UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM ERASMUS UNVERSITY ROTTERDAM INSTITUTE OF SOCIAL STUDIES THE NETHERLANDS VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS COLLATERAL LIQUIDITY AND LOAN DEFAULT RISKS: THE CASE OF VIETNAM BY NGUYEN LE HIEU MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY, Dec 2016 UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM INSTITUTE OF SOCIAL STUDIES THE HAGUE THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS COLLATERAL LIQUIDITY AND LOAN DEFAULT RISKS: THE CASE OF VIETNAM A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By NGUYEN LE HIEU Academic Supervisor: Dr LE HO AN CHAU HO CHI MINH CITY, Dec 2016 DECLARATION By these statements, I declare that the thesis titled “Collateral liquidity and loan default risks: the case of Vietnam” is result of my own works and efforts All the contents in this thesis are my study based on reviewing some previous papers which are clearly indicated in references In addition, this thesis has not been submitted to get any other degrees or certifications Signature NGUYEN LE HIEU December 2016 ACKNOWLEDGEMENTS I desire to express my sincere gratitude to my supervisor Dr Le Ho An Chau for her devotion to this thesis completion Her recommendations really help to improve the quality of this study very much My special thanks for all lecturers who taught me many profound and useful knowledge Thanks all VNP office employees who supported me so much during my master course Besides, I really appreciated unforgettable memories that I have experienced with all my classmates in VNP class 21 This friendship will be maintained and developed deeply in future Lastly, I would like to show my thankfulness to my family who really supported me so much and therefore I am enabled to finish this course ABSTRACT This thesis investigates the impact of the liquidity level of collaterals on the probability of default of individual loans and examines the channels through which collaterals affect default risks Following the approach of Jiménez and Saurina (2004), binominal logit model is applied on the data from individual loan accounts of a medium – size commercial bank in Vietnam The empirical results suggest the significant and negative impact of collaterals’ liquidity on loans’ probability of default, supporting the dominance of borrower selection effect and risk shifting effect over lender selection effect Moreover, the finding also implies that bank has not applied carefully and thoroughly screening process on loans that are fully secured by low liquid collaterals and therefore impaired the credit quality of loan portfolio CONTENTS CHAPTER 1.1 Research background and motivation 1.2 Research objectives and research questions 1.3 Research Methodologies and Data 1.4 Research Contribution 1.5 Structure of thesis CHAPTER 2.1 Theoretical review of relationship between collaterals and loan risks 2.2 Empirical review of relationship between collaterals and loan risk 12 2.3 Theoretical framework 16 CHAPTER 17 3.1 Research methodology 17 3.2 Data 19 CHAPTER 23 4.1 Descriptive Statistics and Pre-estimation tests .23 4.2 Empirical results .25 4.3 Robustness test 30 CHAPTER 35 5.1 Main findings & conclusion 35 5.2 Policy implications 35 5.3 Research limitation and further research 37 REFERENCES 39 APPENDIX 42 LIST OF TABLES AND FIGURES Table 1: Summary of variables 21 Table 2: Summary of loans characteristics 23 Table 3: Summary of default loans according to liquidity levels of collaterals 24 Table 4: Summary of default loans according to varied amounts of loans 24 Table 5: Summary of loans default according to rate of protection 25 Table 6: Summary of loans default according to loan time 25 Table 7: Estimation results of Logit model 26 Table 8: Estimation results of Logit model (exclude interest and loan time factors) 27 Table 9: Estimation results of second logit model for robustness test 31 Table 10: Estimation results of the third logit model for robustness test 33 Figure 1: NPL rate of Viet Nam for the period from Dec-2012 to Jun-2013 Figure 2: NPL of Viet Nam for the period from Jun-2014 to Dec-2015 Figure 3: The transmission channels of collaterals on loan risk Figure 4: Screening cost prorated Figure 5: House price index of HCM city from 2009 to Q3-2016 36 CHAPTER INTRODUCTION 1.1 Research background and motivation Non-performing loans (NPL) are a severe problem for the whole economy of the world since they lead to the financial crisis in East Asian countries, America and Sub-Saharan Africa (Farhan, Satta, Chaudrhy & Khalil, 2012) Therefore, finding out the main determinants of NPL plays an important role in policy making in order to prevent the future bad debts (Adebola, Wan Yusoff, & Dahala (2011) in Farhan et al (2012)) Previous studies identify that macro-economic conditions, bank and borrowers specific characteristics, loans characteristics, relationship banking1, and collaterals are key drivers of default risks and hence NPL The relation between collateral characteristics and loan default is investigated in many studies over the world However, the findings are inconsistent among different papers, some of which show positive relationship while the others provide evidence of a negative effect Berger, Frame and Ioannidou (2011) find a positive relationship between collaterals pledge and ex-post NPL in Bolivia for the period from 1998 to 2003 This result is supported by Jiménez and Saurina (2004) for Spain Berger and Udell (1990) in Leitner (2006) shows that borrowers who pledge collaterals tend to be worse and therefore are riskier Leitner (2006) explains that this finding is due to collaterals’ requirement of banks for riskier borrowers In contrast, John, Lynch and Puri (2003) investigate the yield difference between secured and unsecured loans in US and conclude that higher yield is decided by secured loans This result implies that borrowers who pledge collaterals are more efficient than others Kugler and Oppes (2005) investigate the impact of collaterals2 on loans risk in case of group lending in a developing country and find that collaterals are used by individuals to prevent loans default under joint borrowing Banks who supply more services in long time for customers will have more private information of their customers according to Argawal et al (2009) Collateral in this paper is defined as equity capital of individual dedicated to investment projects Berger et al (2011) argue that the diversified findings about this relationship arise from the variation of data samples which include different types and characteristics of collaterals Moreover, previous papers investigate only the impact of collaterals on NPL by comparing the default risk (probability of default) between secured loans and unsecured loans To my knowledge, there is very limited work on the impact of different collateral types and