Determinants of loan repayment performance of household and micro borrowers in vietnam

VIETNAM NATIONAL UNIVERSITY, HANOI VIETNAM JAPAN UNIVERSITY - PHAM THANH DUNG DETERMINANTS OF LOAN REPAYMENT PERFORMANCE OF MICROBUSINESS BORROWERS IN VIETNAM MAJOR: MASTER OF BUSINESS ADMINISTRATION CODE: 8340101.01 RESEARCH SUPERVISORS: Prof Dr HIROSHI MORITA Assoc Prof Dr VU ANH DUNG Hanoi, 2020 THESIS ACKNOWLEDGMENT Foremost, I would like to express my sincere gratitude to my advisors Dr Vu Anh Dung and Dr Hiroshi Morita for the support of my Thesis study and research, for their motivation, and immense knowledge, as well their experience in the field Their guidance has helped me much in the time of research and writing of this thesis Besides my advisors, I would like to sincerely thank the rest of my thesis committee: Prof and Dr Matsui, Dr Tran Thi Lien, Dr Kodo, Dr Tran Thi Bich Hanh, and especially Dr Yoshifumi Hino, for their encouragement, insightful comments, and hard questions My thanks also go to Ms Nguyen Thi Huong, MBA program assistant who has enthusiastically supported me in the process of completing the procedure, as well as connecting for smooth communication between students and advisors in the time of my doing research i TABLE OF THE CONTENTS ABSTRACT iv LIST OF TABLES vi LIST OF FIGURES vii LIST OF DEFINITIONS AND ABBREVIATIONS viii CHAPTER ONE: INTRODUCTION 1.1 Background of the Problem 1.2 Statement of the Problems 1.3 Objectives of the Study 1.4 Research Questions 1.5 Scope of the Study .6 1.6 Structure of the research .6 CHAPTER TWO: LITERATURE REVIEW .8 2.1 Concept and Definition 2.2 Related Literature Reviews to the Variables Used in the Study 15 2.3 Cox Regression Model .24 Table 2.1 Comparison of models on the random sample 25 2.4 Research Gap 26 CHAPTER THREE: DATA AND METHODOLOGY 30 3.1 Description of the Study Area 30 3.2 Data Description .30 3.2.1 Sampling procedure and technique 31 3.3 Variables in the Research 3.3.1 Dependent Variables .32 3.4 Method for Data Analysis 34 3.5 Research Model 37 3.6 Summary of Cox Model factors in this research .38 ii CHAPTER 4: RESULTS AND DISCUSSIONS 39 4.1 Introduction 39 4.2 Summary Statistics .39 4.3 Results of the Cox Proportional Hazard Model 41 4.4 Discussions 51 CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS 61 5.1 Conclusions 61 5.2 Recommendations 63 5.3 Recommendations for Further Research 68 5.4 Limitations of the Study .68 REFERENCES 70 APPENDIX .73 iii ABSTRACT Introduction: Vietnam is a developing economy where the service industry and light industry account for a big percentage of businesses in the economy Businesses in these sectors are almost small and micro enterprises and household/family businesses (MSEs) They are always in need of capital for their activities However, it’s very difficult for them to get access to loans from banks and credit organizations Because they almost don’t have collateral to secure for their loan and are not able to provide an eligible financial statement which is usually required by banks Consequently, banks or creditors are not interested in offering loan to such clients because they usually don’t have collaterals and have difficulties in providing documents as required by banks, which is very risky to a bank if they accept such types of customers, whereas, there are not enough tools to define and mitigate relevant risk It’s very difficult for them to determine which are key factors affecting on loan repayment performance of micro-businesses in Vietnam, especially when using cash in payment is still popular, which makes banks find it more difficult and not interested in loans for micro business So, detailed research on the determinants