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

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 pop

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VIETNAM NATIONAL UNIVERSITY, HANOI

VIETNAM JAPAN UNIVERSITY

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PHAM THANH DUNG

DETERMINANTS OF LOAN REPAYMENT PERFORMANCE OF MICROBUSINESS

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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

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TABLE OF THE CONTENTS

ABSTRACT iv

LIST OF TABLES vi

LIST OF FIGURES vii

LIST OF DEFINITIONS AND ABBREVIATIONS viii

CHAPTER ONE: INTRODUCTION 1

1.1 Background of the Problem 1

1.2 Statement of the Problems 4

1.3 Objectives of the Study 5

1.4 Research Questions 6

1.5 Scope of the Study 6

1.6 Structure of the research 6

CHAPTER TWO: LITERATURE REVIEW 8

2.1 Concept and Definition 8

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

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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

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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 0 and 1, if borrowers defaulted it takes 1 and otherwise 0

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 6 latest months, are the key factors that affect the loan repayment performance Among them, 5 new factors which are analyzed regarding Vietnam

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economy’s characteristics and as a result of this Research are: Percentage of share owning, Digital sales channel, Number of clients, Turnover stability in 6 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

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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

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LIST OF FIGURES

Figure 1.1 Enterprise size categorizes by capital scale and labor scale 2

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LIST OF DEFINITIONS AND ABBREVIATIONS

Asymmetric information Information failure occurs when one party to an

economic transaction possesses greater material knowledge than the other party

Adverse selection Adverse selection is when sellers have information that

buyers do not have, or vice versa, about some aspect of product quality

public and creates Demand Deposit Lending activities can be performed either directly or indirectly

through capital markets Creditors 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 Credit Scoring Creditworthiness test system or also known as Credit

Risk Rating system Credit risk The risk of default on a debt that may arise from a

borrower failing to make required payments, including both principal and interest

Debtor 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

Financial company 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 term loans to individuals However, it may

short-extend credit to businesses, both small and large, as well

Microcredit Microcredit is the extension of very

small loans (microloans) to impoverished borrowers who typically lack collateral, steady employment, or verifiable credit history

Microfinance A category of financial services targeting individuals

close to being in default Many loans become performing after being in default for 90 days, but this can depend on the contract terms

non-Loan repayment 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

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

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CHAPTER ONE: INTRODUCTION

1.1 Background of the Problem

Micro enterprises and trading households (MSEs) play a vital role in the economic growth of Vietnam MSEs not only contribute to GDP of Vietnam, but also help create millions of jobs which make direct contribution to reduce unemployment and enhance public welfare Vietnam is home to 700,000 enterprises registered under the Law on Enterprises and more than 5.2 million trading households, in addition to foreign-invested enterprises In this case, the private sector accounts for over 60%

of the GDP, which undoubtedly makes it a driving force of the economy (The Vietnam Investment Review, 2019)

In terms of labor scale, nearly 98% of total enterprises in Vietnam are classified as Micro, Small and Medium Enterprises (MSMEs) while large-sized enterprises account for only the remaining 2% (Decree No 56/2009/ND-CP) Moreover, by the General Statistics Office of Vietnam, in 2017, Vietnam also has more than 5.2 million trading households (HHs) which have similar characteristics as micro enterprises (GSO, 2016) This research, research subjects are included of both micro-enterprises and trading households, which is abbreviated as MSEs in this document

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Figure 1.1 Enterprise size categorizes by capital scale and labor scale

Since 2000, when the Enterprise Law was promulgated, Vietnam is emerging as an attractive and potential address for investors from all over the world According to the Annual Business Report 2016 by Vietnam Chamber of Commerce and Industry, Vietnam witnessed a considerable increase in the number of new enterprises registered, number of employees, as well as total capital during the 2007-2015 period In line with the remarkable economic development of Vietnam overall, small and medium-sized enterprises (SMEs) in Vietnam also have experienced amazing growth, especially

However, 70-80% MSEs in Vietnam have been facing with many challenges and difficulties One of key challenges to them is not having access to finance through formal channels (Banks, other financial companies) According to the Insight Asia survey of SMEs in 4 areas of F&B, supporting industry, fashion apparel and cosmetics which have fewer than 200 employees and now have stable production with workshops but intend to expand, or relocate its production / service area, 62%

of the respondents said that they faced difficulties in capital (mainly to invest in factories, machinery )

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In recent years, it is easier for MSEs to get a loan due to an increase in credit channels to them Some banks and financial companies such as VPBank, FEcredit, MSB, etc… have started offering loans to MSEs, but the number is still limited However, upward ratio of bad debt and Non-performing-loan (NPL) has been a big concern to many banks and creditors and make them hesitate in making new loans NPL is a situation in which, all or a part of principal and interest is not paid duly by borrowers which means bank will lose their money