characteristics on default risk Berger et al (2011) find that liquid collaterals decrease the probability of default when compared to non-liquid collaterals However, previous papers mainly focus on loans for companies/enterprises rather than individual and consumer loans In Vietnam, bad debt has increased sharply since 2011 and still been serious until now As we can see in Figure and 2, NPL ratio has risen from 4.08% in Dec-2012 up to 4.67% in April2013, then decreased lightly to 4.46% in Jun-2013 and kept declining to 2.58% in Jun-2016 However, this does not represent an improvement in loan quality of banks but due to banks’ switching to other asset titles in the balance sheet to hide bad debts The Viet Nam Assets Management Company (VAMC) was established on July-2013 with its main objective is bad debts purchasing by issuing special bonds for payment As of Jun-2016, VAMC purchased about 251.000 billions of bad debts from banks and only 15% of these bad debts were collected (VAMC, 2016) Hence, these purchased bad debts help to reduce NPL of banks but they were not collected in reality and still harm the whole economy Furthermore, many bad debts have been restructured but still classified as normal debts instead of bad debt in almost Vietnamese banks (this problem is permitted by the State Bank of Viet Nam) and therefore these bad debts were hidden Figure 1: NPL rate of Viet Nam for the period from Dec-2012 to Jun-2013 Source: State Bank of Viet Nam in http://tapchitaichinh.vn/ Figure 2: NPL of Viet Nam for the period from Jun-2014 to Dec-2015 Source: State Bank of Viet Nam Ogeisia et al (2014) argue that lending in low income countries is notoriously risky because of information asymmetry problem which are high in developing countries United Nations Conference on Trade and Development - UNCTAD (2005) explains that high level of information asymmetry arises from weak credit information infrastructure, ineffective public vif, uncentered Variable VIF 1/VIF liquidrank middleterm amount2 amount3 protect75 protect100 profession year ownership 7.62 2.86 1.75 1.72 2.25 1.05 0.131269 0.349439 0.571355 0.580475 0.443539 0.951343 1.56 2.80 0.639084 0.357746 3.90 2.59 1.17 0.256381 0.386522 0.856823 Mean VIF 2.66 We can see strong multicollinearity problem happens for the main predictor liquidrank Because middleterm has the second level of multicollinearity and insignificant relation, this variable is dropped out of model Predict PD based on all explanation variables after dropping middleterm variable 51 logit default liquidrank amount2 amount3 amount1 protect75 protect100 protect > 50 i.profession i.year ownership note: amount1 omitted because of collinearity note: protect50 omitted because of collinearity Iteration 0: log likelihood = -1120.1469 Iteration 1: log likelihood = -945.28928 Iteration 2: log likelihood = -929.33676 Iteration 3: log likelihood = -929.20645 Iteration 4: log likelihood = -929.20644 Logistic regression Number of obs LR chi2(10) Prob > chi2 Pseudo R2 Log likelihood = -929.20644 = = = = 2,295 381.88 0.0000 0.1705 default Coef liquidrank amount2 amount3 amount1 protect75 protect100 protect50 -.4815818 5323998 1.522872 7769242 1.847998 0480508 1605361 1565143 (omitted) 1308738 4756882 (omitted) -10.02 3.32 9.73 0.000 0.001 0.000 -.5757596 2177548 1.21611 -.387404 8470449 1.829634 5.94 3.88 0.000 0.000 5204163 9156665 1.033432 2.78033 profession 4294149 6180874 1826838 1572201 2.35 