of loan repayment performance of micro-businesses in Vietnam is essential Objectives: The objectives of this research is to analyze the determinants of credit risk of loan repayment performance of micro-businesses in Vietnam Methodology: A study was conducted on 200 enterprises and household businesses who have taken loans from Banks and P2P lending companies in Vietnam in 24 months starting from Jan 2018 to the end of 2019 (Regarding the average term of a loan is 12 months) The data used in this study was the secondary data from a joint-stock commercial bank and a P2P Lending company in Vietnam The Cox regression model is used with twelve explanatory variables Loan repayment status/Default rate is the dependent variable, while twelve characteristics of borrower and the enterprise owned by the borrower’s characteristics are considered as explanatory variables In this case, the value of the dependent variable (loan repayment status) is and 1, if borrowers defaulted it takes and otherwise Results: Ten out of eleven significant factors identified through the relevant model are: Gender, Age, Housing, Educational level, Business sector, Years in business, Percentage of share, Digital sales channel, Number of Clients, and Turnover stability in latest months, are the key factors that affect the loan repayment performance Among them, new factors which are analyzed regarding Vietnam iv economy’s characteristics and as a result of this Research are: Percentage of share owning, Digital sales channel, Number of clients, Turnover stability in latest months and Years in Business Conclusion: Hence, regarding the Research’s result, Banks and financial companies can use it to build their credit scoring models or system Accordingly, increase their ability in risk management as well as enhance their desire in helping MSEs get the loan that suitable and necessary for them, which is very important to improve economic growth Policymakers should pay attention to issue more proper policy to further support for MSEs who have been given with not enough financial as well as non-financial aids Furthermore, enterprise’s stakeholders can use this research’s result to increase performance rating for themselves and improve its rating to banks and creditors Keywords: Credit risk; Loan repayment performance; Microcredit; Micro business; Cox Regression model v LIST OF TABLES Table 2.1 Comparison of models on the random sample 25 Table 3.1 Sample Distribution 32 Table 3.2: Description of independent variables 33 Table 4.1: Status of repayment 39 Table 4.3: Summary statistics for continuous variables 41 Table 4.4: Univariate analysis result for each covariate 42 Table 4.5: Multivariate Cox Proportional Hazards Regression results 45 Table 4.6: Final model of Cox PH 46 Table 4.7: Cox Model with Time-Dependent Covariates 47 Table 4.8 Analysis of The variable of “Business sector” as categorical variable 48 vi LIST OF FIGURES Figure 1.1 Enterprise size categorizes by capital scale and labor scale vii LIST OF DEFINITIONS AND ABBREVIATIONS Asymmetric information Adverse selection Banks Creditors Credit Scoring Credit risk Debtor F&B Financial company Information failure occurs when one party to an economic transaction possesses greater material knowledge than the other party Adverse selection is when sellers have information that buyers not have, or vice versa, about some aspect of product quality A financial institution that accepts deposits from the public and creates Demand Deposit Lending activities can be performed either directly or indirectly through capital markets A creditor is a party (e.g., person, organization, company, or government) that has a claim on the services