Consequently, banks will tighten their credit policy and the access to finance for MSEs

is even smaller They require MSEs to give collateral or strict requirement and procedure or documents to get a loan In the reverse side, MSEs do not have collaterals

or are not in business long enough (usually more than 2 years as required by Vietnamese banks) to be provided with unsecured loans, incapable of formulating feasible production, business, projects, not good financial recording, etc for commercial banks to consider loan Therefore, It’s very difficult for MSEs to get loans from banks and credit organizations The priority almost is given to state owned enterprises, or big enterprises, or enterprises which have already doing business dozen years Due to lack of capital, MSME can’t develop as they should have been, even they face the risk of bankruptcy, being merged and acquired quite easily

Difficulties piled up, MSEs have no way than "take risk" to find informal sources of capital, also known as "black market" with extremely high lending interest rates, which almost lead to undesirable consequences as we already knew through many report/articles of violence relating to loans from black market recently As unofficial survey by Viet Nam Business Association, nearly 70% of 2,600 surveyed small-and-medium sized enterprises (SMEs) had to seek loans on the black market as they were unable to access bank loans The businesses’ ability to access loans fell from 45 per cent in 2011-2013 to 24 per cent in 2015 The loans granted to the remaining 30 per cent accounted for only 3 per cent of total bank capital in the market

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1.2 Statement of the Problems

The root cause of NPL is internal factors of the MSEs debtors Banks can make a mistake in making a loan decision for the debtor because the banks have a difficulty

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separate good quality and the poor quality debtor (Taswan, 2011), which is often called asymmetric information experienced by financial institutions (banks) and debtor as borrowers (Bakhtiar and Sugema, 2012) and can lead to adverse selection Wrong choice and disbursement of borrowers will put banks into a double-risk situation: make disbursement to not qualified borrowers and reject qualified applicants To minimize the risk of NPL, by [4], banks in the world implement a creditworthiness test system called Credit Scoring However, the more important thing is to predict the time-to-default has not yet been well defined In Vietnam, just few banks focus on credit to MSEs but they almost use traditional ways of case – by – case appraisal They also haven’t implemented a credit Scoring system sophisticated enough due to inadequate analysis of key determinants and lack of a proper credit risk modelling Whereas, having a credit risk modelling for internal rating is more and more important, especially nowadays, managent of risk is required by the standard of Basel II in many countries in the world, by [27]

This leads to a fact that it’s even more difficulty for MSEs to get loans Being afraid

of making wrong loan decisions, banks have trend to increase the requirement or increase interest rate to compensate for the risk cost that rise from unqualified customers and risk they may have to incur loss Accordingly, many MSEs find it even more difficult to get a loan from banks If they can get a loan, the interest rate

is also very high compared with the return that they can make from their business This, finally, make the flow of capital not smooth and limit economic growth Also, high NPL ratio can also lead to crisis and make banks stop lending out Also, it also can make investors worried of putting money in new enterprises as well as deposit money into bank Accordingly, this affect to all businesses in all fields, hence to the whole economy

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Internationally, there are many research using different models on determinants of credit risk of MSEs, but no research is made on trading households which is one of important business force in Vietnam In Vietnam, no research on determinants of credit risk of MSEs is available up to now Hence, the research is based on some research from foreign countries with similar characteristics in the economy to find out determinants on credit risk of loan repayment performance of micro business in Vietnam

Due to this reason, this Research is necessary to analysis a credit risk of loan repayment of micro business in Vietnam So that, it helps banks and credit organizations easier to define key determinants influencing default rate to mitigate risk for themselves and widely open to offering loan to MSEs in the economy Accordingly, it will help banks to develop a credit scoring system in the most proper way to minimize credit risk It also can help MSEs improve their performance and business efficiency through enhancing key factors that have a great impact on their business

Determinants of loan repayment performance to help the bank separate good quality debtor and bad quality debtor is expected to come out as the result of the research

1.3 Objectives of the Study

1.3.1 General Objectives

The general objective of this study is to analyze a credit risk of determinants of loan repayment performance of micro enterprises and household businesses in Vietnam

1.3.2 Specific Objectives

Specific Objectives of the Research is:

1 To identify determinants affect loan repayment performance of micro businesses in Vietnam Micro business includes micro enterprises and household business

1.4 Research Questions

From the above point of view and Objectives, research question problem as indicated as follows

1 What are determinants on loan repayment performance of micro business

2 What are key factors affecting default rate of micro credit in Vietnam?

3 Which model is most appropriate for analyses of credit risk of microcredit loan repayment in Vietnam?

1.5 Scope of the Study

Research subjects are micro enterprises and trading household borrowers (MSEs) in Vietnam MSEs is defined in term of capital amount, annual turnover, and number

of employees as per Vietnam Enterprise Law

These MSEs are located in Hanoi and neighboring area Hanoi is the capital of Vietnam with the highest density of enterprises and household businesses from various fields and sectors in the economy With data and sample taken within 2 continuous years of 2018 and 2019, from Hanoi and rural area – typical area, the research outcome is expected to indicate the overview of loan repayment performance of micro business in Vietnam

1.6 Structure of the research

With the aim at providing a full picture of micro business in Vietnam and determinants of credit risk of their loan repayment performance in the most detail and transparent way, this research is presented into 5 chapters as follows:

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Chapter 1: Introduction – This chapter reveals basic ideas of the research in

terms of background, objectives, subject, and scope of the research, and research questions in order to provide readers with general view and motivation of the Researcher

