COLLEGE OF BUSINESS AND ECONOMICS
DEPARTMENT OF ACCOUNTING AND FINANCE
DETERMINANTS OF NON-PERFORMING LOANS (NPLs)
IN DEVELOPMENT BANK OF ETHIOPIA
THESIS PAPER SUBMITTED TO THE DEPARTMENT OF ACCOUNTING AND FINANCE FOR THE PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF DEGREE OF MASTER OF SCIENCE IN ACCOUNTING AND FINANCE (MSC.)
WALELGN MESFIN GESSESE
UNDER THE GUIDANCE OF
Dr.P.ATHMA KARAN REDDY
CO – ADVISOR
MR. KINFE YOHANS
Walelgn Mesfin Gessese, hereby I declare that the thesis entitled “Determinants of Nonperforming loan in Development Bank of Ethiopia” submitted by me for the award of the Degree of Masters of Science in Accounting & Finance, at Wollo University is original work and it hasn’t been presented for the award of any other Degree, Diploma, other similar titles of any other university or institution and that all sources of materials used for this thesis have been properly acknowledged.
Name: Walelgn mesfin
Date: May 2018
Wollo University, Wollo, Ethiopia
This thesis has been succumbed to Wollo University College of Business and Economics for investigation with my approval as a university advisor.
Wollo University, Dessie
STATEMENT OF CERTITICATE
This is to certify that the thesis prepared by walelgnmesfin, entitled: Determinants of Non-performing loans: Empirical Study on Ethiopian commercial Banks and submitted in Partial fulfillment of the requirements for the degree of Degree of Master of Science (Accounting and Finance) complies with the regulations of the University and meets the Accepted standards with respect to originality and quality.
Signed by the Examining Committee:
Examiner_____________________ ( ) Signature_____________ Date____
Examiner _____________________ ( ) Signature__________ Date _____
Advisor; Athma Reddy (PhD) Signature__________ Date
Chair of Department or Graduate Program Coordinator
First, I would like to extend my deepest gratitude to the almighty God who gave me Strength to undertake this project. Next, I would like to extend my deeper gratitude to my advisor, Dr.P.Athma Karan Reddy and coo advisor Mr. Kinfe yohanes for their vital comments, encouragements and guidance at various stage of the study. My sincere thanks go to all members of my family for their love and support during the entire study period and special gratitude also extended to Development Banks of Ethiopia management and staff members for their cooperation in providing me all the necessary data required of the stud, especially ato anmaw gedamu who helped me in any form of assistance.
Finally, I would like to thank for Wollo University and all my fellow Accounting MSc students of whom we struggled together.
AMCs -Asset Management Companies
BIS -Bank for International Settlements
CSA-Central Statics Agency
DBE- Development Bank of Ethiopia
ERCA-Ethiopia Revenue ; Custom Authority
FDRE-Federal Democratic Republic Of Ethiopia
FIC-Financial Intelligence Center
IAS-International Accounting Standard
IASB-International Accounting Standard Board
IIF-Institution of International Finance
IMF-International Monetary Fund
KYC-Know Your Customer
MoFED-Ministry of Finance ;Economic Development
NBE-National Bank of Ethiopia
NPLs-Non Performing Loans
PRLR-Project Rehabilitation ; Loan Recovery
LIST OF TABLES
Table -3.1.Variables and Their Sign.
Table -4.1: Descriptive Spastics of Variables
Table- 4.3.: Unit Root Test of Variables at Level and Difference Tests
Table -4.4: Selecting Order of VAR
Table -4.5: Number of Co-Integration Vector Based On Statics
Table- 4.6 Number of Co-Integration Vector Based On Statics
Table-4.7: Piracies Granger Causality Test
Table-4.8: Heterosdasticty Test
Table-9: Serial Correlation Lm Test
LIST OF FIGURES
Figure_1: Conceptual Framework
Figure_2: Normality Test of Residuals
Figur_3; Residual Graphs of NPL
Over the last few years, the literature that examines NPLs has expanded in line with the interest afforded to understanding the factors responsible for financial vulnerability. The study aimed to investigate the explanatory power of variables as determinants of NPLs.
Non Performing Loans is the most important issue for banks to survive. Since long, the argument has been that different factors affect the nonperforming loans and the existing literature on macroeconomic variables suggests that many macroeconomic variables do strongly influence them. This study used time series data of NPLs and nine independent variables, which are macro-economic factors and bank specific factors and over the period of 1992-2017, and multivariate time serious model of vector auto regressive and vector error correction model of Johansen approach was used to test the explanatory power of variables as determinants of NPLs.The study adopts a quantitative methods research approach by combining documentary analysis (structured review of documents). The study adopted the Descriptive and explanatory research Design and applied multiple regression models using Eviews8 version on secondary data to determine the relationship between causes of Non Performing Loans in Development bank of Ethiopia.
Key words: NPLs, Determinants, Development Bank of Ethiopia,
Table Of Contents
LIST OF TABLES…………………………………………………………………………………………….0
1.1BACK GROUND OF THE STUDY 1
1.2.BACKGROUND OF THE ORGANIZATION 4
1.3.STATEMENT OF THE PROBLEM 5
1.4.RESEARCH QUESTIONS (RQ) 7
1.5.HYPOTHESES OF THE STUDY (HP) 8
1.6.OBJECTIVES OF THE STUDY 8
1.6.1GENERAL OBJECTIVE 8
1.6.2SPECIFIC OBJECTIVE 8
1.7.SIGNIFICANCE OF THE STUDY 9
1.8.AREA OF THE STUDY 9
1.9.SCOPE THE STUDY 9
1.10.LIMITATION OF THE STUDY 10
1.11.STRACTURE OF THE STUDY 10
1.13.ETHICAL ISSUES 11
REVIEW OF LITERATURE 11
2.1.THEORETICAL REVIEW OF NON-PERFORMING LOANS 12
2.1.1.DETERMINANTS OF NON-PERFORMING LOANS 23
2.2.2.FINANCIAL PERFORMANCE 27
2.2.EMPIRICAL LITERATURE REVIEW OF NPL 28
2.2.1.STUDIES IN ETHIOPIAN CASE 28
2.2.2.RELATED LITERATURE REVIEW IN OTHER COUNTRIES 32
2.2.3.CONCLUSIONS OF THE REVIEWS AND RESEARCH GAP 35
2.3.RESEARCH GAP 37
3.1.RESEARCH DESIGN 38
3.2.RESEARCH APPROACH 38
3.3.DATA SOURCE AND COLLECTION 38
3.4.METHODS OF DATA ANALYSIS 39
3.5.VARIABLE SPECIFICATION AND RESEARCH QUESTIONS 39
3.5.1.DEPENDENT VARIABLES 39
3.5.2.INDEPENDENT VARIABLES 39
3.6.RESEARCH MODEL SPECIFICATION 43
3.6.1.JOHANSEN APPROACH 44
3.6.2.UNIT ROOT TEST 44
3.6.3.CO-INTEGRATION TESTS 44
3.6.4.ERROR CORRECTION MECHANISM AND GRANGER CAUSALITY 45
3.7.CONCEPTUAL FRAMEWORK 46
ESTIMATIONS OF THE MODEL AND ANALYSIS OF RESULTS……………………….47
4.1.DATA DESCRIPTION 47
4.2. TESTS OF STATIONARY 48
4.2.1. UNIT ROOT TEST 48
4.2.2.CO-INTEGRATION AND ERROR CORRECTION MODEL 49
4.2.3.ESTIMATES OF LONG RUN AND ERROR CORRECTION MODEL 52
4.2.4.GRANGER CAUSALITY TEST 53
4.2.5.DIAGNOSTIC TESTS 54
4.3.SERIAL CORRELATION LM TEST 54
4.3.1.HETEROSKEDASTICITY TEST: 55
4.3.2.TEST FOR NORMALITY 55
CONCLUSION AND POLICY IMPLICATION…………………………………………..57
5.2.RELATED POLICY IMPLICATIONS (RECOMMENDATION) 59
1.1 BACK GROUND OF THE STUDY
Non-performing loans, which are not only damaging the banking sector but also hampering the economy as a whole. The volume of non-performing loan is rising every year as we can see under developing countries. In order to reduce non-performing loans it is necessary to find the root causes of these loans.
A loan that has in default for 90 days or 3 month called Nonperforming loans (NPL’s), these loans caused by non-payment or failure in payment, although it relies on the agreement. Continuously increasing figures in nonperforming loans bringing threats in Banking Sector. The Banks are part of financial institutions; it is their function to provide funds against of collateral or non-collateral security which conversion of assets from excess to shortage of amount in economy. It is risky in treating or dealing effectively to accomplish their task, which is task oriented. Although cash is a part of an assets, when cash or loan could not be covered, then it effect on liquidity risk and credit growth, which puts the bank into trouble. The high borrowers or long terms borrowers are expected to be defaulters, so the long term or big borrowers should be treated carefully. Banks must have strategy to reduce NPL’s. The banks found with highest significance in nonperforming loans are Public sector Bank among all banking industry. Nonperforming loans brings an impact on operating risk, credit risk, monetary policy, market risk, liquidity risk, debt/equity risk, interest rate risk, reputation risk, earning risk, legal risk ; solvency risk. Banks must have well-structured strategy for the recovery of loan and the Bank must design or apply its own strategy for the recovery of loans.
Advances and loans are the most important mechanism of Banks assets, these loans could be a threat for a Bank. Several Banks has been solvent due to fail in recovery of loans, it is just because of poor strategies and managing. Banking industry playing a vital role in the economic development of a country. Earlier studies proved that various banks have found obstacle to meet their objectives due to inefficient of performance causes solvency.
Lending is not an easy task for banks because it creates a big problem, which is called non-performing loans.
According to the International Monetary Fund (IMF, 2009), a non- performing loan is any loan in which interest and principal payments are more than 90 days overdue; or more than 90 days worth of interest has been refinanced .On the other hand the Basel Committee1 (2001) puts non performing loans as loans left unpaid for a period of 90 days.
According to NBE (National Bank of Ethiopia), the standard loan classifications are defined as follows:
Passed: Loans paid back on time as per the loan contract agreement.
Special Mention: Loans to incorporations, which may get some trouble in the repayment due to business cycle losses.