of a second party It is a person or institution to whom money is owed Creditworthiness test system or also known as Credit Risk Rating system The risk of default on a debt that may arise from a borrower failing to make required payments, including both principal and interest A debtor (also, debitor) is an entity that owes a debt to another entity The entity may be an individual, a firm, a government, a company or other legal person Food and Beverage industry HHs One that makes loans It may offer loans to both individuals and businesses Usually, when we think of a financial company, we think of one that offers shortterm loans to individuals However, it may extend credit to businesses, both small and large, as well Household business or family business MSEs Micro and small enterprises MSMEs Micro, small and medium enterprises Microcredit Microcredit is the extension of very small loans (microloans) to impoverished borrowers who typically lack collateral, steady employment, or verifiable credit history A category of financial services targeting individuals Microfinance viii NPL Loan repayment and small businesses who lack access to conventional banking and related services Microfinance includes microcredit, the provision of small loans to poor clients; savings and checking accounts; micro-insurance; and payment systems Non-Performing-Loan is a loan that is in default or close to being in default Many loans become nonperforming after being in default for 90 days, but this can depend on the contract terms Repayment of part of a loan, usually includes principal and interest, monthly P2P Lending company Peer-to-peer lending, also abbreviated as P2P lending, is the practice of lending money to individuals or businesses through online services that match lenders with borrowers Peer-to-peer lending companies often offer their services online, and attempt to operate with lower overhead and provide their services more cheaply than traditional financial institutions Working capital is defined as current assets minus current liabilities Secured loan A secured loan is a loan in which the borrower pledges some asset (e.g a car or property) as collateral for the loan, which then becomes a secured debt owed to the creditor who gives the loan SMEs Small and medium enterprises Unsecured loan An unsecured loan is a loan that is issued and supported only by the borrower's creditworthiness, rather than by any type of collateral Unsecured loans—sometimes referred to as signature loans or personal loans—are approved without the use of a property or other assets as collateral ix Determinants of credit risk from household businesses Internationally, there are some research on the Determinants of credit risk of loan repayment performance of microcredit However, “microcredit is a common form of MEs credit that involves an extremely small loan given to an individual to help them become self-employed or grow a small business These borrowers tend to be low-income individuals, especially from less developed countries (LDCs)” Hereby, in this Research, I focus on loans to MSEs and households necessary for them to run their business, which is much higher than the amount stated in the concept of microcredit internationally Hence, there is almost no research on the exact subject and target Instead, research on microcredit – a similar target was used Besides, Lack of organized secondary data, primary data, as well as inadequate resources and experience of the researcher may lead to the completion of this research, not as the author’s expectation 69 REFERENCES Changkuoth Jock Chol, (2019) Survival Analysis of Determinants of Credit risk of Microfinance Loan repayment: In case