3.93 0.019 0.000 0713613 3099416 7874685 9262332 year -.0730287 -.5888943 1846753 2120885 -0.40 -2.78 0.693 0.005 -.4349856 -1.00458 2889281 -.1732085 ownership _cons 208326 -.6230577 1675615 2466107 1.24 -2.53 0.214 0.012 -.1200884 -1.106406 5367404 -.1397095 Std Err z P>|z| [95% Conf Interval] Test for multicollinearity 52 vif, uncentered Variable VIF 1/VIF liquidrank amount2 amount3 protect75 protect100 profession year ownership 4.13 1.64 1.59 2.12 1.05 0.241896 0.610917 0.626987 0.472023 0.953686 1.53 2.39 0.653443 0.419223 3.84 2.40 1.15 0.260293 0.416462 0.873310 Mean VIF 2.18 Now the multicollinearity still happen but at an acceptable level Test for specification error of model linktest Iteration Iteration Iteration Iteration Iteration Iteration 0: 1: 2: 3: 4: 5: log log log log log log likelihood likelihood likelihood likelihood likelihood likelihood = = = = = = -1120.1469 -941.74673 -925.87787 -925.01833 -925.01698 -925.01698 Logistic regression Number of obs LR chi2(2) Prob > chi2 Pseudo R2 Log likelihood = -925.01698 default Coef _hat _hatsq _cons 1.295764 1264435 035562 Std Err .1211987 0436652 0950955 z 10.69 2.90 0.37 P>|z| 0.000 0.004 0.708 = = = = 2,295 390.26 0.0000 0.1742 [95% Conf Interval] 1.058219 0408612 -.1508217 1.533309 2120258 2219458 The prediction _hat is significant at 1% which shows the suitability of the using model However, the prediction square _hatsq is also significant at 1% This implies a specification error of the model As and professions variables are significant, interaction effect may appears in model Adding interaction variables that are hcmtrade and hcmservice in the model, then, regressing the model again 53 Regress the model including interaction variables logit default liquidrank amount2 amount3 amount1 protect75 protect100 protect > 50 i.profession hcmtrade hcmservice ownership i.year note: amount1 omitted because of collinearity note: protect50 omitted because of collinearity Iteration 0: log likelihood = -1120.1469 Iteration 1: log likelihood = -913.15293 Iteration 2: log likelihood = -892.46037 Iteration 3: log likelihood = -891.95552 Iteration 4: log likelihood = -891.95505 Iteration 5: log likelihood = -891.95505 Logistic regression Number of obs LR chi2(12) Prob > chi2 Pseudo R2 Log likelihood = -891.95505 = = = = 2,295 456.38 0.0000 0.2037 default Coef liquidrank amount2 amount3 amount1 protect75 protect100 protect50 -.3334272 5116916 1.288118 8390971 2.055095 0527406 1627406 1629333 (omitted) 1347392 4708942 (omitted) -6.32 3.14 7.91 0.000 0.002 0.000 -.4367969 1927259 9687743 -.2300575 8306572 1.607461 6.23 4.36 0.000 0.000 5750131 1.132159 1.103181 2.978031 profession 9795993 1.865046 2225037 2191612 4.40 8.51 0.000 0.000 5435002 1.435498 1.415698 2.294594 hcmtrade hcmservice ownership -1.735085 -.9653824 2746586 2163086 289213 1731567 -8.02 -3.34 1.59 0.000 0.001 0.113 -2.159042 -1.532229 -.0647224 -1.311128 -.3985354 6140396 year -.2259606 -.4006779 1872429 2110941 -1.21 -1.90 0.228 0.058 -.59295 -.8144148 1410287 013059 _cons -1.201969 2670936 -4.50 0.000 -1.725463 -.6784748 Std Err z P>|z| [95% Conf Interval] Test for specification error of model 54 linktest Iteration Iteration Iteration Iteration Iteration Iteration 0: 1: 2: 3: 4: 5: log log log log log log likelihood likelihood likelihood likelihood likelihood likelihood = = = = = = -1120.1469 -905.36235 -892.55597 -890.8063 -890.80362 -890.80362 Logistic regression Number of obs