Substandard: Loans whose interest or principal payments are longer than three months in
Arrears of lending conditions are eased. The banks make 20% provision for the unsecured portion of the loans classified as substandard;
Doubtful: Full liquidation of outstanding debts appears doubtful and the accounts suggest that there will be a loss, the exact amount of which cannot be determined yet. Banks make 65%provision for doubtful loans;
Virtual Loss and Loss (Unrecoverable): Outstanding debts are regarded as not collectable,
Usually loans to firms, which applied for legal resolution and protection under bankruptcy laws. Banks make 100% provision for loss loans.
Non-performing loans comprise the loans in the latter three categories, and are further
Differentiated according to the degree of collection difficulties.
Under the Ethiopian banking business directive, non-performing loans are defined as “Loans or Advances whose credit quality has deteriorated such that full collection of principal and/or interest in accordance with the contractual repayment terms of the loan or advances in question” National Bank of Ethiopia (NBE, 2008).
The causes for loan default vary in different countries and have a multidimensional aspect both, in developing and developed nations. Theoretically, there are so many reasons as to why loans fail to perform. Some of these include depressed economic conditions, high real interest rate, inflation, lenient terms of credit, credit orientation, high credit growth and risk appetite, and poor monitoring. Over the years, there have been an increased number of significant bank problems in both, matured as well as emerging economies (Brown bridge and Harvey, 1998). Bank problems, mostly failures and financial distress have afflicted numerous banks, many of which have been closed down by regulatory authorities Non-performing loans were cited as the major common problem that was faced by most Banks. Lending is one of the main activities of a bank and interest income makes up the lion share of profit. To achieve the objectives of circulating more and financial resources to meet the increasing demand for credit and to keep the Bank in sound financial position, the loans extended to various sectors of the economy must be recovered in full. Both the principal, which is used for re-lending as well as the interest to meet the operating costs, must be recovered. However, for the last many years the Bank’s loan repayment performance has been very low due to various factors. These factors may explain among others the loan repayment behavior of borrowers and lending behavior of the Bank. In the last few decades, we can see many banking failures in all over the world (Brown bridge and Harvey, 2012), and due to these banking failures, regulatory authorities (Brown bridge, 2012) have closed many banks. These banking failures negatively affect the economy in many ways, firstly these banking failures causes banking crisis by harming the banking sector, secondly it also reduces the credit flow in the country, which ultimately affects the efficiency and productivity of the business units (Chijoriga, 2013; Brown Bridge and Harvey, 2012).
In the case of Ethiopia, banks, insurance companies and micro-finance institutions are the major financial institutions. This sector is closed for non-Ethiopian citizens. Proclamation No.592/2008 (FDRE, 2008) does not permit foreigners to own and operate banks in Ethiopia. There is a relatively favorable environment for banking industry and other financial institutions in Ethiopia. An efficient and well-functioning financial sector is essential for the development of any economy, and the achievement of high and sustainable growth. One of the indicators of financial sectors health is loan qualities. Most unsound financial sectors show high level of non- performing loans within a country.
The banking sector plays an important role as a financial intermediary and is a primary source of financing by mobilizing and channeling the scarce resources of the economy. As financial intermediaries of the economy, the banking industry was significantly affected by the crisis. Non-performing loans (NPls) began to rise as deteriorating finances of distressed borrowers and high interest rates impaired borrowers’ ability to service loans. In addition, the collapse of financial and property asset values substantially reduced the value of the collateral for many bank loans. As a result, most financial institutions experienced erosion in profits. The financial institutions’ capital base was affected by increased losses from loan defaults, requiring them to seek recapitalization. As non-performing loans (NPls) continued to rise, the banking system faced the risk of systemic failure. Non-performing loans (NPLs) are a worldwide issue that affects the stability of financial markets in general and the viability of the banking industry in particular. The non-performing loans (NPLs) problem is often cited as one of the potential risks that may cause economic and financial instability.
Development and Commercial banks are the dominant financial institutions in most economies and it plays a critical role to emerging economies where most borrowers have no access to capital markets. Well-functioning banks accelerate economic growth, while poorly functioning banks are an impediment to economic progress and aggravate poverty. Loans therefore represent the majority of a bank’s asserts.
Customers get dissatisfy while communicating the information during the request or claim that pressurize the recital or routine of the bank. The customer while submitting loan request should provide correct information. The procedure or process of granting loans is a tough step, so the bank should take this step carefully. The management should be highly effective during operations all the activities should be performed with proper documentations and according to the agreement with respect to reach competence.
1.2 BACKGROUND OF THE ORGANIZATION
Development bank of Ethiopia (DBE) is one of the oldest and specialized financial institution (banks) in the country, which was established in 1901 E.C, it accounts more than 100 years and engaged in providing short, medium and long-term development credits by financing viable projects from the priority areas of the government. DBE’s distinguished feature is its “project” based lending tradition. Project financed by the Bank are carefully selected and prepared through appraisal, closely supervised and systematically evaluated. It mobilizes funds from domestic and foreign sources. As a strategic government owned institution, DBE is uniquely positioned in the financial industry as it is empowered to extend both development finance and short-term working capital loans as a package (DBE’s Loan Manual, 2014). DBE provides development loans; higher purchase to borrowers based on the government priory area which are manufacturing, agro-processing industries, mining or extractive industries, commercial agricultural, Tour industry ; construction industry projects by mobilizing local funds and other development loans provided by international organizations for development purpose to helps the country’s economy by providing financial service to the investors constitute the major sources of its income.DBE has 13 Districts,110 branches throughout the country. The main target of the bank is not profitable organization like commercial banks rather support the countries development by providing investment loans throughout the country at low interest rate with technical support by professional staffs without held collateral asset outside the project as we compared to other local banks. This study will be conducted on determinants of nonperforming loans in DBE.
1.3 STATEMENT OF THE PROBLEM
A loan on which the borrower is not making interest payments or repaying any principal. At what point the loan is classified as non-performing by the bank, and when it becomes bad debt, depends on local regulations. Banks normally set aside money to cover potential losses on loans (loan loss provisions) and write off bad debt in their profit and loss account. In some countries, banks that have accumulated too many NPLs are able to sell them on – at a discount – to specially established asset management companies (AMCs), which attempt to recover at least some of the money owed.
Problems of Nonperforming Loans (NPLs) gained increasing attentions in the past few decades. It is critical issue for every bank to manage bad loans. Many countries are suffering from Nonperforming Loans (NPLs) in which banks are unable to get profit out of loans.
High level of nonperforming loan is linked with banks failures and financial crisis. Failure in one bank might lead to run on bank, which in turn has contagious impact affecting the whole banking industry and other parts of the world. Regular monitoring of loan quality, possibly with an early warning system capable of alerting regulatory authorities of potential bank stress, is thus essential to ensure a sound financial system and prevent systemic crises.
In Ethiopian context, the Banks in the country are required to maintain ratio of their non-performing loans below five percent (NBE, 2008). Despite this, which is relativity very high when compared with the set threshold or the industry average.
Though, there are a number of studies that are conducted at a global level to examine the determinants of NPLs, most of the studies were made with reference to developed countries like Italy, Spain, Greece, Europe and USA and the like. This means, they do not explain the issues for emerging market particularly for Ethiopian case. The operation of modern and organized financial institution is the most crucial part for any country to ensure the economic growth and development. In case, financial sector of Ethiopian economy is dominated by banking sectors. Therefore, it is important to examine their asset quality. Further, by having a lot of literature on the determinants of NPLs of banks across worldwide, it is important to examine in Ethiopia case. This is due to the fact that, it is difficult to generalize about the NPLs for the developing economy based on the result of developed economy without making any research. Besides, since the majority of bank assets are hold by loans, unless the determinants of NPLs are visualized to enhance the quality of asset, it is hard for the survival the banking sectors.
Generally, the basic motive for this study is that, different studies were done in Western Europe and East African countries (Saba et al. (2012), Louis et al. (2010), Badar and Yasmin (2013) and Moti et al. (2012). However, the results of those studies were inconsistent. This inconsistency of results might be attributable to the method of data analysis used by different researchers and difference in the economic condition of the countries in which banking sectors are operating. For instance; the study of Saba etal. (2012) on the title of “Determinants of Nonperforming Loan on US Banking sector” found negative significant effect of lending rate and positive significant effect of real GDP per capital and inflation rate on NPL via OLS regression model. Similarly, the study of Louzis et al. (2010) examined the determinants of NPLs in the Greek financial sector using dynamic panel data model and found as real GDP growth rate, ROA and ROE had negative whereas lending, unemployment and inflation rate had positive significant while loan to deposit ratio and capital adequacy ratio had insignificant effect on NPLs. However, Swamy (2012) examined the determinants of NPLs in the Indian banking sector using panel data and found as GDP growth rate, inflation, capital adequacy and bank lending rate have insignificant effect on NPLs. Shingjergji (2013) who conducted study on “the impact of bank specific factors on NPLs in Albanian banks system” utilized OLS estimation model and found as ROE have significant negative on NPLs. However, Ahmad and Bashir (2013) conducted a study on the “Bank Specific Determinants of Nonperforming Loan” by static panel data model and found as ROE
has insignificant negative association with NPLs. Makrietal.(2014) identify the factors affecting NPLs of Euro zone’s banking systems through difference Generalized Method of the Moments (GMM) estimation. Accordingly, they found, as ROA did not show any significant impact on NPL ratio. However, Selma and Jouini (2013) conducted a study on Italy, Greece and Spain for the period of 2004-2008 via panel data model and found a significant negative effect of ROA on NPLs. similarly, Boudriga et al. (2009) conducted a study on the title “Problem loans in the MENA countries via random-effects panel regression model and found as ROA has significant negative effect on NPLs..
Wondimagegnehu (2012) in his study “determinants of NPLs on commercial banks of Ethiopia” revealed that underdeveloped credit culture, poor credit assessment, aggressive lending, botched loan monitoring, lenient credit terms and conditions, compromised integrity, weak institutional capacity, unfair competition among banks, willful defaults by borrowers and their knowledge limitation, fund diversion for unexpected purposes and overdue financing has significant effect on NPLs. Conversely, the study indicated that interest rate has no significant impact on the level of commercial banks loan delinquencies in Ethiopia
Mitiku (2014) studied the “Determinants of Commercial Banks Lending: Evidence from Ethiopian Commercial Banks using panel data of eight commercial banks in the period from 2005 to 2011 with the objective of assessing the relationship between commercial bank lending and its determinants (bank size, credit risk, GDP, investment, deposit, interest rate, liquidity ratio and cash required reserve). Based on seven years financial statement data of eight purposively selected commercial banks and using Ordinary Least Square (OLS) technique, the study found that there was significant relationship between commercial bank lending and its size, credit risk, gross domestic product and liquidity ratio. While interest rate, deposit, investment, and cash reserve required do not affect Ethiopian commercial bank lending.