of Gambella micro credit and saving Institution, Ethiopia Hawassa University Nguyen Dang Nhat Phuong, (2018) Internal credit scoring in risk management at Nam A Bank –– MBA Faculty – Ho Chi Minh Industry and Technique University VPBank, (2013) Credit scoring system of Vietnam Prosperity Joint stock bank S M Sadatrasoul1*, M.R Gholamian1, M Siami1, Z Hajimohammadi, (2009) Credit scoring in banks and financial institutions via data mining techniques: A literature review Norhaziah Nawai and Mohd noor mohd sheriff,(2010) Factors Affecting Repayment Performance in Microfinance Programs in Malaysia Suraya Hanim Mokhtar1, Gilbert Nartea2, Christopher Gan, (2011) Determinants of microcredit loans repayment problem among microfinance borrowers in Malaysia Cox, D.R (1972) Regression models and life tables Journal of the Royal Statistical Society Series B (Methodological), Vol 34, No.2, PP 187-220 Ofgaha Alemu Dire, (2018) Determinants of Loan Repayment of Micro and Small Enterprises in Jimma Town, Ethiopia Bule Hora University Ruslan Abdul Nasser, NunungNuryartono, Hari Wijayanto, (2002) Credit risk Model for Micro, Small and Medium Enterprises (MSMEs) Loan at Bank XYZ 10 Rahman,(2011) Microcredit and poverty reduction: Trade-off between building institutions and reaching the poor 70 11 SyedMasud, AhmedMushtaque and Chowdhury AbbasBhuiya, (2010), Micro-Credit and Emotional Well-Being: Experience of Poor Rural Women from Matlab, Bangladesh 12 Ofgaha Alemu Dire, (2018) Determinants of Loan Repayment of Micro and small enterprises in Jimma Town, Ethiopia, Bule Hora University, Ethiopia 13 Michal Rychnovsky, (2018) Survival Analysis as a tool for Better Probability of Default Prediction, University of Economics, Prague 14 Emilio M Santandreu 1, Joaqn López Pascual and Salvador Cruz Rambaud, (2017) Determinants of Repayment among Male and Female Microcredit Clients in the USA An Approach Based on Managers’ Perceptions 15 Analysis of Credit risk on Bank Loans using Cox’s Proportional Hazards Model by Obuda felix Yala REG NUMBER: I56/74741/2014 16 Nancy Gathoni Kiliswa, Mohamed Sayeed Bayat, (2014) Determinants of Loan Repayment in Small Scale Enterprises in Developing Countries, University of Fort, South Africa 17 Farhad Taghizadeh-Hesary Naoyuki Yoshino, Phadet Charoensivakorn, and Baburam Niraula, (2009) Credit risk analysis of small and medium sized enterprises based on Thai data 18 Ngonnyani Danstun, (2019) The Effect of credit collection policy on portfolio at risk of microfinance institutions in Tanzania St John’s University of Tanzania, Tanzania Mapesa Harun School of Business, Mzumbe University, Tanzania 19 Ana Maria Sandica Monica Dudian, (2017) Time to Default in Credit Scoring Using Survival Analysis, The Bucharest University of Economics Studies, Romania 20 Appiah Naana Benedicta, (2011) Factors influencing loan delinquency in small and medium enterprises’ in Ghana commercial Bank Ltd 21 Improved Cox Hazard model for loans and advance impairment by George Kamau MButhia Reg No I56/76652/2014 71 22 Mikir Melese and Milkessa Asfaw, (2020) Determinants of Loan Repayment Performance of Omo Microfinance Institution: In the case of Mizan Aman Town, Southwest Ethiopia 23 Ryo Hasumi and Hideaki Hirata June, (2010), Small Business Credit Scoring: Evidence from Japan, Hosei University, Japan 24 Shaik Abdul Majeeb Pasha and Tolosa Negese, (2014) Performance of Loan repayment determinants in Ethiopian Micro Finance – An Analysis, Eurasian Journal of Business and Economics, 7(13), 29-49 25 Mohammed Ameen Qasem Ahmed Alnawah, Ma Huimin, Yousif A Alhaj, A S M Towhid, (2018) Factors affecting repayment performance in microfinance banks in Yemen: The case of