Chapter 2: Literature review – The 2nd chapter introduce and build comprehensive understanding over both basic and complicated concepts in banking and finance, explanation of credit risk, as well as a consolidated review of determinants of credit risk to the loan repayment performance of micro business, research gap as well as popular research models used in analyzing these determinants in Vietnam and in the world

Chapter 3: Research methodology – After Literature review, the 3rd chapter focus on a detail review of relevant models and the research model chosen to apply in this research Chapter 3 also describes how the research is designed and carried out, how data is collected, and cleaned before processing and analyzing in Chapter 4

Chapter 4: Data analysis and findings focus on presenting and analyzing

data and results collected This chapter also aims at discussion of research

results and comes up with relevant findings

Chapter 5: Conclusion – the last chapter summarizes the contributions,

recommendations as well as limitations, and intentions for future research

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CHAPTER TWO: LITERATURE REVIEW

2.1 Concept and Definition

2.1.1 Overview of Micro Credit

Microcredit is “the extension of very small loans (microloans) to impoverished borrowers who typically lack collateral, steady employment, or verifiable credit history It is designed to support entrepreneurship and alleviate poverty”

Microfinance—also called microcredit—is a way to provide small business owners and entrepreneurs access to capital Often these small and individual businesses don’t have access to traditional financial resources from major institutions (The balance.com)

In almost all previous Research and journals, microcredit is understood as a very small loan amount given to the poor or business unit, or women who want to start their own microenterprises

The average loan amount differs from countries to countries By [34], in USA, average microcredit is US$9,732 In Malaysia, that is US$427, that is in India, that

 total capital shall not exceed VND100 billion (around USD4.4 million);

 total revenue of the preceding year shall not exceed VND 300 billion (around USD13.2 million)”

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Among that, Micro and small enterprises (MSEs) is the concept to refer to very small and micro enterprises, which total revenue of not over USD 01 million, which

is similar to VND22 billion, with an average number of employees of 50

By Decree No 56/2009/ND-CP, MSEs and HHs account for nearly 98% business units of the economy in Vietnam Their total capital normally not exceed VND10 billion Their total working capital demand is around 1 billion per year and their normal tenor is from 3 months to 9 months, which equals to an average loan amount

of from VND 300 million to 900 million

These characteristics, compared with the concept of microcredit is different Because microcredit is referred to a very small loan amount, including the poor Hence, in this study, I would like to use the concept of MSEs credit, which is explained in detail as below, to fit with research subjects in Vietnam in this research

MSEs credit

According to the internationally-used concept of Microcredit and regard to the objectives of the research, a new concept will be created within the scope of this paper MSEs credit is understood and defined as “credit provided to micro and small enterprises and household business for their business to a certain amount and without collateral” MSEs credit includes loans provided MSEs, not included other financial services

From now in this research, MSEs is understood both as very small and micro enterprises and household businesses This concept will be used throughout the Research and the main study objectives of this Research

MSEs in Vietnam and as defined in this Research, are classified into 3 main groups,

as per the practice of banks and experience of experts, as below:

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 Startup enterprises with years in the business of fewer than 2 years They are

in their early stage and has earned the very first income Usually, they don’t have enough or can’t prove financial ability and don’t have collateral to get a loan from banks Desired loan ticket size is around 300 to 500 million VND

 Micro and small enterprises whose precursors are household for several years and seek for more capital to scale up their business, to move to higher target

in terms of revenue, benefit, as well as open more branches, etc… Desired loan ticket size is from 500 million VND to less than 1 billion VND

 Household or family business with a number of employees less than 10 people and average loan ticket size of 100 million VND

2.1.3 Characteristics of MSEs credit

Number of MSEs is very high in Vietnam, and most of them have demand to get a loan from banks and financial companies But just a small percentage can get access

to the capital they need, because of some characteristics of MSEs, such as: they don’t have collateral, their cash flow is frequent by month, even by day Their demand is repeatable which means after paying the first loan, they will ask for a new loan They also can ask for a higher loan amount if they prove their good payment history after the first loan They don’t have a strong ability of management and documentation which means the procedure for a loan should be as simple as possible

Accordingly, the main characteristics of MSEs-credit includes, but not limited to:

 First, small loan amount and short-terms loan, usually up to VND500 million and the term up to one year

 Secondly, payment schedules is equally monthly installment (EMI), which means MSEs pay an equal amount every month until the loan contract ends

 Thirdly, the loan procedure for MSEs credit is expected to be as simple as possible, with least required documents as possible because MSEs in Vietnam haven’t equipped themselves with an adequate management system

2.1.4 Overview of Micro Creditors in Vietnam

In Vietnam, many years ago, banks and creditors mainly focus on big cooperate and medium-sized enterprises Normally, these are secured loans with collateral as of real estate, factory, stock or payables Micro creditors are almost state-owned credit funds, and the average ticket size is just around 10 million VND, which is very small and not enough for a MSEs’ working capital demand Normally, as experience by experts in banking sector and standard by many Vietnamese joint-stock banks, a client of MEs will need an average of VND500 million per year to finance their working capital demand