In view of the above discussions, numerous studies were conducted on the determinants of Non-performing loans. Most of these studies focused on Bank specific and Macro-economic determinates of NPL with specific branches. However, in the previous empirical analysis no study has been conducted in all branches of DBE, most of the studies were focused on other countries; rarely study in Ethiopia, no more research has been conducted in Development Bank of Ethiopia (DBE) with all branches in particular, and increases the NPLs amount from year to year above the threshold of NBE (5%) which is the vulnerable issues in financial institution.
Therefore, this study expects to fill the gap by assessing the association between bank, macro and customer-specific factors and level of nonperforming loans (NPLs).
1.4 RESEARCH QUESTIONS (RQ)
In line with the broad objective highlighted above, the following three research questions were formulated:
1. What are the main determinants of NPLs?
2. What are the major factors affecting Nonperforming loans of DBE?
3. What policy measures the bank’s management that would help improve the NPLs Status must undertake?
1.5 HYPOTHESES OF THE STUDY (HP)
According to the result of data analysis, the hypotheses of this study formulate by referring the existing theories and past Empirical studies that have been conducted on the determinants of bank’s NPLs. The hypotheses of this particular study intended to catch the determinants of NPLs Quantitatively through structured review of documents. In line with the broad objective of the study, the following nine hypotheses were formulated.
HP1: There is a significant positive relationship between loan growth of a bank and Bank’sNPLs.
HP2: There is a significant negative relationship between financial performances of a bank and bank NPLs.
HP3: There is a significant negative relationship between credit follow up of a bank and bank’s NPLs.
HP4: There is a significant negative relationship between GDP growth and NPLs.
HP5: There is a significant negative relationship between real interest rate and NPLs.
HP6: There is a significant negative relationship between inflation and NPLs.
HP7: There is a significant negative relationship between real exchange rate and NPLs
HP8: There is a significant positive relationship between unemployment rate and NPLs
1.6 OBJECTIVES OF THE STUDY
1.6.1 GENERAL OBJECTIVE
The general/overall objective of the study is to investigate the determinants of non-performing loans in Development bank of Ethiopia.
1.6.2 SPECIFIC OBJECTIVE
1. To identify the main determinants of nonperforming loan in Development bank of Ethiopia.
2. To determine the major factors affecting non performing loan
3. To indicate the main solutions/policy implication for reduction of non-performing loan in Development bank of Ethiopia.
1.7 SIGNIFICANCE OF THE STUDY
The recent global financial crisis and the subsequent recession in many developed countries have increased households and firms’ defaults, causing significant losses for banks. This calls for regular monitoring of loan quality, possibly with an early warning system capable of alerting regulatory authorities of potential bank stress to ensure a sound financial system and prevent systemic crises.
Prudent risk management, with a special emphasis to credit risk is pivotal. To put in place adequate credit management tools, understanding factors that contribute to the occurrence of bad loan play a crucial role.
This study thus will help Ethiopian banks in general and DBE in particular to get insight on what it takes to improve their loan qualities and to examine its policy in banking supervision pertaining to ensuring asset quality banks maintain. In addition, the study will also contribute to the existing body of knowledge regarding the determinants of nonperforming loans and motivate further research generally for other banks and specificity on development banks.
In addition, this study has significant to the Bank, to detect and identify the major problem of macro variables to contribute for NPLs. The research done will enable the bank to find the right strategy to overcome the problem and the possible actions to be taken by development bank of Ethiopia to sustain their profitability, sustainability and support the country’s economic development.
1.8 AREA OF THE STUDY
The area of this research is Ethiopian governmental bank, which is development bank of Ethiopia on determinants of nonperforming loan for all branches.
1.9 SCOPE THE STUDY
This research focus in development bank of Ethiopia on determinants of nonperforming loan. Besides, the data will be used in the study covered the period of 26 years data in DBE ; other sources. The study is conduct to look at determinants of NPLs, which is focuses more on the bad loans, the loan that is unable to be paid bank to the bank by the customers.
To undertake research study in the country level of all banks is difficult to the researcher due to time and financial constraint as the result this study limited to development bank of Ethiopia and not included other governmental and private bank of Ethiopia. Development bank of Ethiopia covers all regional states and city administration of our country Ethiopia.
1.10 LIMITATION OF THE STUDY
Upon conducting this research, the researcher faces several limitations that acted as barriers to carry out good research project.
The researchers face difficulties to get the collaboration to provide data, makes clearly explain to them about the purpose and requirement of data in order to obtain data and information.
In addition, the researcher faces time and financial constraint. The time to complete this research is limited. The researcher needs to finish this research efficiently and properly. So that, the researcher manage the time smartly in order to make sure this research can be finish on time. The researcher has to be spending a huge amount of money in terms of printing expenses, photocopy, and internet used in order to get information. All of these will be quite expensive to the researcher.
1.11 STRACTURE OF THE STUDY
This study organizes into five chapters. Introduction of the study with respect to sub-titles presented in chapter one, chapter two presents literature review of the study including both the theoretical and empirical review pertaining to the determinants of bank’s NPLs as well as research gap according to empirical reviews are discussed. Research design and methodologies are presented under chapter three. This is followed by the results and analysis of different data source in chapter four. Finally, chapter five presents the conclusions and recommendations.
National Bank of Ethiopia (NBE):-It is the reserve or central bank of Ethiopia. Besides licensing and supervising banks, insurers and other financial institutions, NBE fosters a healthy financial system and undertakes other related activities that are conducive to rapid economic development of Ethiopia. (Proclamation No.592/2008, FDRE, 2008)
Loans and Advances : means any financial assets of a bank arising from a direct or indirect advance or commitment to advance funds by a bank to a person that are conditioned on the obligation of the person to repay the funds, either on a specified date or on demand, usually with interest (NBE Directive, SSB/43/008).
Borrower: – is the one who borrows money from the lender (Bank).
Lending -is the provision of resources (granting loan) by one party to another party.
Nonperforming loans – loans or advances whose credit quality has deteriorated such that full collection of principal and/or interest in accordance with the contractual repayment terms of the loan or advances are in question; or when principal and/ or interest is due and uncollected for 90 (ninety) consecutive days or more beyond the scheduled payment date or maturity (NBE Directive, SSB/43/008).
Credit risk – it is the risk that a financial contract will not be concluded according to the agreement. The counterparty to an asset will default the risk.
1.13 ETHICAL ISSUES
Almost all the financial institutions have strict policy implications on the confidentiality of their data. They can pay the ultimate price for the breach of this duty of confidentiality. Disclosing of information by employees to a third party can expose the institution to potential legal conflict. Due to this ethical issue, they are fearful in disclosure of such information. However, this fear was addressed by explaining the core of the study to the information providing with the assurance that the data will be handled professionally through formal letter. Therefore, before data collection, permission is obtained from the management body of the DBE through formal letter. The formal letter was taken from Wollo University specifically from the research and graduate studies office of business and economics collage and then given to those banks to undertake the tasks freely and confidentially.
REVIEW OF LITERATURE
In the preceding chapter, background information of the study with respect to the research problem and objective of the study were discussed. The purpose of this chapter is to discuss both theoretical and empirical issues pertaining to the determinants of bank’s NPLs. The chapter has three sections. The first section 2.1 presented theoretical review of NPLs. Section 2.2 presents a review of empirical studies that have been conducted so far on determinants of bank’s NPLs. Finally, research gaps are presented in section 2.3.
2.1. THEORETICAL REVIEW OF NON-PERFORMING LOANS
According to Isa (2009), from a pragmatic point of view, the rationale behind the existence of banks is the provision of different types of loans, which in turn are considered as the main source of the banking profits. Therefore, DBE attempts to invest as much of the available funds as possible, in the form of loans and credit facilities to maximize their profit. This in turn results in the majority of development banking assets being in the form of loans and credit facilities (Chou and Tanguy 2008). Despite the loan portfolio is typically the largest asset and the predominate source of revenue of banks, the function of granting credit is not free of risks (Casket al.2006). In practice, loans are considered as the types of investment, which have the highest levels of risks about the difficulty of the funds ‘recovery. DBE is exposed to numerous difficulties regarding the protection and recovery of funds granted in the form of loans and credit facilities.
2.1.1. THEORETICAL REVIEW OF BANKING
This section deals about the role of banks, bank lending and credit methodology of banks.
184.108.40.206. ROLE OF BANKS
Banks role in the economy of any country is very significant. They play intermediation function and collect money from those who have excess and lend it to others who need it for their investment. Availing credit to borrowers is one means by which banks contribute to the growth of economies. Lending represents the heart of the banking industry. Loans are the dominant asset and represent 50-75 percent of the total amount at most banks, generate the largest share of operating income and represent the banks greater risk exposure (Mac Donald and Koch, 2006).Moreover, its contribution to the growth of any country is huge in that they are the main intermediaries between depositors and those in need of fund for their viable projects (creditors)thereby ensure that the money available in economy is always put to good use.Therefore,managing loan in a proper way not only has positive effect on the banks performance but also on the borrower firms and a country as a whole. Failure to manage loans, which make up the largest share of banks assets, would likely lead to the episode of high level of non -performing loans.
220.127.116.11. BANK LENDING
DBE’s main area of focus is provision of medium and long-term loans for investment projects in the Government’s priority areas. In line with the Agriculture Development Led Industrialization strategy (ADLI) of the country, the Bank provides finance to encourage investment in Agriculture and manufacturing industries preferably export focused. Major categories of priority area:
• Commercial agriculture
• Agro-processing industries;
• Manufacturing sector including mining or extractive industries.
• Tour and Construction industries
18.104.22.168. CREDIT METHODOLOGY
Every bank activity has its own process to prove loans to its customers. In the case of DBE to
Provide loans it has three steps these are credit assessment, project appraisal and loan approval team to avoid the conflict of interest and used for cheek and balance of each loan provision.
