinants of nonperforming loans – The case of Vietnamese banking sector As the consequence, the NPLs rate was 3.4% in 2012 which doubly increases comparing with this in 2009 Many reactions were implemented by State bank of Vietnam to solve the bank’s NPLs The number of policies was implemented including increasing capital adequacy ratio to 9%, increasing restriction for lending credit, establishing Vietnam asset management company (VAMC), buying NPLs of weak banks, restructuring weak banks, issuing new loan classification, etc In addition, minimum of charter capital of banking sector was increased Interest rate ceilings were re-imposed to control operation of banking sector as well stable the economy However, the NPLs rate was not significantly improved According the World Bank’s report, the NPLs declined to 3.107% by the end of 2013 because of transferring bad loans to the VAMC However, the NPLs in 2013 also emphasizes that this rate could be 9% if all restructured loans were included (Mellor, Minh, & Thuc, 2014) In the other sides, according to rating agency Moody’s estimation, NPL could be higher and exceed 15% in the case of implement international standard assessment The concern of NPLs was raised in Vietnamese banking sectors in recent years In addition, the root cause of NPLs of bank’s sector was examined to find out best measure for NPLs solving Therefore, the main purpose of this research is to examine the determinants of NPLs in the case of Vietnamese banking sector in order to find out the appropriate policy implication for solving banking NPLs 1.2 Research problem Reviewing empirical studies, there are many approaches to examine the determinants of NPLs On the one hand, macroeconomic factors could be employed to evaluate their effect on NPLs Berge and Boye (2007) conclude that real interest rate and unemployment are highly sensitive with the problem loans They find out that one of primary contribution in real interest rate and unemployment rate improvement is the problem loans’ declining (Berge & Boye, 2007) Besides that, according to study of Reinhart and Rogoff (2011), they made conclusion that NPLs could be considered as the one root cause of banking crisis According International Monetary Fund working paper, basing on the NPLs in Central, Eastern and South Eastern Europe, the research indicates that strong feedback of macroeconomic condition including GDP growth, unemployment and inflation on NPLs (Klein, 2013) The econometric result suggests GDP growth is one of the macro explanatory of NPLs Besides that, the significant linkage between macroeconomic condition and NPLs is also supported by the Truong Ngoc Thanh – Class 19 Page Determinants of nonperforming loans – The case of Vietnamese banking sector investigation of determinant of NPLs of 85 banks in three countries including Italy, Greece and Spain (Messai & Jouini, 2013) However, this approach does not consider the effect of banking specific variables that illustrate the characteristic of each bank, which generates different effect on the risk exposure at the bank level On the other hand, some empirical studies attempt to find out the linkage between bank-specific variables and NPLs including bank capitalization, bank profitability, bank regulation, etc This approach is more powerful in explanation of difference of banking NPLs For instance, using the aggregate banking data from 59 countries, internal factor including the capital adequacy ratio, prudent provisioning policy, private or foreign ownership, strengthening the legal system have significant impact on banks’ NPLs (Boudriga, Taktak, & Jellouli, 2009) Moreover, the insolvency of financial institution is also the result of high NPLs (Farhan, Sattar, Chaudhry, & Khalil, 2012) In