Several years ago, some foreign financial companies first appeared and made big changes to the banking and financial market in Vietnam, especially segments of retail banking and micro-enterprises However, due to the sophistication in appraisal

of MSEs customers, as well as small NIM (net interest margin), just a few banks and financial companies offer loans to MSEs Normally, they just focus on retail banking and ignore or pay little attention to MSEs In 2019, there are only 2 banks and 2 financial companies, and 2 P2P lending companies offer loan to MEs with the maximum amount of VND500 million

Lendbiz JSC is the only P2P lending company now in Vietnam can connect investors and MSEs for loans up to VND 1 billion In this study, secondary data was taken from Lendbiz’s clients and credit officers

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2.1.5 Credit Needs by MSEs in Vietnam

As mentioned in the introduction part, the market of MSMEs in Vietnam is very big, includes both small, micro-enterprises, and household businesses This is estimated to be around 6 million of borrowers including both MEs and HHs

The need for credit from MSEs is very high and increasing recently when the economy of Vietnam is in its fastest period and businesses are thirsty for capital Main borrowers include, but are not limited to:

First, startup enterprises which need money to finance their business when they start

a business and are expected to have potentially good cash flow in the near future Normally, these are enterprises that have operated for around 1 year

Secondly, enterprises that have been in business for a certain period of time but still

in their early stage and need money to extend their activity Normally, these are enterprises that have operated for 2-3 years

Thirdly, the biggest clients in the MEs group They already got loans from banks and all are secured loans They have certain amount of assets but that is not enough

to secure for loans at bank Hence, they seek for short term unsecured loan for their working capital demand Though the unsecured loan accounts for small percentage among their total liability, but it still plays a very important part in their business because it can help to maintain liquidity of the enterprise

2.1.6 Credit Risk Defined

According to Investopedia, “Credit risk is the possibility of a loss resulting from a borrower's failure to repay a loan or meet contractual obligations Traditionally, it refers to the risk that a lender may not receive the owed principal and interest, which results in an interruption of cash flows and increased costs for collection [18] has similar findings Excess cash flows may be written to provide additional cover for credit risk Although it's impossible to know exactly who will

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default on obligations, properly assessing and managing credit risk can lessen the severity of a loss Interest payments from the borrower or issuer of a debt obligation are a lender's or investor's reward for assuming credit risk

 Credit risk is the possibility of losing a lender takes on due to the possibility

of a borrower not paying back a loan

 Consumer credit risk can be measured by the five Cs: credit history, capacity

to repay, capital, the loan's conditions, and associated collateral

 Consumers posing higher credit risks usually end up paying higher interest rates on loans”

There are several traditional ways to mitigate risk For instance, the lender/creditor will take as many as possible information from the borrower, verify the information and analyze clients using some tools depending on each type of client To secure for the loan in case being turned into bad debt, most of the banks in Vietnam will require the borrower to take out their collateral, such as house, factory, car, etc or guarantees from third parties Also, by the rule of risk and return, banks also charge

a higher interest rate for loan with higher risk, as a way of compensation for the potential loss they may incur

2.1.7 Relationship between Loan Default Problem and Determinants of Loan Repayment Performance

Loan default problem is unwanted things and strategy to any creditors, from banks

to financial companies When Loan default occurs, it can knock down any creditors, destroy the lending capacity of even very wealthy creditors It erodes benefit of banks and also erode belief though banking and financial system Banks and creditors will tighten credit policy and deny new applicants to get access to credit Moreover, the loan default problem means the loss of future credit, loss of potential deposit from society

By [28], A loan default problem occurs when a borrower fails to make a payment

on time (defaults on a payment of interest or principle) or they do not comply with

According to the Credit Institution Law of Vietnam No 47/2010/QH12 and Circular

No 39/2016- NHNN, Loan is the lending whereby a credit institution assigns or commits to deliver a sum of money to customers for use for a specific purpose within a certain period of time according to an agreement on the principle of reimbursement Loan can be categorzied basing on loan tenor, loan purpose, etc Related to business loan, there are 2 main types of loans as below:

a) Short term loan for working capital demand: A typical business cycle of each enterprise is normally less than 12 months, depending on business sector and scale

of each enterprise For instance, for an enterprise that provides service, it takes only around 1 month For an enterprise that focuses on trading, it takes from 2-3 months For a manufacturer, it may take longer from 3 to 5 months from the time when they buy material, to the time they sell and collect money from the buyer The business cycle is even longer with construction enterprises due to the specific characteristic

of this business sector It takes them 6-12 months, or even years to finish one business cycle Accordingly, the demand for working capital is always short term and less than or equal 12 months

c) Medium and long term loan for investing of fixed assets: Besides demand for working capital, enterprise also need medium and long term to buy or invest in factory building or fixed asset which is essential to medium and big enterprises Depreciation time will be several years, and the payback time should be relevant Therefore, medium and long term loans usually from over 12 months to 72 months (equal 6 years)

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With the scale and characteristics of MSEs which most focus on service, trading or small manufacturing small manufacturing sector, the demand of big amount for fixed asset is not too much or they can well arrange with a short term loan In this research, research subjective are short term loan under 12 months