22.214.171.124. CREDIT ASSESSMENT
The Bank accepts applications from both recruited and walk-in customers if they fulfill the Bank’s loan requirements. The Credit Process/branches mainly focus on recruiting customers by attracting and persuading potential applicants using appropriate means of communication. This requires the understanding of the strategic and operational plan of the Bank and identifying the sources of such potential customers. It is also important to promote the Bank’s services, offer options of model bankable projects and encourage potential investors to apply for credit. The Credit Process and/or branch offices should select potential customers applying for project financing based on the eligibility criteria and checklists for customer requirements:
Due diligence or KYC (Knowing your customer) assessment will be undertaken by the Bank to identify the integrity of the borrower. This is done to protect the Bank from entering into
relationships with inappropriate borrowers and to check the borrower’s credit worthiness. This requires knowledge of gathering and evaluating KYC information of the applicant in compliance with the due diligence assessment guidelines and formats of the DBE and the requirements of the NBE directive No SBB/46/2010
126.96.36.199. CREDIT APPRAISAL
Appraisal is the comprehensive and systematic assessment of all aspects of a proposed project. After a project has been prepared, it is generally appropriate for a critical review or an independent appraisal to be conducted. This provides an opportunity to reexamine every aspect of the project plan to assess whether the proposal is appropriate and sound before large sums are committed. Financial institutions normally make their own appraisal of projects presented to them for loans before they contribute funds for implementation of the projects. That is why project appraisal is usually seen as a major activity of lending institutions while project promoters/consultants normally undertake project feasibility study. Usually, the techniques applied to appraise projects center around technical, commercial, market, managerial, organizational, and financial and possibly economic aspects.
188.8.131.52. CREDIT APPROVAL
Once loan applications for financing of development projects are received and screened for appraisal by the Credit Process/branches, the Projects Appraisal Sub O Process/regional appraisal teams appraises the project, it needs to be decided upon by the Loan Approval Team/regional approval teams. The LAT (loan approval team) is to make decisions on the approval or rejection of the loan. In this process, the Loan Approval Team deliberates and decides on the loan approval document to accept or reject the loan proposal. Once the loaning decision is made in the Loan approval Process/team, the case goes back to the Credit Process/branch for subsequent actions.
184.108.40.206. PROJECT SUPERVISION AND FOLLOW-UP
The Bank undertakes project supervision and follow-up activities using both on-site and off-site supervision methods. The purpose of project follow-up is to ensure that the financed projects are properly implemented and operating. It also helps to provide technical assistance as and when required. All financed projects by the Bank should, therefore, be properly followed up and full-fledged reports have to be prepared. Off-site supervisions using periodic reports from borrowers can be made as per agreements between the parties. Projects deemed unstable and non-performing loans should be followed up more frequently.
220.127.116.11.1. TYPE OF FOLLOW-UP REPORTS
Follow-up works can be made at three major stages: namely, when the project is under Implementation, at project completion/commissioning and project under operation and reports can be made accordingly.
A. PROJECT IMPLEMENTATION FOLLOW -UP REPORT (PIFR)
This report is to be prepared while the financed project is under implementation and in the pre-commissioning stage. It is intended to highlight all stakeholders on the implementation progresses, problems faced and corrective measures that need to be taken if any part of the project implementation activities have gone out of track and to design and implement means of getting back all the project implementation elements to track. This report is, therefore, vital and each project under implementation should be visited frequently, at least monthly, after signing the loan contract.
B. PROJECT COMPLETION/COMMISSION FOLLOW-UP REPORT (PCFR)
PCFR is usually a onetime project completion report that has to be prepared at the Commissioning of the financed project. The project passes to the operational phase from the implementation phase at a project milestone. The main purposes of this follow-up report are:
i. It helps to summarize the activities undertaken, the challenges faced in project implementation and the solutions provided and the final results obtained up to and including to the project commissioning/ completion date;
ii. It indicates the issues that have to be given due attention by the operational managements the project management hand over the project after full commissioning;
iii. It ensures the completeness of all the major implementation activities undertaken and actual completion date compared with the planned;
iv. The report also ensures the proper utilization of all the financial resources budgeted for the project from equity as well as loan and compares the list of materials executed against the planned.
C. PROJECT OPERATIONS (ONGOING PROJECTS) FOLLOW-UP REPORT (POFR)
POFR is a regular project follow-up report that has to be prepared at different periods in the financed project operation phase until the Bank’s loan is fully repaid with all the due interest and other charges. While the loan is outstanding, the Bank has the obligation to closely follow up on the financial conditions of the borrower to detect any adverse changes that may affect his/her capacity to repay the loan as agreed. Appropriate actions shall be taken as soon as possible, even when the loan is still performing but has shown some early signs of trouble. The main purposes of this follow-up report are:
i. It helps to assess and evaluate the actual project operational performance with the project appraisal and loan agreement;
ii. It also helps to identify the major risks on the sustainability and loan repayment potential of the project and to recommend the risk mitigating mechanisms for rescuing the project;
iii. It enables the Bank to monitor the financed project whether it is delivering the intended output and ultimately planned outcome of the project;
iv. It is a base to provide technical assistance to financed projects and any risk mitigation mechanisms as a means for project rehabilitation.
Therefore, these three interrelated project follow-up reports have to be prepared based on the
requirements of project management and operations information needs, which are important for decision-makings on the project at various stages in the project cycle.
18.104.22.168. BANKING RISKS
Risk management is a regulation at the central part of any banking organization and covers all the actions that influence its risk profile. It includes identification, measurement, monitoring and controlling risks. Risk-taking is an inherent element of banking and, indeed, profits are in part the reward for successful risk taking. On the other hand, excessive and poorly managed risk can lead to losses and thus endanger the sustainability of the Bank. Risk in a banking organization refers to the likelihood that the outcome of an occurrence could bring adverse impacts on the institution’s capital, earnings or its viability. Such outcomes either could result in direct loss of earnings and erosion of capital or may result in burden of restriction on the bank’s capability to meet its business objectives and to execute its strategies successfully. It is expected to ensure that the risks that the bank is taking are warranted. Risks are considered warranted when they are understandable, measurable, controllable and within the bank’s capacity to readily withstand adverse results. Sound risk management systems enable the bank to take risks knowingly, reduce risks where appropriate and strive to prepare for a future, which by its nature cannot be predicted with absolute certainty.
Banks must have comprehensive risk management process (including board and senior management oversight) to identify, evaluate, monitor and control or mitigate all material risks
in addition, to assess their overall capital adequacy in relation to their risk profile. Whilst the types and degree of risks and organization may be uncovered, depend upon may factors such as its size, complication, business activities, and amount .The most common risks the bank faces, namely: Credit Risk, Liquidity Risk, Interest Rate Risk, Foreign Exchange Rate Risk and Operational Risk.
22.214.171.124.1. CREDIT RISK
Credit risk is defined as the potential that a bank’s borrower or counterparty will fail to meet its obligations in accordance with agreed terms. It is the most prominent risk faced by banks and banking systems that needs to receive management’s full-fledged attention and proper administration. Credit risk arises:
• Any time bank funds are extended, committed, invested, or otherwise exposed, whether reflected on or off the balance sheet;
• From several factors such as weak or nonexistent credit standards for borrowers, poor loan portfolio management, individual borrowers’ ability to repay, general economic conditions, specific events, and from local, regional or national causes.
Credit risk is the single largest factor affecting the soundness of a banking institution and the
Financial system as a whole. Banks need to manage credit risk inherent in the entire portfolio as well as the risk of in individual credits or transactions. Over the years, weak credit risk management practices and poor credit quality continue to be the major causes for Bank’s failure and Banking crisis worldwide.
126.96.36.199.2. LIQUIDITY RISK
Liquidity risk is the risk that a bank cannot meet payment obligations (commitments, repayments and withdrawals) in a timely and cost effective manner. It is the inability of a bank to raise funds in the market at a cost equivalent to that of other similar banks or to sell assets/instruments in the market (e.g. failure to discount Treasury bill) when it needs to do so.
Liquidity is important to:
? Pay creditors,
? Meet unforeseen deposit withdrawals/runoffs
? Accommodate unexpected changes in loan demand, loan commitments and fund normal loan growth without making costly balance sheet adjustment,
? Pursue other investment opportunities; and
? Cover administrative and operational expenses.
Thus, banks must have adequate liquidity in order to timely serve their customers and to operate efficiently and profitably. From the definition and our explanation of the need for liquidity, we can deduce that liquidity has three components, namely, amount (sufficient fund), timeliness (as needed) and cost (at a reasonable cost or in the most cost-efficient way possible).The purpose of liquidity management is to ensure that every bank is able to meet fully its contractual commitments. The ability to fund increases in assets and meet obligations as they become due is critical to the ongoing viability of any bank. Therefore, managing liquidity is among the most important activities conducted by banks.
188.8.131.52.3. OPERATIONAL RISK
Operational risk is the risk of direct or indirect loss resulting from inadequate or failed internal processes, people and system or from external events or catastrophes. Operational risk is associated with human error, system failures and inadequate procedures and controls. This risk is, therefore, imbedded in all of the bank’s operations including those supporting the management of other risks. The following are some examples (loss events) of operational risk:
? Failure in execution, delivery and/or processing: including data entry errors, miss operation of systems, miscommunication, missing deadlines, accounting errors, reporting failures, failure to settle business transaction (including losses arising from an unintentional or negligent failure to meet obligations to specific clients),missing/incomplete documentation and deviations from business strategies, policies ; procedures;
? Fraud (internal and external): including theft/robbery, forgery, undertaking unauthorized transactions, intentional manipulations of computer systems, and misreporting;
? System Failures: including failures in hardware, software, telecommunications, power supply, IT systems and programming;
? Damages to physical assets: losses arising from loss or damage to physical assets(including human resource) due to natural disaster (like earthquakes, thunder/lightning, floods), fire and/or other events (like terrorism and riots);
? Problems related to human resource management: including wrongful layoffs, employee discriminations, harassments, compensation claims, improper alignment of compensation schemes and incentives, lack of adequate skills, and problems arising from workplace safety (like violation of health and safety rules);
? Others like breaches of regulations (there may be fines/warnings from regulatory body as a result), misuse of confidential client information, money laundering, client complaints; client initiated legal actions and vendor ; outsourcing related disputes. The following types of Operational Risks are;
• IT Risk, IT risk arises from any potential adverse outcome, impairment, loss, violation, failure or disruption in the performance of business function or processes due to the use of or reliance on technology. Exposure to this risk can result from among others, system flaws, software defects and network vulnerabilities.
• Legal Risk, Legal risk is the risk arising from the potential that unenforceable contracts, lawsuits, or adverse judgments can disrupt or otherwise negatively affect a bank’s operations or conditions.
• Regulatory Risk, Regulatory risk is the risk of being downgraded, fined, suspended, license revoked, arising from failure to comply with regulatory requirements or directives.