Alkuraimi Islamic microfinance bank University of Technology, Wuhan China 26 Analysis of Credit Risk on Bank Loans Using Cox’s Proportional Hazards Model by Obuda Felix Yala Reg Number: I56/74741/2014 27 Shu-Min Lin Doctor of Philosophy, (2007) SMEs Credit Risk Modelling for Internal Rating Based Approach in Banking Implementation of Basel II Requirement The University of Edinburgh 2007 28 Joshua Sumankuuro, (2010) Loan Defaults in microfinancing of Small and Medium Scale Enterprises Charles Sturt University 29 Decree No 56/2009/ND-CP on support for small and medium enterprise, issued 30th June, 2009 30 Vietnam Enterprise Law 2018 No 68/2014/QH13 issued on 26th November, 2014 31 Asian Journal of Research in Business Economics and Management Vol 6, No 3, March 2016, pp 0-0) 72 APPENDIX Table 2.1 Comparison of models on the random sample Summary Development Comparison Logistic regression Gini 0.51 0.39 Cox model Gini 0.50 0.40 Logistic regression lift 10% 2.73 2.78 Cox model lift 10% 2.96 2.32 Table 3.1 Sample Distribution Group of MSEs Frequency Percentage Traders 63 31.5% Small Manufacturers 49 24.5% Construction 39 19.5% Service 49 24.5% Total 200 Table 3.2: Description of independent variables No Variables Covariates (Explanatory) X1 Age Age of customer Continuous X2 Gender Gender for the clients 0=Male, 1=Female X3 Marital status Marital status for clients 0=single, 1=married, 2=divorced, 3=widowed X4 Level educational for clients 0=Illiterate,1=Primary, 2=Secondary, 3=Preparatory or college, 4= Bachelor degree and above Educational level (Educlevel) Description 73 Values/Code X5 X6 X7 X8 X9 10 X10 11 12 X11 X12 Digital channel or not Besides traditional channel of sale, Micro business has digital channel 0=No, 1=Yes Housing The home that the client live in 0=Private, 1=Rent Percentage of share owning Percentage of share by the owner in the company 0=, 50% Years in business Number of experience of the owner 0=,1 year Amount of loan (AOL) Amount of money that clients took Continuous Business sector (BS) Distribution of clients by purposes of loan 0=Construction, 1=Trade, 2=Service, 3=Small manufacturing Number of clients Numbers of clients that the enterprise has up to now 0=, 50 The stability of turnover in latest months Change in turnover in months within months period 0, difference >30%, 1, difference < 30% Table 4.1: Status of repayment Default status Frequency Percent Non Default Valid Default Total Valid Percent Cumulative Percent 90 45.0 45.0 45.0 110 200 55.0 100.0 55.0 100.0 100.0 74 Table 4.2: Summary statistics for categorical variables Frequency Male Valid Female Total Single Valid Married Total 114 86 200 Frequency 57.0 43.0 100.0 Percent 52 148 200 26.0 74.0 100.0 Frequency High school Valid Degree or Higher Total Valid Percent Cumulative Percent Cumulative Percent 26.0 100.0 30.0 30.0 140 70.0 70.0 100.0 200 100.0 100.0 Percent 101 99 200 50.5 49.5 100.0 Percent 82 118 200 Frequency Over 50% Less than Valid 50% Total Cumulative Percent 57.0 100.0 30.0 Frequency Rent Valid Owned Total Percent Valid Percent 57.0 43.0 100.0 Valid Percent 26.0 74.0 100.0 60 Frequency No digital Valid Digital Total Gender Percent 41.0 59.0 100.0 Percent Valid Percent 50.5 49.5 100.0 Cumulative Percent 50.5 100.0 Valid Percent 41.0 59.0 100.0 Cumulative Percent 41.0 100.0 Cumulative Percent 49.0 100.0 98 49.0 Valid Percent 49.0 102 51.0 51.0 200 100.0 100.0 75 YIB Percent Frequency Less than years Valid Over years Total Valid 44.5 44.5 44.5 111 200 55.5 100.0 55.5 100.0 100.0 Percent 39 63 49 19.5 31.5 24.5 Valid Percent 19.5 31.5 24.5 49 24.5 24.5 200 100.0 100.0 Frequency Percent Valid Less than 50 Over 50 Total Valid Percent Valid Cumulative