2.2 Related Literature Reviews to the Variables Used in the Study

In this section, related literature reviews of scholars on MEs - borrower’s characteristics, Determinants of loan repayment performance, and Creditors’ method in the appraisal of clients are addressed The literature review is made with the objectives of identifying research/literature on micro-credit loan repayment in general and determinant a credit risk of MEs credit loan repayment on microcredit and savings institution, in particular, understanding the problem and identifying any research gaps in the area

Normally, key variables used as determinants of credit risks and loan repayment performance are divided into 3 main groups (Borrower characteristics, Business characteristics, and Loan characteristics) which are: Gender, Age, Educational level, Residence, Marital status, Business Sector, Total sale, Total income, interest rate, loan amount, Number of dependents, extra income, an extra loan from other banks and creditors ,etc…

Key variables used as determinants of credit risks and loan repayment can be categorized as Qualitative and Quantitative data, regarding their own characteristics Some popular and commonly used variables considered as key determinants of loan payment performance in related Literature Review are reviewed as below:

2.2.1 Gender

Gender (Male/female) is a demographic factor that is proved to affect the behavior

of each person, in many areas, and loan repayment performance is not an exception

In Vietnam, there haven’t been similar research on the effect of gender factor to loan repayment performance but as many experts and experienced loan appraisers, female usually makes a good impression and feeling to creditors and is expected to make the repayment duly because their characteristics are more gentle and exposed

to pressure –if any, which make them react more quickly with requirements of payment than male Moreover, Vietnamese women are believed to have the ability

to make savings and spend less money on entertainment activities than males, as well as have better skills in cash flow management – qualitative criteria to many banks and financial companies in Vietnam when they make a loan decision

2.2.2 Age

According to [1] Age was found to be a significant effect with the regression coefficient value of -0.024 and p-value of 0.0001 with a hazard ratio of 0.98

This finding indicates that:

Age of borrowers is expected to have the same direction, or positive impact on loan repayment, which means, the older the borrower is, the less probability of default his/her loan is It also means that younger clients would have 0.98 times faster of

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loan default than the older ones This can be explained by a truth that older age person normally is wiser and more experienced in making a decision, opportunity cost of all options, so that the default risk is lower thanks to both positive willingness to pay and ability to pay

Also, [2] found that Age of the borrower’s was found to be as expected, as positive impact on loan repayment, which means as age increased, the probability of being defaulter is decreased

In Vietnam, there hasn’t been yet research on effect of age factor to loan repayment performance but as many experts and experienced loan appraisers, older age are more likely to access credit from formal financial institutions than those with younger age Because they are wiser and better in making decision, management of their life, their financial status which are essential in creating willingness to pay of a debtor Also, the older the debtor are, the possibility that they are married or have been responsible for someone else is higher, which means

For sure, we are mentioning borrowers who meet the requirement of banks in term

of age Internationally, people are within labor age are more eligible for requesting a loan, in term of both health condition as well as ability to make and maintain income In Vietnam, only borrowers who are between 20 and 55 are accepted as a potential customer of banks and financial companies People over that age are not qualified for business loans, but retirement loan only

2.2.3 Marital Status

Marital status, includes 4 groups: single, married, divorced and widow However, in term of statistics and analytics for credit risk, it’s simplified into 2 main groups: married and not married

By [2], 78.94% of the borrowers who have defaulted problems are married, and 24.49 % are not yet married From this, we may conclude that married borrowers are easier to have bad debts than unmarried ones According to [3], it also seems

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true because married clients are likely to have larger families than the ones that are not married Single clients are therefore more likely to pay off their debts than the married ones

However, this is not entirely true because in general, the owner of an enterprise, though small, with an average age of 30, mainly gets married Sample of MSEs borrowers is different from a sample of consumption borrowers By [3] it’s found that marital status had the regression coefficient value of -0.257 and p-value 0.0001 with the hazard ratio of 0.77 The negative coefficient value means that the correlation between marital status and loan default problem was inverse direction From a study conducted at a Tunisian MFI, [13] it’s also concluded that being female and married is likely to increase the probability of loan repayment of borrowers

Also, from some journals on the internet, it’s stated that married debtors would repay their loan faster than the unmarried ones It is probably because the married ones will be more responsible and mature as debtors

In Vietnam, marital status is used in credit scoring of some banks and financial companies, but just as reference information Just a few creditors such as FE Credit (a famous financial joint-stock company in Vietnam), HDBank (a famous joint-stock bank of Vietnam), etc use demographic variables in their credit scoring system, especially the marital status of MSEs owners, hence, no data or literature review of marital status in Vietnamese research or journals But, as experts in credit approval, given the other conditions are the same, the married business owners have

a clearer commitment to making loan repayment

2.2.4 Educational level

Educational level refers to the highest level of schooling that a person has reached This variable is very meaningful determinants in assessing credit risk, especially of MSEs, because, to be the owner of an enterprise, a certain level of education and

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experience is required Professional background, experience, and ethics of the business owner will directly affect organizational culture as well as business results, usually in the same direction

As usual, the Education level will be divided into 4 main groups: Primary, Secondary, High, University, and above However, depending on the research target, education level can also be categorized as illiterate, primary, etc…Within the research of credit risk of MSEs borrowers, high school, diploma, and undergraduate are used because there are almost no illiterate borrowers among MSEs’ owners