• Strategic Risk, Strategic risk refers to the potential negative impact on a bank’s earnings andcapital that can arise in circumstances where decisions taken by the organization or the manner in which business strategies are executed results in losses or missed opportunities for the organization to remain relevant in the market place as a profitable and viable business entity.
• Reputational Risk, Reputational risk arises from negative publicity, be it true or not, regarding bank’s business practices.
• Systematic Risk, Systematic risk refers to the danger that problems in a single financial institution might spread and, in extreme situation, such contagion could disrupt the normal functioning of the entire financial system.
184.108.40.206. CURRENCY RISK
Foreign exchange (currency) risk is the risk of loss due to changes in the value of foreign currencies in terms of Birr (the local currency). The potential for loss arises from the process of revaluing foreign currency position in Birr terms. When banks have an open position in a foreign currency (Where the value of asset/inflow exposures in one currency is not equal to the value of liability/outflow exposures in that currency), the process of revaluation normally will result in a gain or loss. The gain or loss is the difference between the aggregate change in the Birr equivalent value of assets denominated in the foreign currency and the aggregate change in the value of liabilities and capital denominated in that currency. Whether the gain or loss that the bank incurs will be recognized in its book depends on accounting rules.
The National Bank of Ethiopia (NBE) is gradually liberalizing foreign exchange controls in the country. Currently, banks are allowed to take open positions in foreign currencies subject to regulatory limits set by the NBE. For this reason, all banks are exposed to foreign exchange rate risk. The goal of foreign exchange risk management is to ensure that foreign exchange risk is controlled and managed within the bank’s risk management program.
220.127.116.11. INTEREST RATE RISK
Interest rate risk is the exposure of banks financial condition to adverse movements in interest rates. It arises when there is a mismatch between positions, which are subject to interest rate adjustment within a specified period. The bank is lending, funding and investment activities give rise to interest rate risk. Exposure to this risk in banking book primarily results from timing difference in the re-pricing of assets and liabilities, both on and off balance sheet. In the scenario of rising interest rate, when liabilities re-price faster than assets, interest spread would fall and hence profitability of the bank would be adversely affected. Changes in interest rates affect bank’s earnings by changing their net interest income and the level of other interest sensitive income and operating expenses. Changes in interest rates also affect the underlying value of the bank’s assets, liabilities and off-balance sheet instruments because the present value of future cash flow ( and in some cases, the cash flows themselves) change when interest change. Therefore, an effective risk management process that maintains interest rate risk within prudent levels is essential to the safety and soundness of the bank. Major portion of income for most banks comes from interest income from loans. It is also important to note that major portion of banks’ expense is the interest paid to depositors. Thus, banks should properly determine loan price (the interest rate chargeable on loans) and cost of funds (interest rate to depositors) so as to have positive spread that is sufficient to cover the operational cost, credit risk premium (charge that help offset the likelihood of principal and interest loss) and return on economic capital invested. The following shows a general approach to loan pricing.
Lending Rate = Cost of Fund + Operational & Overhead costs +Charge for possible loss + Return on Economic Capital Allocated
For proper pricing of their assets/loans, it is important for banks to properly price their liabilities since the net interest income, major sources of their total earnings, is the difference between what they earn on loans and advances (asset price) and what they pay to depositors (liability price).Generally, it is important to note that part of interest rate risk management entails pricing or getting a return commensurate with the risk taken.
18.104.22.168. BANKS CREDIT RISK MANAGEMENT SYSTEM
Risk management is a regulation at the nucleus of each banking organization and incorporates all the activities that have an effect on its risk profile. It involves launch the contexts, identification, measurement; monitoring and controlling risks.
o Establishing The Context
This defines the framework, which encompasses the scope of risks to be managed, the process/systems and procedures to manage risks, and the roles and responsibilities of individuals involved in risk management.
o Risk Identification
Once the context is established, the first step in managing a potential risk is identification of risks. Risks are about events that, when triggered, cause problems. Hence, risk identification can start with the source of problems or with the problem itself. Risk identification should be a continuing process and risk should be understood at both the transaction and portfolio levels. Some examples of risk identification techniques include brainstorming, questionnaire, business study, audit and inspection.
o Risk Measurement/Assessment
Once the risks associated with a particular activity have been identified, the next step is to measure the significance of each risk. Each risk should be viewed in terms of its three dimensions: size, duration and probability of adverse occurrences.
o Risk Treatment/Control
After measuring the significance (severity and probability of occurrence) of risks, it is important to design mechanisms to mitigate their adverse effect. Strategies of risk treatment include:
Avoiding the risk- this is not performing an activity that carries risk;
Transferring the risk to another party- this is causing another party to accept the risk, typically by buying insurance policies or by hedging financial instruments;
Reducing the negative effect of the risk- this involves putting in place mechanisms that may reduce the severity of the loss;
Accepting risks- Involves accepting the loss when it occurs.
o Risk Monitoring
Results of past practice, experience and actual loss should be reviewed and evaluated to update risk management framework accordingly. This is important to evaluate whether the previously selected risk management strategies and systems are still applicable and effective for the changing environment.
22.214.171.124. DEFINITION OF NON-PERFORMING LOANS
A loan on which the borrower is not making interest payments or repaying any principal. At what point the loan is classified as non-performing by the bank, and when it becomes bad debt, depends on local regulations. Banks normally set aside money to cover potential losses on loans (loan loss provisions) and write off bad debt in their profit and loss account. In some countries, banks that have accumulated too many NPLs are able to sell them on – at a discount – to specially established asset management companies (AMCs), which attempt to recover at least some of the money owed.
• Impaired loans with due payment (World Bank)
• Loans with deteriorated quality (NBE)
• Loans that failed to be paid in principal/interest within agreed time (NBE)
• Failure to meet to consecutive repayments (DBE)
As per NBE(2012 p.3), NPLs are defined as “loans or advances whose credit quality has deteriorated such that full collection of principal and/or interest in accordance with the contractual repayment terms of the loan or advances in question”.
The definition of NPLs varies from one banking system to another according to banking laws and regulations (Isa 2009). In practical terms, Quantitative and qualitative criteria are used individually or collectively by credit institutions to identify the situation of the loan. Freeman (2005 p.8) give the definition of NPLs as “a loan is NPLs when payments of interest and/or principal are past due by 90 days or more, or interest payments equal to 90 days or more have been capitalized, refinanced, or delayed by agreement”.
“Short term loans are NPLs when principal and/or interest is due and uncollected for 90(ninety) consecutive days or more beyond the scheduled payment day or maturity. Medium and long term loans are NPLs when principal and/or interest is due and uncollected for 12(twelve) consecutive months or more beyond the scheduled payment day or maturity”.
According to NBE (2012) directive, Ethiopian commercial banks are required to classify
Their loans as pass, special mention, substandard, doubtful and loss in accordance with Bank for International Settlements (BIS) 3 standards as presented below:
• Pass: loans in this category are fully protected by the current financial and paying capacity of the borrower and not subject to any criticism.
• Special mention: Short term loans past due for 30 days or more, but less than 90 days and medium and long term loans past due for 6 month or more, but less than 12 months.
• Substandard: Short term loan past due for 90 days or more, but less than 280 days and medium and long term loans past due for12 months or more, but less than 18 months
• Doubtful: Short term loan past due for 280 days or more, but less than 360 days and medium and long term loans past due for18 months or more, but less than 3 years.
• Loss: Short term loan past due for 360 days or more, and Medium and long term loans past due for3 years or more.
126.96.36.199. DETERMINANTS OF NON-PERFORMING LOANS
Unfortunately, there is no particular theoretical framework that emphasizes on the determinants of NPLs (Isa 2009). These concepts can be extended to NPLs, since; NPLs are the result of a particular behavioral pattern emerging from moral hazard on the side of borrower and adverse selection on the side of lenders (Isa 2009). Therefore, the concepts of asymmetric information can be examined to give further meaning and to understand behavioral aspects of NPLs. According to Arrests and Sawyer (2001), the first important theoretical concept in relation to NPLs, as the articulation of asymmetric information, is the adverse selection issue. Starting from other researchers ‘idea, this research will consider some determinants of NPLs in DBE.
1. MACRO ECONOMIC FACTORS
The existing literature provides evidence that suggests a strong association between NPLs and macroeconomic factors. Several macroeconomic factors, which the literature proposes as important determinants of NPLs, are real GDP growth, inflation rate, effective exchange rate, real interest rate, unemployment rate, broad money supply (M2) and GDP per capital (Salas and Saurian 2002, Office 2005 and Jimenez and Saurian 2005). This study only considers the growth in real GDP, annual inflation rate, real interest rate.
1.1. REAL GDPGROWTH
There is an inverse relationship between GDP growth and the level of NPLs reported by commercial banks, Hou (2006), Pasha and Kerman (2009), Louzis et al. (2010) and Abeam et al. (2012). The explanation provided by the literature for this relationship is that, Changes in business cycle affect the credit worthiness of borrowers in terms of repayment capacity. Hence, strong growth in real GDP usually translates into more income, which improves the debt servicing capacity of borrower, which in turn contributes to lower NPLs. Conversely, when there is a slowdown in the economy (low or negative GDP growth), the economic activities in general aredecreasing and the volume of cash held for either businesses or households are decreasing. These conditions contribute in deteriorating the ability of borrowers to repay the loans, which lead to increase the likelihood of delays their financial obligations and thus banks? exposure to credit risk increase. In this regard, Hou (2006) noted that, each NPL in the financial sector is viewed as an obverse mirror image of a poorly unprofitable enterprise.
1.2. REAL INTEREST RATE
It is inflation-adjusted interest rate; the existing literature has suggested empirically significant positive association between interest rate and NPLs. The explanation of positive relation is that with the increase in interest rate the difference between deposits rate and lending rate increases. Only low quality borrowers show willingness to pay high interest rate, thus banks in order to earn lend more funds to the low quality borrowers. Low quality borrowers by using bribes to bank officials and other corruption supported practices do not repay the loan, consequently results in. the growth of NPLs. Moreover, researcher expected positive association between real interest rate and NPLs, In this general setting, a higher interest rate leads to even greater adverse selection; that is, the higher interest rate increases the likelihood that the lender is lending to a bad credit risk and ultimately increases NPLs (ezSaurina (2006), Pasha and Khemraj (2009), Ahmad et al (2009) and Metaxas et al (2010).