addition, other study attempts to find out impact of ownership status or market power on NPLs It generally accepted that NPLs associated with the inefficiency, failures of the banks in the financial crisis period (Ahmad & Bashir, 2013) Other approach to examine NPLs’ determinant is analyzing the effect of both macroeconomic and bank-specific factors on NPLs In particular, the macroeconomic and microeconomic factors are combined to examine the NPLs of commercial and saving bank in Spain It concludes that all macroeconomic and microeconomic factors have specific effect on NPLs (Salas & Saurina, 2002) Using the data of Greek banking system, the empirical study combines both macroeconomic and bankspecific factors to assess NPLs’ determinant This study finds out that bank-specific factors have a different impact on NPLs of different loan categories including mortgage, business and consumer loan portfolios (Louzis, Vouldis, & Metaxas, 2011) Government intervention and foreign investment are also considered as the endogenous variables that affect to NPLs Some arguments show that government intervention play important role to manage economic in which market failure are balanced (Garcıa-Marco & Robles-Fernandez, 2008) Other arguments supported for private-sector monitoring hypothesis Regarding foreign investment, it is general accepted that bank will get advantages from experience of management as well as capital from foreign investment However, its effect varies in different studies In summary, the financial problem raise more concern in the NPLs in recent years The determinants of NPLs are examined in many empirical studies However, the determinants of NPLs in the case of Truong Ngoc Thanh – Class 19 Page Determinants of nonperforming loans – The case of Vietnamese banking sector Vietnamese banking sector are not examined Therefore, this study will examine the NPLs’ determinants in the case of Vietnamese banking sector 1.3 Research objectives and research question 1.3.1 Research objectives As discussion above, the main purpose of this study is to examine the determinants of NPLs The unbalanced panel data of 30 Vietnamese banks over the period of 2008-2012 is used in this study Both macroeconomic and bank-specific factors are employed in order to model the NPLs’ determinant In particular, this study will examine the effect of exogenous variables including GDP growth, unemployment rate, lending interest rate and sovereign debt on NPLs The endogenous variables including return on equity, inefficiency rate, non-interest rate, leverage ratio and credit growth are also examined In addition, the effect of government intervention and foreign investment on NPLs is investigated by assessing the difference of NPLs in state-owned bank and fully foreignowned bank In finally, the policy implication for NPLs solving is suggested after examining the regression results 1.3.2 Research questions According to the research objectives, this study will attempt to answer following research questions The first question is which factors will affect on the NPLs The second question is how they affect on NPLs The third question is what the cause of these effect And the final question is which policy applicant could be raise from analyzing the effect of these factors The rest of study will be arranged as follows Chapter briefly presents the theories and empirical studies regarding NPLs’ determinant In this part, specific influence of each factor on NPLs will be analyzed basing analyzing the result of previous studies Chapter will provide methodology analysis of previous empirical literature This part will give overview of all methodologies were applied in previous study and suitable mythology will be selected to analyze NPLs’ determinants in Vietnamese banking sector Detailed data and data sources are also presented in this part Next chapter will present the analysis results The descriptive statistic as well as economic results is provided in this part This Truong Ngoc Thanh – Class 19 Page Determinants of nonperforming loans – The case of Vietnamese banking sector part also provides regression explanation and comparison with expectation of literature review The conclusion as well as policy implication will be presented in final chapter Chapter also provides research limitation as well as guideline for future studies Truong Ngoc Thanh – Class 19 Page Determinants of nonperforming loans – The case of Vietnamese banking sector APPENDIX APPENDIX 1: DETAILED DATASET OF VIETNAMESE BANKS SHORT NAME FULL NAME STATE-OWNED BANKS VIETINBANK Vietnam Joint-Stock Commercial Bank for Industry and Trade BIDV Bank for Investment and Development of Vietnam VIETCOMBANK Joint Stock Commercial Bank for Foreign Trade of Vietnam MHB Housing Bank of Mekong Delta FULLY FOREIGN-OWNED BANKS HSBC HSBC Bank (Vietnam) Ltd ANZ ANZ Bank (Vietnam) Limited STANDARD Standard Chartered Bank (Vietnam) Ltd INDOVINA Indovina Bank Ltd VID VID Public Bank HONGLEONG Hong Leong Bank Vietnam Limited COOPRATE GROUP OWNED BANKS TECHCOMBANK Vietnam Technological and Commercial Joint-Stock Bank MBANK Military Commercial Joint Stock Bank EXIMBANK Vietnam Export Import Commercial Joint Stock Bank ACB Asia Commercial Joint-stock Bank SAIGONBANK Saigon Commercial Bank SHB Saigon - Hanoi Commercial Joint Stock Bank VPBANK Vietnam Prosperity Joint Stock Commercial Bank MARITIME Vietnam Maritime Commercial Stock Bank LIENVIETBANK Lien Viet Post Joint Stock Commercial Bank VIBANK VietNam International Commercial Joint Stock Bank SEABANK Southeast Asia Commercial Joint Stock Bank DONGA DongA Commercial Joint Stock Bank OCEANBANK Ocean Commercial Joint Stock Bank ABBANK An Binh Commercial Joint Stock Bank TIENPHONG Tien Phong Commercial Joint Stock Bank PHUONGDONG Orient Commercial Joint Stock Bank PGBANK Petrolimex Group Commercial Joint Stock Bank NAMVIET Nam Viet Commercial Joint Stock Bank MEKONG Mekong Development Joint Stock Commercial Bank VINASIAM VinaSiam Bank Truong Ngoc Thanh – Class 19 Page 41 Determinants of nonperforming loans – The case of Vietnamese banking sector APPEDIX 2: DETAILED MEASURES OF VARIABLES Variables GDP Specific calculation GDP growth rate Unit % Explanation The ratio is used to measure the growth rate of the economy GDP = Private consumption + Gross investment + Government investment + Government spending + (Exports – Imports) UN Unemployment rate % This ratio measure the rate of unemployed individuals in labor force This ratio is calculated by diving number of unemployees by total employee in labor force RLR Real lending interest rate % The amount is charged to borrower for the use of assets The real lending interest rate is calculated by adjusting inflation from nominal lending interest rate DEBT Central Government debts/ nominal GDP % The amount of money that is borrowed by one country’s government ROE Profit/ Total equity % This ratio measures the operating efficiency of one company  Profit: total profit after tax of one bank  Total equity: a stock or other security representing an ownership interest INEF Operating expense/ operating income NII Noninterest income/ Total income Truong Ngoc Thanh – Class 19 The ratio measure the cost inefficient of operations activity, in which:  Operating expense: expense in operations excluded interest expense  Operating income: earnings before interest and tax % The ratio measure the percentage of noninterest income over the total of gross revenue Page 42 Determinants of nonperforming loans – The case of Vietnamese