By [9], Using the education level of elementary school as the control variable, the factor of education level had a significant effect on the default level on MSME credit at Bank XYZ By[2], it is shown by all dummy variables of education have a p-value of 0.0001 (less than the significance level of 0.05) The negative coefficient values on education level variable of junior high school, senior high school, diploma, and undergraduate show that clients with lower last education level had a faster default risk than the clients with higher

The educational level coefficient had a positive sign indicating that education has a direct relationship to the repayment rate and this showed that as level of education increases, borrowers enhance their ability to access, evaluate, and understand new production techniques The result of this study showed that the higher the literacy level of the clients, the higher will likely be non-default

They found that education is an important determinant of loan repayment An educated client can use modern technologies, perform farming activities based on cropping calendar, and manage resources properly

Education level is expected to be one of the factors influencing credit repayment because it affects one’s individual character The higher the education level, the wider the knowledge and insight owned

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2.2.5 Residence

By [13], The scholar analysis of credit risk on MEs credit loans in Kenya using survival analysis techniques, the variable of Residence is used because the area of research is quite large Based on the results calculated, the region with the highest percentage of default existed in Medan region with a percentage of 7% It became the basis why Medan was chosen as the reference region variable in the modeling

By [10], The results of Cox regression show that all dummy variables of the region had an effect on the level of default on MSME credit at Bank XYZ It can be seen from the p-value of 0.0001 (less than the significance level of 0.05) However, this may not entirely true because big cities have more MSEs than rural areas and small cities The competition level among MSEs and the demand for business loans in big cities is also higher than that in small cities Accordingly, if we just focus on the ratio of MSEs which got defaulted, it may not reasonable

In Vietnam, Hanoi and Ho Chi Minh city are the 2 biggest cities of Vietnam that are home to many MSEs And loans for MSEs also focus on these 2 cities The data for this research has been taken for the area of Hanoi and Ho chi minh city Besides, to make it precise, many other data should be taken in parallel with the variable of Residence Therefore, this variable will not be used in Research Instead, the variable of Housing will be used Detail of this variable will be mentioned later

2.2.6 Distance of clients from institution

[1] found that if the nearer the borrower is to institution increases, the higher the possibility of borrowers to repay their loan is However, [3] clearly showed that 69.16% of the defaulter respondent’s residence and businesses were near Harari MEs credit institutions, whereas 30.84% were not near to Harari MFI As a finding result, distance of borrowers from the offices doesn’t affect the loan repayment rate

of borrowers

Hence, distance of clients from institution is not really a good variable as determinants of credit risk of loan repayment performance It is just helpful when the loan already get overdue and come to a bad debt and require field collection For prediction of the default risk, it’s not helpful and not used in this Research

2.2.7 Business sector

By [1], a survivor function curve for construction sector experienced the possibility

of faster default with the percentage of 23%, followed by transportation, storage, and communication sector with 19.4% and small manufacturing industry sector with 19.2% [26] carried out a study in order to determinants of access to formal financial sources of micro and small enterprises by using a two binary logistic regressions model According to his finding, “MSEs engaged in small manufacturing and construction sector easily access credit from banks and MFIs as compared to those engaged in trading and service sector”

The reason for this might be enterprises in the asmall manufacturing and construction sector tend to have more fixed asset which can be used as collateral for loan with big amount However, MSEs, especially in Vietnam, almost are business units in trade, service and light industry Their fixed asset is not at very big value Even the owner havn’t had house of their own Therefore, this may not be true in Vietnam

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As experts in banks, and data from Agribank of Vietnam, small manufacturing sector is not a good sector to commercial banks because of poor loan repayment performance By unofficial data of this bank, the level of default for each sector are small manufacturing sector, small manufacturing , service industry, from high to low, respectively The trade sector enjoys the lowest ratio of default This may come from a truth that the benefit ratio and cash flow of MSEs in trade sector are generally better than MSEs in heavy industry or MSEs in small manufacturing sector where business cycle is longer than that of trade sector Due to characteristics

of Vietnam, MSEs almost do not include small manufacturing sector because that mainly taken care by big state owned enterprises

This variable is absolutely a key factor affecting default risk of borrowers and is used in this Research, and divided into 4 main groups: Manufacturing, Trade, Service and Construction

2.2.7 Collateral

Collateral is the asset that borrower use to secure for their loan In case they cannot pay the loan, the bank or the creditor will take that asset, sell it and compensate for the loan and other cost that they have to incur from the defaulted loan

According to [2] it is revealed that, the causes of default that is identified were inadequate loans not requiring collateral Also it was revealed that there is a default problem as 80% of loan officers agreed to that and there are delays on payment of loans

In Vietnam, the similar phenomena happen, loan repayment rate is significantly related with type of collateral offered because collateral usually is house or car and

if the borrowers don’t want to lose their house, they will try their best to pay back the loan However, as mentioned, as Vietnamese expert, collateral does affect the willingness to pay of borrowers and probability that banks can get money back thanks to the seize of borrowers’ asset MSEs usually don’t have big value asset of their own Even he MSEs owners don’t have valuable assets to secure for the loan