1.3. AVERAGEINFLATION RATE
It is defined as a continuous increase in the average price of all goods and services in a given period. There is negative association between inflation and NPLs and will concluded that with the increase in inflation the equity value of the banks declines results in the growth of banks credit risk. Due to this fact, researcher will expected a negative relationship between average inflation rate and nonperforming loan. Inflation affects borrowers? debt servicing capacity through different channels and its impact on NPL can be positive or negative Pasha Kerman (2009) and Nukes’2011). The explanation provided by the literature for this relationship is that, higher inflation can make debt servicing easier by reducing the real value of outstanding loans particularly when the loan rates are fixed (banks do not adjust rates in accordance to the inflation change to maintain their real returns). However, it can also weaken some borrowers? ability to service debt by reducing real income. Moreover, when loan rates are variable(adjusted in accordance to the inflation change), inflation is likely to reduce borrowers? loan servicing capacity as lenders adjust rates to maintain their real returns or simply to pass on increases in policy rates resulting from monetary policy actions to combat inflation. Against this background, the relationship between NPL and inflation can be positive or negative.
1.4. UNEMPLOYMENT RATE
It is defined as the rate of unemployed population from the total number of population in the country. Various studies have found significant positive impact of unemployment on NPLs .The explanation of positive association is that with the increase in unemployment, labor losses their source of income and has no money to repay their loans; as a result, NPLs increases. Conversely, the decline in unemployment rate results in the increase in the number of earning individuals, thus number of debtors having the money to repay the loan increases consequently NPLs declines. Because, researcher expected a positive relationship between non-performing loan and unemployment rate.
1.5. REAL EXCHANGE RATE
A fall in the exchange rate of a country’s currency can occur due to market force, where changes in the demand or supply of foreign currency change the equilibrium price. In case of fixed exchange rate, the government can intervene in the foreign exchange market and cause a fall or devaluation in the exchange rate. In either case, the impact of a fall in the value of a currency will be the same. The gradual devaluation of the value of birr has made domestic product more and more chipper for importers from aboard, enhances export, and increased the demand for domestically produced exportable goods. Conversely, real appreciation would make export less competitive in the world market and hence decreased total export. The positive association of real effective exchange rate with NPLs and concluded that the inflationary pressure and increase in real effective Exchange rate contributes to the growth in NPLs. Therefore, we expect the coefficient of real exchange rate to be positive.
2. BANK SPECIFIC FACTORS
Bank-specific variables refer to those factors, which characterized individual banks. Those factors can be influence by managerial decisions and usually associated with the specific policy choices of a particular bank with regard to its efforts to maximize efficiency and improve its risk management. Hence, bank specific variables that are usually theorized as determinates of NPLs are include, loan growth, financial performance, credit monitoring ; follow up presents the bank-specific variables that are used in this study.
2.1. LOAN GROWTH
The credit policy of the bank plays an essential role in determining the subsequent levels of NPLs. Loan growth have a direct (positive) association with the volume of NPLs reported by commercial banks; Jimenez and Saurian (2006) and Metaxas et al (2010). To maximize the short run benefits, managers seek to rapidly expand credit activities and may hence take inadequate credit exposures. In this regard, Keeton (2012) suggests that rapid growth of loans can be triggered by return maximization strategies. Particularly, during periods of economic growth, the financial institutions engage in market share conquest campaigns discarding the necessary assessment of credit quality of borrowers (Fernandez De List et al., 2010). The search for rapid growth of loans is achieved by either reducing interest rate charged to borrowers or by lending to lower credit quality borrowers. This will lead, through adverse selection reasoning in which banks lend to lower credit quality borrowers and ultimately increase the probability of NPLs.
2.2. FINANCIAL PERFORMANCE
The financial performance of a bank is usually related to the risk taking behavior of managers; Saurian (2006), Jelloulietal (2009), Metaxas et al (2010) and Vogiazas and Nikolaou (2011)). As noted in Hues al. (2009), profitable banks are less engaged in risky activities as they have less pressure to create revenues. Profitable banks have an opportunity to choose a loan applicant who has strong financial performance and lower risk. Hence, as the profitability of banks increases, the probability that managers engaged in risky investment will reduce and ultimately the probability that loans become a nonperforming loans will also reduce with the same manner. To the contrary, unprofitable (inefficient) banks might engage in risky lending activity in particularly when managers have short-term incentives. As long as banks engaged in risky activity the likelihood that loans become default is high and ultimately resulted with sizeable volume of NPLs.
2.3. CREDIT FOLLOW UP
For unstable and non-performing loans the concerned work units should not only prepare follow-up plans at the beginning of the year but also strictly adhere to the plan, as the follow up work is believed to add value to the project rehabilitation and loan recovery endeavor of the Bank;
Follow-up works can be made and reports prepared at three major stages:
o When projects are under implementations;
o When projects are ready for commissioning;
o When projects are under operations.
Similarly, the frequency of project follow-up for rain fed agricultural projects should at least be three times a year at a time of field preparation, crop management (seeding, weeding, thinning)and harvesting time;(DBE Loan manual 2016).
The Bank undertakes project monitoring and follow-up activities using both on-site and off-site supervision methods. The purpose of project follow-up is to ensure that the financed projects are properly implemented and operating. It also helps to provide technical assistance as and when required. All financed projects by the Bank should, therefore, be properly followed up and full-fledged reports have to be prepared. Off-site supervisions using periodic reports from borrowers can be made as per agreements between the parties. Projects deemed unstable and non-performing loans should be followed up more frequently. (DBE Loan manual 2016).
2.2. EMPIRICAL LITERATURE REVIEW OF NPL
2.2.1. Review Finding Of Major Studies
Over the last few years, the literature that examines non-performing loans has expanded in line with the interest afforded to understanding the factors responsible for financial Vulnerability. This situation will be attributed to the fact that impaired assets plays a critical role in financial vulnerability as evidenced by the strong association between NPLs and banking/financial crises. In this section, we review the existing literature to formulate a theoretical framework to investigate the determinants of non-performing loans in development bank of Ethiopia.
2.1.2. STUDIES IN ETHIOPIAN CASE
In the context of Ethiopia, there appears to be very limited studies on the determinants of bank’s NPLs. To the knowledge of the researcher, there is only the work of Negara (2012) that investigated the determinants of NPLs in the context of ECBs. However, also other studies investigated the other aspect of NPLs in ECBs. These studies are the work of Tilahun (2010) and Azalea (2009). Thus, this particular section provides a detailed review of those related studies conducted in the context of Ethiopia. Negara (2012) assessed the determinants of NPLs in Ethiopian commercial banking sector using a survey data collected from both private and state owned commercial Banks using a self-administered questionnaire. In addition to the survey data, the study used interview with senior bank officials. Descriptive statistics and correlation matrix were used to analyze the data. The findings of the study shows that poor credit assessment, failed loan monitoring, underdeveloped credit culture, lenient credit terms and conditions, aggressive lending, compromised integrity, weak institutional capacity, unfair competition among banks, willful default by borrowers and their knowledge limitation, fund diversion for unintended purpose, over/under financing by banks ascribe to the causes of loan default. However, the study outcome failed to support the existence of relationship between banks size, interest rate they charge and ownership type of banks and occurrences of nonperforming loans. On the other hand, the study of Tilahun (2010) identified the underlying NPLs management difficulty of ten privately owned commercial bank in Ethiopia given in to consideration that the managements are different from bank to banks based on their perception towards the NPLs.
By using a mixed research approach, Azalea (2009), investigated legal problems in realizing nonperforming loans of Ethiopian commercial Banking sector. The finding of the study indicated that legal gaps that exist in procedural laws and institutional problems affect the resolution process. Furthermore, the study argued that, issuing appropriate laws covering financial securities, establishing a comprehensive institutional framework including Asset Management Companies (AMCs) with clear accountability and transparency are found to be very important.
W. N. Geletta (2012) by using mixed research approach in his Research Report of the determinants of nonperforming loan in Ethiopian banks, found that fund diversion, compromised integrity, over/under Financing were the most frequently mentioned factors followed by unfair competition among banks, willful default and macroeconomic conditions. In a question where the respondents were requested to rate factors they believed cause occurrences of nonperforming loans in order of importance; poor monitoring by banks, banks size, poor risk assessment, credit culture/orientation were rated to be the top four factors causing loan default. On the other hand, charging high interest rate and rapid loan growth were rated among the least factors causing occurrences of nonperforming loans. TesfayeBoruLelissa (2014) based on his paper on the determinants of Ethiopian banks performance considering bank specific and external variables on selected banks’ profitability for the 1990-2012 periods, moreover he found that bank specific variables by large explain the variation in profitability. High performance is related to the ability of banks to control their credit risk, diversify their income sources by incorporating non-traditional banking services and control their overhead expenses. In addition, on his paper found that bank’s capital and liquidity status are not significant to affect the performance of banks. On the other hand, on his the paper finds that bank size and macro-economic variables such real GDP growth rates have no significant impact on banks’ profitability. However, the inflation rate is determined to be significant driver to the performance of the Ethiopian commercial banks. Million Sileshi, Rose Nickel and Sabina Wangia3 (2012),by using A structured questionnaire to gather information from 140 smallholder farmers and by applying a two limit to bit regression model to identify factors that influenced loan repayment. They found that, agro ecological zone, off-farm activity and technical assistance from extension agents positively influenced the loan repayment performance of smallholder farmers, while production loss, informal credit, social festival and loan-to-income ratio negatively influenced the loan repayment of smallholder farmers. Tahoma Dual (2010), identified that non-performing loan negatively affect the financial performance of the bank through reduction in loan interest income and lending funds. his study identified that, ineffective loan monitoring and poor credit appraisal as the major factors accounting for non-performing loan from the lending institution side and lack of proper education on business area, lack of sufficient income, absence of sufficient infrastructure, lack of sufficient supervision from the bank, lack of saving account, high consumption expenditure and high interest charge were identified as the causes for non-performing loan from the borrower side. To improve on the quality of the bank’s loan portfolio, the management commonly uses loan rehabilitation. Some measures have been recommended to management of the bank to minimize the existing burden of non-performing loan those are Credit training programs, effective loan monitoring i.e. giving good on sight supervision to client, full filling required credit staff and giving awareness to clients to present adequate collateral and advising clients to have good knowledge on the business area they engaged.Kassahunfiseha (May 2013) based on his Research findings indicated that non-performing loans were caused by both internal and external factor in the context of development bank of DillaBranch. Internal factors such as poor credit policy, Weak credit analysis, and poor credit monitoring and inadequate risk management has impact on nonperforming loan. In addition, researcher finding highlighted that; external factor namely natural disaster, market failure and integrity of the borrowers and nonperforming loans have negatively affected the performance of the bank interims of profitability and liquidity. In addition to the above factors, most of the borrowers do not use the loan for the intended purpose. As a result, diversification of fund or debt occurs. This diversification of fund might be used for non-productive purpose; the debtor will not be able to repay the loan.