banking sector Variables Specific calculation LR Total liability/ total assets CRE (Loant - Loant-1)/ Loant-1 Truong Ngoc Thanh – Class 19 Unit Explanation  Noninterest income: the income of other activities excepting lending activities  Total income: income of banks including interest income and noninterest income This ratio measure the ability to finance obligation of company  Total liability: total payables in balance sheet of bank including short-term and long-term payables  Total assets: total assets in balance sheet of bank including short-term and long-term assets % This ratio measures the growth rate of loan portfolio of a bank Page 43 Determinants of nonperforming loans – The case of Vietnamese banking sector APPEDIX 3: CORRELATION BETWEEN NPLS AND BANK-SPECIFIC VARIABLES Variables Correlations Trend Hypothesis Negative Bad management II (-) Pro-cyclical credit policy (+) Positive Bad management (+) Skimping (-) Positive Diversification (-) ROE 10 15 Relationship in reality -10 10 ROE NPLs 20 30 INEF 10 15 Fitted values -10 10 20 INEF Fitted values NII 10 15 NPLs -50 50 100 NII NPLs Truong Ngoc Thanh – Class 19 Fitted values Page 44 Determinants of nonperforming loans – The case of Vietnamese banking sector Variables Correlations Trend Hypothesis Negative Moral hazard (+) Too big to fail (+) Negative Credit growth (-) LR 10 15 Relationship in reality LR Fitted values CRE 10 15 NPLs 100 NPLs Truong Ngoc Thanh – Class 19 200 CRE 300 400 Fitted values Page 45 Determinants of nonperforming loans – The case of Vietnamese banking sector APPEDIX 4: VARIABLES TEST Mutli-collinearity Multi-co linearity is the problem of model in which there is one or more relationship among the regressors As the consequence of co linearity, the model exist the large confidence interval in which the null hypothesis may not be rejected In addition, the t ratio may be statistically insignificant To test the collinearity, the variance-inflating factor (VIF) is used to measure the degree of estimators inflated by co linearity VIF is calculated as following calculation 𝑽𝑰𝑭 = 𝟏 𝟏 − 𝒓𝟐 Where r2 denotes the coefficient of correlation among variables If the VIF is lower than 5, it is conclude that there are less co linearity among variables Following is the result: Variables VIF 1/VIF RLRt-1 UN t-1 ROE t-1 LR t-1 CRE t-1 GDP t-1 INEF t-1 NII t-1 NPLs t-1 Mean VIF 5.47 4.14 2.31 1.83 1.83 1.75 1.69 1.53 1.36 2.43 0.183 0.242 0.432 0.545 0.547 0.570 0.592 0.655 0.736 According to the result, it is recognized that all VIF indicators are lower than It is concluded that there is no co linearity among variables Unit root test If the means and variance of time series is constant over the time, this time series is said to be stationary In addition, the covariance between two time periods depends only on the distance between the two time periods If time series is non-stationary, the model result is less practical value for forecasting purpose In the case of non-stationary time series, the model will obtain high value of R2 and significant t However, the result is unreliable To test the stationary for times series, the unit root test is applied In the unit root test, null hypothesis is said that there is unit root in model The stationary Truong Ngoc Thanh – Class 19 Page 46 Determinants of nonperforming loans – The case of Vietnamese banking sector time series is the alternative hypothesis This study will apply the Dickey-Fuller test to check stationary of model The simple model for unit root test is presented as follows yt = ρ yt-1 + ut where y denotes for variable, t denotes for time index A unit root present in the model if ρ equal in which the variable in time t is correlate with variable in time t-1 Following is the results