LR chi2(2) Prob > chi2 Pseudo R2 Log likelihood = -890.80362 default Coef _hat _hatsq _cons 1.101079 0547847 -.0287792 Std Err .0889057 0363416 0954801 z 12.38 1.51 -0.30 P>|z| 0.000 0.132 0.763 = = = = 2,295 458.69 0.0000 0.2047 [95% Conf Interval] 9268269 -.0164436 -.2159167 1.275331 126013 1583583 Now prediction square is insignificant at 5% Hence, the model now is suitable and meaningful Test again for multicollinearity vif, uncentered Variable VIF 1/VIF liquidrank amount2 amount3 protect75 protect100 profession hcmtrade hcmservice ownership year 4.32 1.64 1.66 2.14 1.05 0.231384 0.610823 0.601216 0.467259 0.952621 3.07 6.18 4.57 2.51 1.15 0.325900 0.161908 0.218784 0.397867 0.870087 3.96 2.40 0.252696 0.416028 Mean VIF 2.89 Subtitute liquidrank variable by dummy variables which represent for ranks of liquidity of collaterals Rank will be omitted from the model and the result will show the differrence in PD between each of ranks and rank 55 logit default rank1 rank2 rank3 rank4 rank6 rank5 amount2 amount3 amount1 pro > tect75 protect100 protect50 i.profession hcmtrade hcmservice ownership i.year note: rank5 omitted because of collinearity note: amount1 omitted because of collinearity note: protect50 omitted because of collinearity Iteration 0: log likelihood = -1120.1469 Iteration 1: log likelihood = -911.98164 Iteration 2: log likelihood = -890.93473 Iteration 3: log likelihood = -890.41047 Iteration 4: log likelihood = -890.40995 Iteration 5: log likelihood = -890.40995 Logistic regression Number of obs LR chi2(16) Prob > chi2 Pseudo R2 Log likelihood = -890.40995 Std Err z P>|z| = = = = 2,295 459.47 0.0000 0.2051 default Coef [95% Conf Interval] rank1 rank2 rank3 rank4 rank6 rank5 amount2 amount3 amount1 protect75 protect100 protect50 1.584249 1.04372 6881042 4726243 0690027 5157579 1.249982 8635729 2.059237 3173783 2615197 1645694 1873511 2504978 (omitted) 1639457 1651124 (omitted) 1364831 4723621 (omitted) 4.99 3.99 4.18 2.52 0.28 0.000 0.000 0.000 0.012 0.783 9621986 5311504 3655541 105423 -.4219639 2.206299 1.556289 1.010654 8398257 5599694 3.15 7.57 0.002 0.000 1944302 9263678 8370856 1.573596 6.33 4.36 0.000 0.000 596071 1.133424 1.131075 2.985049 profession 9985595 1.897921 2237895 2232058 4.46 8.50 0.000 0.000 5599401 1.460445 1.437179 2.335396 hcmtrade hcmservice ownership -1.780319 -.9992672 2682251 2226233 29159 1745032 -8.00 -3.43 1.54 0.000 0.001 0.124 -2.216652 -1.570773 -.0737949 -1.343985 -.4277613 6102451 year -.2292811 -.4450581 1878804 2166305 -1.22 -2.05 0.222 0.040 -.5975199 -.869646 1389577 -.0204702 _cons -2.931585 1807326 -16.22 0.000 -3.285814 -2.577355 Test for specification error and multicollinearity: 56 linktest Iteration Iteration Iteration Iteration Iteration Iteration 0: 1: 2: 3: 4: 5: log log log log log log likelihood likelihood likelihood likelihood likelihood likelihood = = = = = = -1120.1469 -904.14938 -891.04397 -889.23968 -889.23693 -889.23693 Logistic regression Number of obs LR chi2(2) Prob > chi2 Pseudo R2 Log likelihood = -889.23693 default Coef _hat _hatsq _cons 1.102432 0551089 -.0284142 Std Err .0889681 036217 095091 z 12.39 1.52 -0.30 P>|z| 0.000 0.128 0.765 = = = = 2,295 461.82 0.0000 0.2061 [95% Conf Interval] 9280575 -.0158751 -.2147892 1.276806 1260929 1579607 vif, uncentered Variable VIF 1/VIF rank1 rank2 rank3 rank4 rank6 amount2 amount3 protect75 protect100 profession hcmtrade hcmservice ownership year 1.05 1.15 1.48 1.31 1.19 1.65 