Percent 19.5 51.0 75.5 100.0 Cumulative Percent 73 36.5 36.5 36.5 127 200 63.5 100.0 63.5 100.0 100.0 Frequency Percent Difference over 30% Different less 30% Total Cumulative Percent 89 Frequency Construction Trade Service Small production Total Valid Percent Valid Percent Cumulative Percent 110 55.0 55.0 55.0 90 45.0 45.0 100.0 200 100.0 100.0 Table 4.3: Summary statistics for continuous variables N Minimum Maximum Mean Age 200 24 57 36.73 Amount 200 11,600,000 1,000,000,000 308,275,000 Valid N (listwise) 200 76 Table 4.4: Univariate analysis result for each covariate Age B -.008 Variables in the Equation SE Wald Df Sig Exp(B) 014 300 584 992 Omnibus Tests of Model Coefficientsa -2 Log Overall (score) Change From Change From Likelihood Previous Step Previous Block Chidf Sig ChiDf Sig ChiDf Sig square square square 862.532 301 584 302 583 302 583 Gender Variables in the Equation B SE Wald Df Sig Exp(B) 092 192 227 634 1.096 Omnibus Tests of Model Coefficientsa -2 Log Overall (score) Change From Change From Likelihood Previous Step Previous Block Chidf Sig ChiDf Sig Chidf Sig square square square 862.608 227 1 634 226 634 77 Variables in the Equation B SE Wald Df 089 212 177 Marital Sig Exp(B) 674 1.093 Omnibus Tests of Model Coefficientsa -2 Log Overall (score) Change From Change From Likelihood Previous Step Previous Block Chidf Sig ChiDf Sig Chidf Sig square square square 862.655 177 674 179 672 179 672 Edu Variables in the Equation B SE Wald Df Sig Exp(B) 009 193 002 961 1.010 Omnibus Tests of Model Coefficientsa -2 Log Overall (score) Change From Change From Likelihood Previous Step Previous Block Chidf Sig ChiDf Sig Chidf Sig square square square 862.832 002 961 002 961 002 961 Dig Variables in the Equation B SE Wald Df Sig Exp(B) 099 204 238 626 1.105 Omnibus Tests of Model Coefficientsa -2 Log Overall (score) Change From Change From Likelihood Previous Step Previous Block Chidf Sig ChiDf Sig Chidf Sig square square square 862.599 238 626 235 628 235 628 Variables in the Equation B SE Wald Df Sig Exp(B) Housing -.275 196 1.958 162 760 78 -2 Log Likelihood Omnibus Tests of Model Coefficientsa Overall (score) Change From Change From Previous Step Previous Block 78 Omnibus Tests of Model Coefficientsa -2 Log Overall (score) Change From Change From Likelihood Previous Step Previous Block Chidf Sig Chidf Sig Chidf Sig square square square 847.645 15.670 000 15.189 000 15.189 000 Clients Variables in the Equation B SE Wald Df Sig Exp(B) 328 196 2.794 095 1.388 Omnibus Tests of Model Coefficientsa -2 Log Overall (score) Change From Change From Likelihood Previous Step Previous Block Chidf Sig Chidf Sig Chidf Sig square square square 860.109 2.818 093 2.725 099 2.725 099 Variables in the Equation B SE Wald Df Sig Exp(B) Turnover 024 320 006 940 1.024 Omnibus Tests of Model Coefficientsa -2 Log Overall (score) Change From Change From Likelihood Previous Step Previous Block Chidf Sig ChiDf Sig Chidf Sig square square square 862.829 006 940 006 940 006 940 Omnibus Tests of Model Coefficientsa -2 Log Overall (score) Change From Change From Likelihood Previous Step Previous Block Chidf Sig Chidf Sig Chidf Sig square square square 860.856 1.969 161 1.978 160 1.978 160 79 Table 4.5: Multivariate Cox Proportional Hazards Regression results Variables in the Equation B SE Wald Df Sig Exp(B) Share -.400 273 2.147 143 670 a Omnibus Tests of Model Coefficients -2 Log Overall (score) Change From Previous Step Change From Previous Block Likelihood Chidf Sig Chidf Sig Chidf Sig square square square 844.314 19.533 11 052 18.520 11 070 18.520 11 070 a Beginning Block Number Method = Enter B Age Gender Edu Dig Housing Share YIB Sector Clients Turnover Marital -.009 142 -.090 -.084 -.238 -.218 121 412 210 -.103 048 SE 017 208 223 240 205 310 257 134 232 352 242 Variables in the Equation Wald df Sig .299 464 163 123 1.352 496 223 9.447 817 086 039 1 1 1 1 1 Covariate Means Mean Age 36.864 Gender 445 Edu 573 Dig 327 Housing 427 Share 855 YIB 264 Sector 1.036 Clients 400 Turnover 100 Marital 718 80 585 496 687 726 245 481 637 002 366 769 844 Exp(B) 991 1.152 914 919 788 804 1.129 1.510 1.233 902 1.049 95.0% CI for Exp(B) Lower Upper 958 1.025 766 1.734 590 1.415 574 1.472 527 1.177 438 1.477 682 1.867 1.161 1.965 783 1.943 453 1.797 653 1.685 Table 4.6: Final model of Cox PH Omnibus Tests of Model Coefficients -2 Log Likelihood Overall (score) Chi- df Change From Previous Step Sig Chi- square 844.353 a df Sig square 19.494 10 034 Change From Previous Block Chi- df Sig square 18.481 10 047 18.481 10 047 Variables in the Equation B SE Wald df Sig Exp(B) 95.0% CI for Exp(B) Lower Age Upper -.009 017 268 605 991 959 1.025 131 201 425 514 1.140 768 1.693 Edu -.097 220 194 660 908 590 1.397 Dig -.093 235 157 692 911 574 1.445 Housing -.240 205 1.367 242 787 527 1.176 Share -.220 310 505 477 802 437 1.473 YIB 123 257 228 633 1.130 683 1.871 Sector 414 134 9.519 002 1.512 1.163 1.967 Clients 218 227 924 336 1.244 797 1.942 -.095 349 074 786 910 459 1.802 Gender Turnover Covariate Means Mean Age 36.864 Gender 445 Edu 573 Dig 327 Housing 427 Share 855 YIB 264 Sector 1.036 Clients 400 Turnover 100 81 Table 4.7: Cox Model with Time-Dependent Covariates Omnibus Tests of Model Coefficients -2 Log Overall (score) Likelihood Chi- df Change From Previous Step Sig square 848.897 a Chi- df Change From Previous Block Sig Chi- square 14.676 10 144 13.938 df square 10 176 13.938 10 a Beginning Block Number Method = Enter Variables in the Equation B Age*T_COV_ SE Wald df Sig Exp(B) -.001 002 060 806 999 023 029 657 417 1.024 Edu*T_COV_ -.025 032 618 432 975 Dig*T_COV_ -.012 034 119 730 988 Housing*T_COV_ -.037 029 1.631 202 963 Share*T_COV_ -.029 049 337 561 972 T_COV_*YIB 012 038 096 756 1.012 Sector*T_COV_ 055 021 7.220 007 1.057 Clients*T_COV_ 015 034 191 662 1.015 -.010 051 036 850 990 Gender*T_COV_ T_COV_*Turnover 82 Sig .176 Covariate Means Mean Age 37.111 Gender 441 Edu 565 Dig 318 Housing 448 Share 877 YIB 262 Sector 900 Clients 356 Turnover 106 T_COV_ 4.846 Age*T_COV_ 180.172 Gender*T_COV_ 2.109 Edu*T_COV_ 2.738 Dig*T_COV_ 1.518 Housing*T_COV_ 2.254 Share*T_COV_ 4.303 T_COV_*YIB 1.246 Sector*T_COV_ 4.017 Clients*T_COV_ 1.643 T_COV_*Turnover 507 Table 4.8 Analysis of The variable of “Business sector” as categorical variable B Age Gender Marital Edu Dig Housing Share YIB Amount Sector Sector(1) Sector(2) Sector(3) Clients Turnover SE -.005 108 013 -.106 -.106 -.233 -.068 087 000 -2.622 -2.129 -1.913 242 -.046 Variables in the Equation Wald df 017 094 216 250 243 003 228 216 253 176 208 1.250 332 042 266 107 000 077 13.919 742 12.495 716 8.832 687 7.751 245 979 361 017 83 Sig 1 1 1 1 1 1 759 617 959 642 675 264 837 744 781 003 000 003 005 322 898 Exp(B) 995 1.114 1.013 899 899 792 934 1.091 1.000 073 119 148 1.274 955 ... and not interested in loans for micro business So, detailed research on the determinants of loan repayment performance of micro- businesses in Vietnam is essential Objectives: The objectives of. .. category of financial services targeting individuals Microfinance viii NPL Loan repayment and small businesses who lack access to conventional banking and related services Microfinance includes microcredit,... Objectives of the Research is: To identify determinants affect loan repayment performance of micro businesses in Vietnam Micro business includes micro enterprises and household business To identify