Inverse chi-squared(50) Inverse normal Inverse logit t(104) Modified inv chi-squared P Z L* Pm NPLs ROE NII LR CRE 125.31*** -1.13 -4.22*** 7.53*** 231.54*** -3.87*** -9.04*** 15.66*** 121.982*** -0.6734 -2.185*** 5.6582*** 406.515*** -8.4266*** -18.31*** 31.6324*** 31.6145 0.8532 0.7715 -2.154 According to the result, it is recognized that all variables are stationary, except credit growth Truong Ngoc Thanh – Class 19 Page 47 Determinants of nonperforming loans – The case of Vietnamese banking sector APPEDIX 5: STATA OUTPUT I Stata output without dummy variables:  Model 1: NPLs it = NPLs it-1 + β1 GDP it-1 + β2 UNit-1 + β3 RLR it-1 + εit Fixed-effects (within) regression Group variable: code Number of obs Number of groups = = 89 26 R-sq: Obs per group: = avg = max = 3.4 within = 0.2854 between = 0.1273 overall = 0.2082 corr(u_i, Xb) F(4,25) Prob > F = 0.0661 = = 11.70 0.0000 (Std Err adjusted for 26 clusters in code) Robust Std Err NPLs Coef lagNPLs lagGDP lagUN lagRLR _cons 1086993 1.451294 -4.468976 -.352617 9.251273 0644627 3653296 1.230543 1344058 3.747368 sigma_u sigma_e rho 1.1459597 1.4751341 37636334 (fraction of variance due to u_i) Truong Ngoc Thanh – Class 19 t 1.69 3.97 -3.63 -2.62 2.47 P>|t| 0.104 0.001 0.001 0.015 0.021 [95% Conf Interval] -.024064 6988837 -7.003327 -.6294309 1.533424 2414626 2.203704 -1.934625 -.0758031 16.96912 Page 48 Determinants of nonperforming loans – The case of Vietnamese banking sector  Model 2: NPLs it = NPLs it-1 + β1 GDP it-1 + β2 UNit-1 + β3 RLR it-1 + β5 ROEit-1 + β6 INEFit-1 + β7 NIIit-1 + β8 LRit-1 + β9 CREit-1 + εit Fixed-effects (within) regression Group variable: code Number of obs Number of groups = = 81 25 R-sq: Obs per group: = avg = max = 3.2 within = 0.3748 between = 0.0089 overall = 0.1768 corr(u_i, Xb) F(9,24) Prob > F = -0.2616 = = 6.63 0.0001 (Std Err adjusted for 25 clusters in code) Robust Std Err NPLs Coef lagNPLs lagGDP lagUN lagRLR lagROE lagINEF lagNII lagLR lagCRE _cons 1349426 1.515015 -4.592409 -.4849662 -.0312307 -.6878815 -.020221 -7.14046 -.0155977 19.40637 1150342 4810067 1.599229 2161728 0896632 3127668 0176691 5.931285 0084761 7.789087 sigma_u sigma_e rho 1.3984086 1.5039032 46369947 (fraction of variance due to u_i) t 1.17 3.15 -2.87 -2.24 -0.35 -2.20 -1.14 -1.20 -1.84 2.49 P>|t| 0.252 0.004 0.008 0.034 0.731 0.038 0.264 0.240 0.078 0.020 [95% Conf Interval] -.1024764 5222663 -7.893055 -.931125 -.2162863 -1.3334 -.0566882 -19.38203 -.0330916 3.330486 3723617 2.507765 -1.291764 -.0388075 153825 -.0423625 0162463 5.101112 0018962 35.48226 Truong Ngoc Thanh – Class 19 Page 49 Determinants of nonperforming loans – The case of Vietnamese banking sector  Model 3: NPLs it = NPLs it-1 + β1 GDP it-1 + β3 RLR it-1 + β4 Debtit-1 + β5 ROEit-1 + β6 INEFit-1 + β7 NIIit-1 + β8 LRit-1 + β9 CREit-1 + εit (Eq 3) Fixed-effects (within) regression Group variable: code Number of obs Number of groups = = 81 25 R-sq: Obs per group: = avg = max = 3.2 within = 0.3748 between = 0.0089 overall = 0.1768 corr(u_i, Xb) F(9,24) Prob > F = -0.2616 = = 6.63 0.0001 (Std Err adjusted for 25 clusters in code) Robust Std Err NPLs Coef lagNPLs lagGDP lagRLR lagDEBT lagROE lagINEF lagNII lagLR lagCRE _cons 1349426 4961359 -.5150033 3.839344 -.0312307 -.6878815 -.020221 -7.14046 -.0155977 6.369162 1150342 3307783 225701 1.336986 0896632 3127668 0176691 5.931285 0084761 4.601934 sigma_u sigma_e rho 1.3984086 1.5039032 46369947 (fraction of variance due to u_i) Truong Ngoc Thanh – Class 19 t 1.17 1.50 -2.28 2.87 -0.35 -2.20 -1.14 -1.20 -1.84 1.38 P>|t| 0.252 0.147 0.032 0.008 0.731 0.038 0.264 0.240 0.078 0.179 [95% Conf Interval] -.1024764 -.1865571 -.9808274 1.07994 -.2162863 -1.3334 -.0566882 -19.38203 -.0330916 -3.128762 3723617 1.178829 -.0491793 6.598748 153825 -.0423625 0162463 5.101112 0018962 15.86709 Page 50 Determinants of nonperforming loans – The case of Vietnamese banking sector II Stata output with dummy variables:  Model 1: NPLs it = NPLs it-1 + β1 GDP it-1 + β2 UNit-1 + β3 RLR it-1 + β5 ROEit-1 + β6 INEFit-1 + β7 NIIit-1 + β8 LRit-1 + β9 CREit-1 + d_STATE_ROE + d _FOREIGN_ROE + εit Fixed-effects (within) regression Group variable: code Number of obs Number of groups = = 79 24 R-sq: Obs per group: = avg = max = 3.3 within = 0.4061 between = 0.0236 overall = 0.0703 corr(u_i, Xb) F(11,23) Prob > F = -0.6638 = = 8.56 0.0000 (Std Err adjusted for 24 clusters in code) Robust Std Err NPLs Coef lagNPLs lagGDP lagUN lagRLR lagROE lagINEF lagNII lagLR lagCRE dSTATE_ROE dFOREIGN_ROE _cons 3551726 2.079084 -5.717281 -.5920979 -.0222337 -.6135166 -.0061147 -8.070856 -.0140524 157449 2301842 19.41781 4818849 9116928 2.291945 3246121 0754567 3169363 0236951 5.845925 0080972 0775755 2566682 7.165306 sigma_u sigma_e rho 1.8988357 1.5123848 61185261 (fraction of variance due to u_i) t 0.74 2.28 -2.49 -1.82 -0.29 -1.94 -0.26 -1.38 -1.74 2.03 0.90 2.71 P>|t| 0.469 0.032 0.020 0.081 0.771 0.065 0.799 0.181 0.096 0.054 0.379 0.012 [95% Conf Interval] -.6416822 1931039 -10.45853 -1.263609 -.1783278 -1.269149 -.0551317 -20.16407 -.0308028 -.0030281 -.3007745 4.595244 1.352028 3.965064 -.9760309 0794133 1338605 042116 0429023 4.022361 002698 3179262 761143 34.24037 Truong Ngoc Thanh – Class 19 Page 51 Determinants of nonperforming loans – The case of Vietnamese banking sector  Model 2: NPLs it = NPLs it-1 + β1 GDP it-1 + β2 UNit-1 + β3 RLR it-1 + β5 ROEit-1 + β6 INEFit-1 + β7 NIIit1 + β8 LRit-1 + β9 CREit-1 + d_STATE_INEF + d _FOREIGN_INEF + εit Fixed-effects (within) regression Group variable: code Number of obs Number of groups = = 80 25 R-sq: Obs per group: = avg = max = 3.2 within = 0.3890 between = 0.0120 overall = 0.1779 corr(u_i, Xb) F(11,24) Prob > F = -0.4625 = = 11.40 0.0000 (Std Err adjusted for 25 clusters in code) Robust Std Err NPLs Coef t lagNPLs lagGDP lagUN lagRLR lagROE lagINEF lagNII lagLR lagCRE dSTATE_INEF dFOREIGN_INEF _cons 3835915 1.871159 -5.130201 -.5587694 -.023885 -.6021107 -.0109139 -6.881422 -.0144898 -1.430405 -.9683407 18.77474 4516905 8564967 2.105027 3090196 0786054 3040793 0232848 6.232468 0085487 1.510174 1.32196 7.460987 sigma_u sigma_e rho 1.5105169 1.5339816 49229321 (fraction of variance due to u_i) 0.85 2.18 -2.44 -1.81 -0.30 -1.98 -0.47 -1.10 -1.69 -0.95 -0.73 2.52 P>|t| 0.404 0.039 0.023 0.083 0.764 0.059 0.644 0.280 0.103 0.353 0.471 0.019 [95% Conf Interval] -.5486519 1034368 -9.474763 -1.196554 -.1861185 -1.2297 -.0589712 -19.7446 -.0321333 -4.547252 -3.696732 3.37602 1.315835 3.638881 -.7856395 0790157 1383485 0254782 0371435 5.981761 0031538 1.686441 1.760051 34.17346 Truong Ngoc Thanh – Class 19 Page 52 Determinants of nonperforming loans – The case of Vietnamese banking sector  Model 3: NPLs it = NPLs it-1 + β1 GDP it-1 + β2 UNit-1 + β3 RLR it-1 + β5 ROEit-1 + β6 INEFit-1 + β7 NIIit1 + β8 LRit-1 + β9 CREit-1 + d_STATE_NII + d _FOREIGN_NII + εit Fixed-effects (within) regression Group variable: code Number of obs Number of groups = = 78 24 R-sq: Obs per group: = avg = max = 3.3 within = 0.3971 between = 0.0000 overall = 0.0895 corr(u_i, Xb) F(11,23) Prob > F = -0.7215 = = 2.69 0.0218 (Std Err adjusted for 24 clusters in code) Robust Std Err NPLs Coef lagNPLs lagGDP lagUN lagRLR lagROE lagINEF lagNII lagLR lagCRE dSTATE_NII dFOREIGN_NII _cons 5182009 2.310253 -5.781021 -.6525874 -.0077769 -.4991951 -.0004769 -7.522818 -.0154476 1168827 1201724 17.81391 5083043 1.187331 2.647771 3694029 0812527 3081074 0274593 5.892684 0088093 0972024 1699602 7.226662 sigma_u sigma_e rho 1.9884471 1.535197 62653738 (fraction of variance due