1.72 2.06 1.05 0.949934 0.865966 0.675975 0.763534 0.842713 0.606421 0.579812 0.485412 0.951062 3.15 6.57 5.05 2.55 1.17 0.317117 0.152229 0.197901 0.391475 0.851860 2.94 2.08 0.340150 0.480128 Mean VIF 2.26 By these test results, the second model is also suitable and meaningful Replacing dummy variables which represent group of loans amount by continuous variable named “sizes” 57 logit default rank1 rank2 rank3 rank4 rank6 rank5 sizes protect75 protect100 > protect50 i.profession ownership i.year note: rank5 omitted because of collinearity note: protect50 omitted because of collinearity Iteration 0: log likelihood = -1120.1469 Iteration 1: log likelihood = -935.72176 Iteration 2: log likelihood = -914.57054 Iteration 3: log likelihood = -914.50561 Iteration 4: log likelihood = -914.5056 Logistic regression Log likelihood = Number of obs LR chi2(13) Prob > chi2 Pseudo R2 -914.5056 default Coef rank1 rank2 rank3 rank4 rank6 rank5 sizes protect75 protect100 protect50 1.38179 1.388772 1.125007 6728665 -.2730854 1081246 7566801 1.754947 3431518 2418181 1493127 1818158 2559713 (omitted) 01084 131751 484023 (omitted) profession 3875942 6447703 ownership Std Err z = = = = 2,295 411.28 0.0000 0.1836 P>|z| [95% Conf Interval] 4.03 5.74 7.53 3.70 -1.07 0.000 0.000 0.000 0.000 0.286 7092252 9148174 8323594 3165141 -.77478 2.054356 1.862727 1.417655 1.029219 2286091 9.97 5.74 3.63 0.000 0.000 0.000 0868785 498453 8062796 1293707 1.014907 2.703615 1876582 1568906 2.07 4.11 0.039 0.000 0197909 3372703 7553976 9522703 2169099 1700674 1.28 0.202 -.1164161 5502358 year -.0591933 -.5680211 1867232 2177214 -0.32 -2.61 0.751 0.009 -.425164 -.9947473 3067774 -.141295 _cons -3.431439 1871061 -18.34 0.000 -3.79816 -3.064717 Note: failures and success completely determined 58 logit,or note: rank5 omitted because of collinearity note: protect50 omitted because of collinearity Logistic regression Log likelihood = Number of obs LR chi2(13) Prob > chi2 Pseudo R2 -914.5056 default Odds Ratio rank1 rank2 rank3 rank4 rank6 rank5 sizes protect75 protect100 protect50 3.982025 4.009923 3.080239 1.959847 7610278 1.114187 2.131189 5.783143 1.366439 9696718 4599189 3563312 1948013 (omitted) 0120778 2807862 2.799174 (omitted) profession 1.473432 1.905549 ownership Std Err z = = = = 2,295 411.28 0.0000 0.1836 P>|z| [95% Conf Interval] 4.03 5.74 7.53 3.70 -1.07 0.000 0.000 0.000 0.000 0.286 2.032416 2.496319 2.298736 1.372336 4608051 7.801808 6.441277 4.127429 2.798879 1.256851 9.97 5.74 3.63 0.000 0.000 0.000 1.090764 1.646173 2.23956 1.138112 2.759108 14.93362 2765016 2989628 2.07 4.11 0.039 0.000 1.019988 1.401118 2.128458 2.591587 1.242232 2112632 1.28 0.202 8901048 1.733662 year 9425246 5666456 1759912 1233709 -0.32 -2.61 0.751 0.009 6536626 3698169 1.359038 8682331 _cons 0323404 0060511 -18.34 0.000 022412 046667 Note: failures and success completely determined Test for multicollinearity 59 vif,uncentered Variable VIF 1/VIF rank1 rank2 rank3 rank4 rank6 sizes protect75 protect100 profession ownership year 1.06 1.12 1.32 1.30 1.17 1.89 1.95 1.08 0.944015 0.894349 0.756533 0.769681 0.854506 0.528209 0.513305 0.928767 1.55 2.28 1.17 0.646615 0.438173 0.852056 2.92 1.96 0.342584 0.511022 Mean VIF 1.60 Wald test for this model 60 test rank1=rank2 ( 1) [default]rank1 - [default]rank2 = chi2( 1) = Prob > chi2 = 2.00 0.1572 test rank1=rank3 ( 1) [default]rank1 - [default]rank3 = chi2( 1) = Prob > chi2 = 7.42 0.0065 test rank1=rank4 ( 