Gadise Gezu (2014); her study was to examine the determinants of nonperforming loans (NPLs) of commercial banks in Ethiopia based on panel data analysis on the time from 2002 to 2013. She used Fixed Effect Model and found out that return on equity (ROE), return on asset (ROA), capital adequacy ratio, lending rate, and effective tax rate had statistically significant effect on the level of NPLs. However, the results of fixed effect regression model revealed the insignificant effect of loan to deposit ratio and inflation rate on the level of NPLs of commercial banks in Ethiopia for the period under consideration. KibromTadesse Gebremedhin (2010), identify the determinants of successful loan repayment performance of borrowers by applying probity model and he found that educational level of the borrowers, repayment period of the loan, availability of other source of income, sector, purpose of the loan and type of labor determine successful loan repayment performance of the borrowers positively and significantly. Other variables such as, gender and household size have positive sign, but are not statistically significant. Moreover, variables such as age, loan diversion, other source of credit show negative sign but not statistically significant. The variable experience is statistically significant but show negative sign. In addition, W. N. Geletta 2012 he found that, In a Liker scale measure average response indicated that respondents agreed that credit assessment is related to loan default. They also agreed with the fact that loans follow up /monitoring is related to occurrence of nonperforming loans. On the other hand, the response on relation between collateral and loan default indicated disagreement. Average response on impact of credit culture /orientation was agreement. The response on the relation between loan price /interest rate/ and occurrence of loan default depicted disagreement. Average view of the respondents on impact of credit terms on loan default was agreement. Respondents were of the view that aggressive lending and compromised integrity lead to occurrences of NPL. The response on the relation between bank size and occurrences of loan default indicates disagreement. Finally, the response to a question relating banks ownership type to occurrences of nonperforming loans was neutral. Moreover in his study he found that, From financial data of banks, and in-depth interview where in senior executives in the Ethiopian banking sector indicated that the critical factors causing occurrences of nonperforming loans include: poor credit analysis by banks, borrowers lack of knowledge and entrepreneurship gap (engaging in unstudied business and management capability limitation), lack of competency of credit operators, not keeping apt with national and global business environment by banks and borrowers ,compromised integrity of credit operators, poor monitoring and follow up of loans by lending banks and limitations in the policy environment ( Central bank’s and others) are causes for NPLs (W. N. Goleta 2012) TilahunAemiroTehulu and Degas Raffia Alana (2014) based on the study of Bank- Specific Determinants of Credit Risk: Empirical Evidence from Ethiopian Banks revealed that credit growth and bank size have negative and statistically significant impact on credit risk. Whereas, operating inefficiency and ownership have positive and statistically significant impact on credit risk.
2.2.2. RELATED LITERATURE REVIEW IN OTHER COUNTRIES
Olayinka Akinlo and Mofoluwaso Emmanuel (2014), in determinants of nonperforming loan in Nigeria they found that, in the long run, economic growth is negatively related to nonperforming loan. On the other hand, unemployment, credit to the private sector and exchange rate exerts positive influence on nonperforming loans in Nigeria. In the short run, credits to the private sector, exchange rate, lending rate and stock market index are the main determinants of non-performing loans.MuniBadar;AtiyaYasminJavid (2011) prove that, there is long run relationship with in Nonperforming Loans and the macroeconomic indicators of Consumer Price Index (CPI),Exchange Rate (ER), Gross Domestic Product (GDP), Money Supply (M2) and Treasury Bill Rate (TB).Farad Ahmad and Taqadus Bashir (2013) in research of Explanatory Power of Macroeconomic Variables as Determinants of Non-Performing Loans: Evidence from Pakistan found that, GDP growth, inflation rate, CPI, exports and industrial interest rate are significantly associated with NPLs, whereas unemployment, real effective exchange rate and FDI) are insignificantly associated with NPLs. Mohammad raze AlizadehJanvisloo and Junaina Muhammad (2012), based on the study of Nonperforming Loans Sensitivity to Macro Variables: Panel Evidence from Malaysian Commercial Banks, they found that, lending interest rate and FDI-net outflow are the most effective factors on NPL ratio. This situation is an evidence of the extreme sensitivity of the commercial banking system in Malaysia as open economy to FDI. In addition, there is a robust negative relationship between NPL and GDP growth with the effects operating with up to two-year lags. Inflation and domestic credit growth have positive and negative effects respectively and their effects last for up to two years, but a mild. Therefore, it can be said that the impact of external shocks on the domestic banking system is more than internal shocks. Meanwhile, the effects of monetary policy shocks are greater than demand and supply shocks. Most prevalent factors indicated to cause occurrence of NPL as per the subjective question indicate that some of the factors like, fund diversion, over/under financing, compromised integrity, credit operators capacity limitation, business failures willful default, poor diversification of portfolio, changing policy environment are commonly shared view by respondents from all the surveyed banks staff ascribing to cause occurrence of nonperforming loans. Besides, respondents from both private and state owned banks staff have so much in common (W. N. Geletta Research Report.2012) His study indicated that poor credit assessment ascribing to capacity limitation of credit operators, institutional capacity drawbacks and unavailability of national data for project financing that had also led to setting terms and conditions that were not practical and/or not properly discussed with borrowers had been the cause for occurrences of loan default. Besides, despite the fact that credit monitoring/ follow-up plays pivotal role to ensure loan collection failure to do this properly was also found to be causes for sick loans. The research also indicated that over financing due to poor credit assessment, compromised integrity of credit operators were cause for incidences of NPL. In fact, cases of under financing loan requirement that meant shortage of working capital or not being able to meet planned targets were associated with defaults. ZhannaMingaleva, Myrzabike Zhumabayeva and Guzman Karimbayeva found that, number of NPL started to increase greatly in many countries of the world beginning with the end of 2007.In 2009 NPL increased in many countries by 4-5 times, in the end of 2012 NPL level reached 20% in some countries. In addition, the study also found out that due to underdevelopment of credit orientation /culture borrowers engaged in business that they had no depth knowledge, diverted loans advanced for unintended purpose and at times made a willful default. The study also depicted that unfair competition among the banks along with the aggressive lending pursued added to the poor customer selection made in a motive to maximize profit by the banks and/ or due to the moral hazard or compromised integrity were the other causes for the loan defaults. In-depth interview also indicated that underdevelopment of supervisory authority competence in formulating policies, monitoring capability also ascribe to occurrences of nonperforming loans earlier. On the other hand the study did not support the existing literature that state occurrences of NPL is related to bank’s size, interest rate banks charge and ownership type of banks ( private/state owned). (W. Geletta Research Report, 2015) Bercoff et al (2002) indicated that NPLs are affected by both bank specific factors and macroeconomic factors. In respect of the factors affecting NPL, the subjective question in the survey and in-depth interviews identified factors such as poor credit assessment, failed loan monitoring, underdeveloped credit culture, lenient credit terms and conditions, aggressive lending, compromised integrity, weak institutional capacity, unfair competition among banks, willful default by borrowers and their knowledge limitation, fund diversion for unintended purpose, over/under financing by banks ascribe to the causes of loan default.
Mabvure Tendai, Joseph, Gwangwava Edson, Faitira Manuere, Motive Clifford, Kamoyo Michael in 2012 ,Research findings indicated that non performing loans were caused by internal and external factors. In the context of Commercial bank of Zimbabwe (CBZ) Bank Limited, internal factors such as poor credit policy, weak credit analysis, poor credit monitoring, inadequate risk management and insider loans have a limited influence towards non-performing loans. The research findings highlighted that external factors namely natural disaster, government policy and the integrity of the borrower as the major factors that caused non-performing loans in CBZ Bank Limited.
Findings indicated that there is an upward trend in non-performing loans since the adoption of multicurrency in 2009. The upward trend has been attributed to the growth in the loan book of the bank during the period under review mainly in the agricultural and manufacturing sectors of the economy. The agricultural sector has not been performing well owing to climate changes and expensive costs related with farming in Zimbabwe. Both sectors suffer severely from the increased competition from cheap products, which are being imported from Asia and South Africa thereby threatening their viability. Findings further indicated that non-performing loans have negatively affected the performance of the bank in terms of liquidity and profitability. It was established that an increase in non-performing loans resulted in a reduction in the company’s profitability as well as the liquidity ratio. Despite strategies put in place by management to reduce non-performing loans, problem loans continue to increase. Internal factors can be easily controlled while external factors can be a threat to the viability of banks. Banks have to be vigilant in their lending decisions to avoid loan losses and the accumulation of nonperforming loans. Banks need to concentrate on sectors that are performing well and avoid lending to those sectors, which have already recorded a significant amount of non-performing loans. One thing to note is that these results can be generalized to the whole banking sector in Zimbabwe as almost all the banks have been affected by non-performing loans. (Mabvure Tendai, Joseph, Gwangwava Edson, Faitira Manuere, Mutibvu Clifford, Kamoyo Michael in 2012) Abd-el-Kader Boudriga, Neil BoulilaTaktak and SanaJellouli, 2006 based on banking supervision and nonperforming loans: a cross-country analysis proposed an empirical framework to investigate the bank industry factors and supervisory determinants of NPLs on a cross-country basis. To assess the impact of the effective implementation of those regulations used interactions of three institutional variables (corruption, democracy, and rule of law) with each of the supervision proxies. In contrast with previous work, include interaction terms between political and business environment variables and each of the supervision variables to investigate their impact on banks credit exposures. Using aggregate data on a panel of 59 countries over the period 2002-2006 and robust econometric techniques, they found that there is strong evidence on the association between NPLs and bank specific variables. Particularly, higher CARs and higher provisions ratios are negatively associated with the level of problem loans. These results remain robust even when split the sample into developing and developed countries. Also, report a desirable impact of private ownership, foreign participation, and bank concentration on the stability of the bank sector. However, foreign entry is reported to deteriorate the credit exposure of financial institutions in developed countries. They contend that due to aggressive commercial strategies when penetrating domestic markets, foreign banks, and investors tend to take on excessive risks compared to local banks. Among the control variables, only financial development explains the level of NPLs. However, economic conditions do not significantly affect bank credit outcomes in developed countries. Economic cycles seem only matter in developing economies. Finally, examine the extent to which supervisory framework has a positive impact on credit risk exposures. Our primarily results indicate no support for any relation between official supervision and problem loans. This adds to the growing evidence against the effectiveness of such devices. (AbdelkaderBoudriga, NeilaBoulilaTaktak and Sana Jealously, 2006).However, the results suffer from the fact that the measures used only relate to statutory powers. Thus, they do not address the issue of the effective implementation of supervisory reforms. To investigate this channel, introduce three interactions using the level of corruption, the degree of political openness, and the rule of law. All of these variables are supposed to have an impact on the efficacy of regulation. This finding does not support the view that market discipline leads to better economic outcomes and to reduce the level of problem loans. The contention is drawn upon the absence of any association between the variable private monitoring and the level of problem loans. Indeed, using various specifications (interactions terms and subsamples of developed and developing countries) the coefficients never entered significantly. Moreover, all regulatory devices either exert a counterproductive impact on problem loans or do not significantly enhance credit risk exposures for countries with weak institutions, corrupt business environment, and little democracy. These findings are confirmed by the results for the developing countries panel.