This can be true or false in two ways: if the loan amount is suitable with capital demand and cash flow of the borrower, then it is the best option despite of big or small amount If the loan is not suitable, larger or smaller than borrower’s demand and cash flow, this will be very dangerous because borrower may use the capital in the wrong way and, or cannot make enough money to pay for the creditors

After investigating, it’s found that loan amount is calculated according to demand and financial statement of the borrowers Many loans of small amount still get defaulted and many loans of big amount do not get defaulted, so this variable is not chosen to investigate as one of factor influencing loan default

2.2.9 Repayment period

Repayment period equals loan term which means the total months from the time of disbursement to the time the borrower has to pay all the loan amount Loan period is the due time for the debtors to repay the primary installment along with the interest Loan period will affect the amount of installment and interest that will be paid every

According to [3] The results show that when the average loan period increased, the clients who repay the installment in a long period would have a 1 time faster default risk than the ones who repay in a short period

This is true in terms of risk management, because the longer the period is, the risker the investment (loan is understood simply an investment where interest is the extra

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value earned for the investor) is This can be easily understood because when time changes, there are many events, force majeure incidents that may happen and affect business activities For i.e, Covid has made many MSEs go bankrupt, many employees lose their job and can’t pay their debt, etc

However, MSEs credit in Vietnam almost is short term loans which finance working capital demand of MSEs Short term loan is loan with repayment period under or equal 12 months Therefore, Repayment period is not a necessary variable and used in this research

2.2.10 Interest rate

[11] found that, the causes of default that are identified were inadequate high interest rate This means that if a borrower has to pay too much for a loan that he takes, the probability of repayment is low It can be simply explained like this: an MSEs borrow money to put into his business, to earn benefit at a certain rate, let’s say 20% If it has to pay an interest rate of 30%, which is higher than the benefit ratio that the money it borrowed can make, the cash flow will be inadequate and there will be no money to pay for the creditor

[2] also use interest rate is one of variables to analyze the probability of default risk However, in this Research, and in Vietnam, a short term loan is given to borrowers with fixed interest rate, which means all MSEs with given qualifications will enjoy similarly In a few Vietnamese banks and financial companies such as FE credit, MSB, interest rate isn’t used as criteria in their scoring system Hence, this variable

is not used in this research

2.3 Cox Regression Model

To analyze credit risk in general and default ratio, researchers can use many different Models, from non-parametric models such as Life tables, Kaplan Meier, to semi-parametric models such as Cox Regression Model, or other more complicated parametric models Among these models, Cox regression model is known as a

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useful tool for survival analysis which is often used in the medical area to define the time that patients may get covered from the disease, or estimate the time duration until an event Recently, the application of Cox Regression Model is more popular

in many other fields, including risk management and credit risk modeling

By [7], Cox Regression Model is a methodology created by Doctor Cox, which helps us to predict the probability/outcome of some event which can happen at a certain point of time, instead of just providing outcome (yes/no, 1/0, etc…) of some event as normal Linear Regression

By [15], when comparing between Logistic Regression Model and Cox Hazard Model, it can be seen that the two models have a very similar performance in the Gini coefficient on the development (training) and comparison (validation) sample, but the Cox model shows a little more stable Moreover, Cox model also helps to predict time to default, which is not available in other models

Table 2.1 Comparison of models on the random sample

Also, by [17], Cox’s proportional hazards modeling is applied in the generation of this model since it’s the most suitable for survival data when proportional hazards have been proved in various groups Therefore, to predict the probability of Loan Default – a situation that a debtor can’t make the repayment duly at a given time during the loan period and the time to default as well as the effect of covariates, Cox Regression Model is the most suitable tool and is used in this Research By [19], it’s agreed that Time to default is key factors in credit scoring

Also, by [21], different from Kaplan Meier model which only allows 1 variable, Cox’s regression allows several variables to be taken into account and tests the independent effects of these variables on the hazard of the event

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2.4 Research Gap

2.4.1 Gap in Research Model

Up to now, in Vietnam, there are several research on credit risk and credit scoring, but no research on the similar topic using Cox Regression Model Binary regression model is used in some research, but this model provides only Yes/No result on the probability of default

The Cox Regression procedure is useful for modeling the time to a specified event, based upon the values of given covariates One or more covariates are used to predict an event, such as failure to pay, time to default, etc… The central statistical output is the hazard ratio Data contain censored and uncensored cases Similar to logistic regression, but Cox regression assesses the relationship between survival time and covariates

To deeply dig into this topic and come out with the most proper determinants to loan repayment performance or default ratio, this research will use the Cox Regression Model which can predict both hazard ratio of default and time to default

2.4.2 Gap in Variables

Variables in Section 2.2 are the most commonly used variables by international research Basing on characteristics of Vietnam economy and practice, as well as observation by experts in banking and finance sector, 6 following variables will be added to the list to investigates determinants which affect loan repayment performance, including Years in business, Percentage of share owned by owners, the stability of turnover in 6 latest months, Availability of Digital Sale channel, Housing and Number of clients Details of new added variables are as below:

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2.4.2.1 Years in business:

Number of years that MSEs and its owners have worked in the field, to the time MSEs – same business sector, apply for a loan This variable is used in the Research being observed from the fact that the more experience the owner has in the sector that his enterprise has business in, the higher probability the borrower can repay the loan It is explained that if the owner has more experience, he or she will have a large network with people in the field, mindset, and experience to build a suitable, smart, and realizable strategy and execution plan for his/her enterprise He or she also understands his/her position and competitors in the segment that they focus on Actually, in Vietnam, this variable is used by quite many banks and financial companies when they make credit assessments to offer a loan or not to borrowers, not only individual but also enterprise customers However, these creditors haven’t applied this variable into their scoring system or model, at least as searchable documents and unofficial information by experts in the field Similar research and journals on the effect of this variable to loan repayment performance are not available currently

2.4.2.2 Percentage of ownership

Percentage of share that the owner has in the company This is a variable to measure the influence of the main owner to the enterprise – or MSEs borrowers In small and micro enterprises, and household business, personality, and background of the owner strongly influence to strategy, efficiency and culture of the MSEs he or she own

There are two types of private enterprises in Vietnam: limited liability company (Ltd) and joint-stock company (JSC) and these 2 types of companies allow many shareholders to put money in the company The higher the number of shareholders

is, the less percentage of share each shareholder owns, in general However, the shareholder who takes the highest percentage of share, usually also the founder, will

2.4.2.3 The stability of turnover in 6 latest months: Measurement of the stability

of MSEs’ turnover within 6 latest months

For young enterprises such as MSEs, it’s not very easy to maintain a stable turnover flow and cash flow accordingly They have to try their best to adapt with the customer and market, learn to survive, and grow in a competitive market If their turnover is not stable, it may lead to the loss of liquidity and hence, MSEs will fail

in making loan repayment Experts in Vietnamese banks and author’s own experience, this is an important factor Though several banks in Vietnam also use this as a requirement to filter qualified clients, they haven’t made a scoring system with this factor and make the measurement of how it affect default risk ratio

In this Research, there are 2 value for this variable: difference > 30% compare to mean value, difference ><30% compare to mean value

2.4.2.4 Availability of Digital sale channel: measurement if MEs use digital sale

channel besides traditional sale channel (Direct sale)

This is expected to heavily affect their business in the era of technology In this era

of 4.0 technology Revolution has changed our life and the way we live and work People don’t need to go to see each other frequently They can also stay at home and work, make transaction, do shopping, etc…This is even more clearly when Covid disaster came and people have to activate the mode of work from home

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Housewife do shopping through e commerce and make payment by e-wallet Then

if MSEs just have a traditional sales channel, they can’t catch up with this change and quickly lose their market share to competitors who are pioneer and already set

up digital sales channel This can be easily seen through the success story of Amazon Retailer or Aeon Mall, who have survived and get amazing growth rate over other traditional retailers

In the research, there are 2 value for this variable: Yes/No

2.4.2.5 Number of clients: Number of clients that MSEs has at the time it applies

for a loan

The more clients it has, the less dependent on a few big clients, which means the risk is diversified from late payables or decrease in turnover due to the withdrawal

of a certain number of customers

This is strongly agreed by appraiser and approver from banks, as well as debt collection experts

2.4.2.6 Housing

Owners of MSEs almost does not have a house or valuable house of their own because of young age Also, many of them put all money they have into the business and live in a rent house This is very normal in Vietnam

However, if they already have a house or in the process of buying a house for their own, things are different This means they already have certain success and can earn money from the business

This variable has two value: Owned house and Rent house which can influence and indicate the ability to make repayment of borrowers

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CHAPTER THREE: DATA AND METHODOLOGY

3.1 Description of the Study Area

The study was conducted in the capital of Vietnam - Hanoi Hanoi is located in the North of Vietnam and has been chosen because it is a typical city with many types

of enterprises and household businesses with an estimated population of 8.1 million

as of 2019 with 12 districts, 17 rural districts and 1 town

According to a ranking recently by PricewaterhouseCoopers, Hanoi has the highest human development Index among the cities in Vietnam and will be the fastest-growing city in the world in terms of GDP growth from 2008 to 2025 The relevant data is collected through Lendbiz– a P2P lending company mainly and VPBank - partially with borrowers doing business in Hanoi and neighboring areas

3.2 Data Description

The data used in this study was both quantitative and qualitative data types The dataset was collected from secondary data sources of Lendbiz and VPBank Secondary data was collected from reports, unpublished documents, and data sources of 200 borrowers who took loans beginning from January 2018 and ending December 2019 with loan term of 10 months to 12 months

The data consisted of uncensored data (customers who had a status of repayment failure until the end of this study period) and censored data (customers whose credits had been paid off and customers whose credit had not been completed but still had good repayment status until the end of the study period

The data set was contained the client description variables, personal information about the MSEs’ main owner (age, gender, educational level, marital status, housing, years in business), the MSEs itself (business sector, availability of digital sale channel, number of clients, the stability of turnover within 6 latest months) The data was collected by appraisal credit officer and coded, cleaned It was