1) [default]rank1 - [default]rank4 = chi2( 1) = Prob > chi2 = 10.72 0.0011 test rank1=rank6 ( 1) [default]rank1 - [default]rank6 = chi2( 1) = Prob > chi2 = 15.71 0.0001 test rank2=rank3 ( 1) [default]rank2 - [default]rank3 = chi2( 1) = Prob > chi2 = 1.75 0.1865 test rank2=rank4 ( 1) [default]rank2 - [default]rank4 = chi2( 1) = Prob > chi2 = 3.92 0.0477 test rank2=rank6 ( 1) [default]rank2 - [default]rank6 = chi2( 1) = Prob > chi2 = 8.09 0.0044 test rank3=rank4 ( 1) [default]rank3 - [default]rank4 = chi2( 1) = Prob > chi2 = 1.07 0.3002 test rank3=rank6 ( 1) [default]rank3 - [default]rank6 = chi2( 1) = Prob > chi2 = 4.97 0.0257 61 test rank4=rank6 ( 1) [default]rank4 - [default]rank6 = chi2( 1) = Prob > chi2 = 2.05 0.1519 10 Wald test for the first model test liquidrank amount2 amount3 protect75 protect100 hcmtrade hcmservice ( ( ( ( ( ( ( 1) 2) 3) 4) 5) 6) 7) [default]liquidrank = [default]amount2 = [default]amount3 = [default]protect75 = [default]protect100 = [default]hcmtrade = [default]hcmservice = chi2( 7) = Prob > chi2 = 271.18 0.0000 test amount2=amount3 ( 1) [default]amount2 - [default]amount3 = chi2( 1) = Prob > chi2 = 21.11 0.0000 test protect75=protect100 ( 1) [default]protect75 - [default]protect100 = chi2( 1) = Prob > chi2 = 6.94 0.0084 11 Marginal effect 62 margin, dydx(*) atmeans post Conditional marginal effects Model VCE : OIM Number of obs = 2,295 Expression : Pr(default), predict() dy/dx w.r.t : liquidrank amount2 amount3 amount1 protect75 protect100 protect50 2.profession 3.profession hcmtrade hcmservice ownership 2.year 3.year at : liquidrank = 4.31024 (mean) amount2 = 2196078 (mean) amount3 = 1834423 (mean) amount1 = 5969499 (mean) protect75 = 5089325 (mean) protect100 = 0091503 (mean) protect50 = 4819172 (mean) 1.profession = 4849673 (mean) 2.profession = 1664488 (mean) 3.profession = 3485839 (mean) hcmtrade = 2623094 (mean) hcmservice = 0976035 (mean) ownership = 1381264 (mean) 1.year = 2305011 (mean) 2.year = 5198257 (mean) 3.year = 2496732 (mean) dy/dx Delta-method Std Err z P>|z| [95% Conf Interval] liquidrank amount2 amount3 amount1 protect75 protect100 protect50 -.0417493 0640703 1612888 1050657 2573241 0065347 0202497 0204492 (omitted) 0163666 0588937 (omitted) -6.39 3.16 7.89 0.000 0.002 0.000 -.054557 0243817 121209 -.0289416 1037589 2013685 6.42 4.37 0.000 0.000 0729876 1418945 1371437 3727537 profession 0979709 259074 0267376 0355767 3.66 7.28 0.000 0.000 0455661 1893449 1503756 3288031 hcmtrade hcmservice ownership -.2172547 -.1208782 0343908 0284809 0361086 0216641 -7.63 -3.35 1.59 0.000 0.001 0.112 -.2730762 -.1916497 -.0080701 -.1614333 -.0501067 0768516 year -.0304312 -.0508608 0260374 0274415 -1.17 -1.85 0.243 0.064 -.0814636 -.1046451 0206012 0029235 Note: dy/dx for factor levels is the discrete change from the base level 63 Marginal effect for the robustness regression margin, dydx(*) atmeans post Conditional marginal effects Model VCE : OIM Number of obs = 2,295 Expression : Pr(default), predict() dy/dx w.r.t : rank1 rank2 rank3 rank4 rank6 rank5 amount2 amount3 amount1 protect75 protect100 protect50 2.profession 3.profession hcmtrade hcmservice ownership 2.year 3.year at : rank1 = 0248366 (mean) rank2 = 0501089 (mean) rank3 = 1869281 (mean) rank4 = 1481481 (mean) rank6 = 0819172 (mean) rank5 = 508061 (mean) amount2 = 2196078 (mean) amount3 = 1834423 (mean) amount1 = 5969499 (mean) protect75 = 5089325 (mean) protect100 = 0091503 (mean) protect50 = 4819172 (mean) 1.profession = 4849673 (mean) 2.profession = 1664488 (mean) 3.profession = 3485839 (mean) hcmtrade = 2623094 (mean) hcmservice = 0976035 (mean) ownership = 1381264 (mean) 1.year = 2305011 (mean) 2.year = 5198257 (mean) 3.year = 2496732 (mean) dy/dx Delta-method Std Err z P>|z| [95% Conf Interval] rank1 rank2 rank3 rank4 rank6 rank5 amount2 amount3 amount1 protect75 protect100 protect50 1977546 1302828 0858929 0589955 0086133 0643797 1560296 1077959 2570452 0393241 0326626 0203939 0232703 031261 (omitted) 0203274 0206516 (omitted) 0165241 0589048 (omitted) 5.03 3.99 4.21 2.54 0.28 0.000 0.000 0.000 0.011 0.783 1206807 0662653 0459217 0133866 -.0526571 2748285 1943003 1258641 1046045 0698837 3.17 7.56 0.002 0.000 0245387 1155531 1042208 196506 6.52 4.36 0.000 0.000 0754093 1415939 1401825 3724964 profession 0992063 26339 0267828 0362294 3.70 7.27 0.000 0.000 046713 1923816 1516996 3343983 hcmtrade hcmservice ownership -.2222291 -.124734 0334813 0291971 0362791 0217591 -7.61 -3.44 1.54 0.000 0.001 0.124 -.2794544 -.1958397 -.0091656 -.1650038 -.0536282 0761283 year -.0310206 -.0559735 0262607 027892 -1.18 -2.01 0.238 0.045 -.0824907 -.1106408 0204496 -.0013063 Note: dy/dx for factor levels is the discrete change from the base level 64 Marginal effect for the third robustness regression margin, dydx(*) atmeans post Conditional marginal effects Model VCE : OIM Number of obs = 2,295 Expression : Pr(default), predict() dy/dx w.r.t : rank1 rank2 rank3 rank4 rank6 rank5 sizes protect75 protect100 protect50 2.profession 3.profession ownership 2.year 3.year at : rank1 = 0248366 (mean) rank2 = 0501089 (mean) rank3 = 1869281 (mean) rank4 = 1481481 (mean) rank6 = 0819172 (mean) rank5 = 508061 (mean) sizes = 6.861002 (mean) protect75 = 5089325 (mean) protect100 = 0091503 (mean) protect50 = 4819172 (mean) 1.profession = 4849673 (mean) 2.profession = 1664488 (mean) 3.profession = 3485839 (mean) ownership = 1381264 (mean) 1.year = 2305011 (mean) 2.year = 5198257 (mean) 3.year = 2496732 (mean) dy/dx Delta-method Std Err z P>|z| [95% Conf Interval] rank1 rank2 rank3 rank4 rank6 rank5 sizes protect75 protect100 protect50 17425 1751305 1418685 0848515 -.0344373 013635 0954208 2213068 0430788 0305108 0186944 0227563 032297 (omitted) 0014318 0160596 0607877 (omitted) 4.04 5.74 7.59 3.73 -1.07 0.000 0.000 0.000 0.000 0.286 0898171 1153305 1052282 04025 -.0977382 258683 2349304 1785088 1294531 0288636 9.52 5.94 3.64 0.000 0.000 0.000 0108288 0639445 1021652 0164412 1268971 3404485 profession 0457323 0835333 0233353 0207197 1.96 4.03 0.050 0.000 -4.02e-06 0429235 0914685 1241431 ownership 0273533 0214319 1.28 0.202 -.0146524 069359 year -.0082333 -.0663903 0262058 0265309 -0.31 -2.50 0.753 0.012 -.0595957 -.1183899 0431291 -.0143907 Note: dy/dx for factor levels is the discrete change from the base level 65 ... DECLARATION By these statements, I declare that the thesis titled Collateral liquidity and loan default risks: the case of Vietnam is result of my own works and efforts All the contents in this thesis... investigates the impact of the liquidity level of collaterals on the probability of default of individual loans and examines the channels through which collaterals affect default risks Following the approach... longer the loan duration is, the lower the PD of that loan is and the same as in case of size of loans The model is controlled for professions of borrowers, timely factors and regions that loans