to u_i) t 1.02 1.95 -2.18 -1.77 -0.10 -1.62 -0.02 -1.28 -1.75 1.20 0.71 2.47 P>|t| 0.319 0.064 0.039 0.091 0.925 0.119 0.986 0.214 0.093 0.241 0.487 0.022 [95% Conf Interval] -.5333067 -.1459289 -11.25835 -1.416756 -.1758609 -1.136564 -.0572807 -19.71276 -.033671 -.0841957 -.2314171 2.864425 1.569708 4.766436 -.3036895 1115808 1603071 1381736 0563269 4.667127 0027757 3179611 4717618 32.7634 Truong Ngoc Thanh – Class 19 Page 53 Determinants of nonperforming loans – The case of Vietnamese banking sector  Model 4: NPLs it = NPLs it-1 + β1 GDP it-1 + β2 UNit-1 + β3 RLR it-1 + β5 ROEit-1 + β6 INEFit-1 + β7 NIIit1 + β8 LRit-1 + β9 CREit-1 + d_STATE_LR + d _FOREIGN_LR + εit Fixed-effects (within) regression Group variable: code Number of obs Number of groups = = 81 25 R-sq: Obs per group: = avg = max = 3.2 within = 0.3866 between = 0.0211 overall = 0.0062 corr(u_i, Xb) F(11,24) Prob > F = -0.9959 = = 7.33 0.0000 (Std Err adjusted for 25 clusters in code) Robust Std Err NPLs Coef t lagNPLs lagGDP lagUN lagRLR lagROE lagINEF lagNII lagLR lagCRE dSTATE_LR dFOREIGN_LR _cons 0953354 1.720435 -5.10142 -.5206405 -.0314687 -.691497 -.014908 -8.065013 -.0152237 48.30827 -8.190775 14.64608 1181527 6059584 1.977607 2574429 0991578 3702449 0173443 5.736121 0084218 27.54773 26.7358 8.529083 sigma_u sigma_e rho 15.216305 1.5223916 9900892 (fraction of variance due to u_i) 0.81 2.84 -2.58 -2.02 -0.32 -1.87 -0.86 -1.41 -1.81 1.75 -0.31 1.72 P>|t| 0.428 0.009 0.016 0.054 0.754 0.074 0.399 0.173 0.083 0.092 0.762 0.099 [95% Conf Interval] -.1485197 4697985 -9.182999 -1.051977 -.2361203 -1.455645 -.0507048 -19.90378 -.0326054 -8.547455 -63.37075 -2.957085 3391905 2.971072 -1.01984 0106956 1731829 0726509 0208888 3.773758 0021581 105.164 46.9892 32.24924 Truong Ngoc Thanh – Class 19 Page 54 Determinants of nonperforming loans – The case of Vietnamese banking sector  Model 5: NPLs it = NPLs it-1 + β1 GDP it-1 + β2 UNit-1 + β3 RLR it-1 + β5 ROEit-1 + β6 INEFit-1 + β7 NIIit-1 + β8 LRit-1 + β9 CREit-1 + d_STATE_CRE + d _FOREIGN_CRE + εit Fixed-effects (within) regression Group variable: code Number of obs Number of groups = = 80 24 R-sq: Obs per group: = avg = max = 3.3 within = 0.3951 between = 0.0348 overall = 0.0989 corr(u_i, Xb) F(11,23) Prob > F = -0.3989 = = 23.14 0.0000 (Std Err adjusted for 24 clusters in code) Robust Std Err NPLs Coef t lagNPLs lagGDP lagUN lagRLR lagROE lagINEF lagNII lagLR lagCRE dSTATE_CRE dFOREIGN_CRE _cons 0981529 1.759113 -5.090349 -.5010818 -.0099383 -.6586745 -.0137781 -9.353256 -.0146254 0786927 -.009088 20.65314 1081899 5594023 1.768403 235513 1022484 3735335 017975 6.0744 0080052 056933 0153907 7.592237 sigma_u sigma_e rho 1.5666015 1.5118071 51779396 (fraction of variance due to u_i) 0.91 3.14 -2.88 -2.13 -0.10 -1.76 -0.77 -1.54 -1.83 1.38 -0.59 2.72 P>|t| 0.374 0.005 0.008 0.044 0.923 0.091 0.451 0.137 0.081 0.180 0.561 0.012 [95% Conf Interval] -.1256549 6019013 -8.748569 -.9882776 -.2214552 -1.431387 -.0509622 -21.91911 -.0311853 -.0390823 -.040926 4.947401 3219608 2.916325 -1.432129 -.013886 2015786 1140385 0234059 3.212599 0019346 1964676 02275 36.35888 Truong Ngoc Thanh – Class 19 Page 55 ...Determinants of nonperforming loans – The case of Vietnamese banking sector ABSTRACT The main purpose of this study is to examine the determinants of non-performing loans (NPLs) in the case of. .. -0 .222 -0 .098 -0 .266 RLR t-1 0.130 0.166 0.494 -0 .832 DEBT t-1 DEBT t-1 ROE t-1 INEF t-1 NII t-1 LR t-1 0.250 0.114 0.558 -0 .948 0.892 ROE t-1 -0 .180 -0 .246 -0 .020 -0 .003 -0 .042 -0 .006 INEF t-1 0.050... Page Determinants of nonperforming loans – The case of Vietnamese banking sector Vietnamese banking sector are not examined Therefore, this study will examine the NPLs’ determinants in the case of