Moreover, the coefficient estimate on the variable supervisory power indicates a positive association with the level of NPLs. Our results suggest that granting increased power to central bankers is detrimental for financial stability in developing economies.
2.3. CONCLUSIONS OF THE REVIEWS AND RESEARCH GAP
The literature review that are discussed so far showed that, banks NPLs are determined by macroeconomic and bank specific factors. The empirical evidence shows that, favorable macroeconomic conditions, such as sustained economic growth, low unemployment and interest rates, tend to be associated with a better quality of bank loans. The studies in general depicted the association between real GDP growths, inflation, real interest rate; unemployment rate and effective exchange rate .On the other hand, bank specific factors like, bank size, financial performance, operational efficiency, rapid loan growth, ownership type, income diversification, risk assessment and monitoring are found to be having significance on the occurrence of NPL. However, Most of the literature that are discussed so far appeared to have focused on studies that were conducted in the banking sector of developed economies (such as united state of American, Spanish, Greek and Italian) and some emerging economies (such as Indian, Chinese, Malaysian, and Indonesia). Consequently, the Banking sectors in most developing economies like Ethiopia have so far received inadequate attention in the literature. Moreover, NPLs of different countries does not necessarily share identical immediate causes since those studies were based on the data from diverse countries. Apart from the data originated from, those literatures by themselves provided contradictory conclusions because of different models and methodologies they used. Hence, their results may not be applicable to Ethiopian banking sector. In the context of Ethiopia, the related study conducted by Negara (2012), assessed the determinants of NPLs in Ethiopian commercial banks by using bank-specific variables. Accordingly, this study clearly failed to identify macroeconomic determinants of NPLs, which have found as a significant determinates of NPLs in many others studies like, Azeemetal. (2012) and Louis et al. (2010). Furthermore, the study used only descriptive statistics and correlation matrix for the entire analysis. However, none of those methods is able to explain causal relationship between variables (i.e., movements in a variable (dependent) by reference to movements in one or more other variables (independent)). For instance, a correlation between two variables measures only the degree of linear association between them (Brooks 2008).In addition, the work of Tilahun (2010) was not mainly intended to investigate the determinants of NPLs, the major aim of the study was to identify the underlying NPLs management difficulty of privately owned commercial bank of Ethiopia without including the two biggest state owned commercial banks that have higher market share in the industry. Moreover, the study used only a descriptive statistics for the entire analysis without considering many limitations associated with it. Similarly, the work Azalea (2009) mainly emphasized on the legal problems in realizing NPLs of Ethiopian banking sector. Thus, the cause of NPLs was not the concern of the study since legal issues comes after the occurrence default loans. Furthermore, the data that has been used in the analysis of all the above studies (i.e. Studies that are conducted in Ethiopian commercial banking context) are mainly obtained from banks officials? through questionnaire and interviews. Hence, intentionally or unintentionally the respondent might be biased.
In general, the lack of sufficient research on the determinants of NPLs in the context of Ethiopia banking sector and the existence of knowledge gap in the area initiate this study. Hence, the motive of this study is to investigate the determinants of NPLs in Ethiopian Development Bank incorporate with all branches; the NPLs amount increase year-to-year particularly DBE and most of the researches depends on the macroeconomic ;bank specific factors with the specific commercial banks. However, this research focuses on all branches of DBE in the country Ethiopia incorporate with some differentiate variables which were not considered in the previous studies by utilizing a regression model.(i.e. credit follow up and, financial performance and loan growth.
2.4. RESEARCH GAP
Based on empirical literature numerous studies were conducted on the determinants of Non-performing loans. Most of these studies focused on Bank specific and Macro-economic determinates of NPL. However, in the previous empirical analysis no study has been conducted on all branches of DBE, rather they were focused on other countries with commercial banks; study in Ethiopia, no more research has been conducted in Development Bank of Ethiopia (DBE) in particular, incorporate with some differentiate variables which were not considered in the previous studies(i.e. credit follow up and, financial performance and loan growth and increases the NPLs amount from year to year above the threshold of NBE which is the vulnerable issues in financial institution.
The preceding chapter presented the review of literature on the determinants of NPLs and identified the existing knowledge gap. The purpose of this chapter is to discuss the research methodology. The chapter is organized with research design with respect to research approaches, data sources, research model variable specification of the study, conceptual framework used in the study.
3.1. RESEARCH DESIGN
To achieve the objective of the study, the researcher uses Eviews8 regression analysis of explanatory research design to identify the major determinant of Non-performing loans of Development Bank of Ethiopia. The reason to uses this research design is there is more studies which were studied on determinant of NPLs by different researchers at different period and the data sources of this study will uses only secondary sources of data .
3.2. RESEARCH APPROACH
The strategy adopted in the study contains diverse methods and tools that are relevant to achieve the desired research outcome. Accordingly, the research strategy employed in this study is quantitative research approach. The use of quantitative strategy of inquiry is necessary when the researcher want to deeply investigate and analyses an event. The purpose of the quantitative aspect of this study is to seek information that can be generalized about the association between macro-economic, bank-and borrower-specific factors and NPLs at DBE.
3.3. DATA SOURCE AND COLLECTION
The researcher uses only secondary sources of data, which can collect from National bank of Ethiopian (NBE), international monetary fund (IMF), World Bank and Biannual reports, journals, pamphlets and internet sources for the reason that, challenging to collect and manage the data with primary sources for all branches of DBE’s throughout the country as well as it requires huge cost & time as well as the researcher can However, when the researcher uses secondary sources of data, easily get the necessary data at central level of DBE different sources. For this particular study, the researcher use time serious data of 26years data from different sourcesstarting1980G.C to 2017 G.C.
3.4. METHODS OF DATA ANALYSIS
The researcher usesEviews8 regression software to analyze secondary data, which is the most appropriate software for time serious data to identify the variables that will be determinants of nonperforming loan in development bank of Ethiopia and to detect the solutions.
3.5. VARIABLE SPECIFICATION AND RESEARCH QUESTIONS
As already shown in the first chapter, the broad objective of this research is to investigate the determinants of NPLs in the context of DBE. In line with the broad objective three specific researches, questions& eight hypotheses formulated, that describes the dependent variable and independent variables of (NPLs).
3.5.1. DEPENDENT VARIABLES
As mentioned in the literature review part of this study, there is no global standard to define NPLs at the practical level. Variations exist in terms of the classification system, the scope, and contents as far as this study intends to investigate the determinants of NPLs in DBE the measurement of NPLs is in accordance with the NBE directive. As per the NBE (2012) directive, NPLs are calcified as Substandard, Doubtful and Loss.
3.5.2. INDEPENDENT VARIABLES
Pervious researches on the determinants of banks? NPLs have shown that, independent variables that can explain the variation on NPLs are classified into bank-specific and macroeconomic variables (Abeam et al. 2012, Delgado and Vallcorba 2007, Louzis et al.2010 and Acton 2009). The bank-specific variables are internal factors and controllable for banks? managers while the macroeconomic variables are uncontrollable and hence external. however, the following subsections presented the bank-specific, borrowers specific and macroeconomic variables used in the econometrics model of this study.
188.8.131.52. BANK SPECIFIC VARIABLES
The existing literature provides evidence that suggests a strong association between NPLs and several bank specific variables. The bank specific variables that are usually theorized as determinants of NPLs are include, loan growth, financial performance, credit follow up. Hence, the following part of this subsection presents the bank-specific variables used in this study.
A. Loan growth: as mentioned in the literature, to maximize the short run benefits, managers seek to rapidly expand credit activities. The search for rapid growth of loans is achieved by either reducing interest rate charged to borrowers or by lending to lower credit quality borrowers (Fernandez De List et al., 2000). This will lead, through adverse selection reasoning (lending to lower credit quality borrowers) and ultimately increase the probability of NPLs. Empirically; various Studies found strong positive relationship between rapid credit growth and NPLs (Keeton, 1999 Salas and Saurian (2002), Jimenez and Saurian (2006) and Metaxas et al (2010)). Hence, a positive relationship between loan growth and NPLs is expected in this study. The variable used to capture credit growth was constructed by finding the annual percentage change in the loan portfolio for DBE.
B.Financial performance: As noted by Hu et al. (2009), profitable banks are less engaged in risky activities as they have less pressure to create revenues. At the opposite, inefficient institutions might engage in risky lending in particularly when managers have short-term incentives. In this regard, many scholars found a negative association between financial performance of a bank and bank’s NPLs (Jimenez and Saurian (2006), Jellouliet al (2009), Metaxas et al (2010) and Vogiazas and Nikolaidou (2011)). Hence, a negative relationship is expected in this study. In this study, the financial performance of a bank was measured by the ratio of Return on asset (ROA).
C. Credits follow up The Bank undertakes project monitoring and follow-up activities using both on-site and off-site supervision methods. The purpose of project follow-up is to ensure that the financed projects are properly implemented and operating. It also helps to provide technical assistance as and when required. All financed projects by the Bank should, therefore, be properly followed up and full-fledged reports have to be prepared. Off-site supervisions using periodic reports from borrowers can be made as per agreements between the parties. Projects deemed unstable and non-performing loans should be followed up more frequently. (DBE Loan manual 2016).In this study the financial performance of a bank was measured by the annual percentage performance follows up.
184.108.40.206. MACROECONOMIC VARIABLES
Apart from bank specific variables, there is abundant empirical evidence that suggests that several macroeconomic factors are important determinants of NPLs. Several macroeconomic factors, which the literature proposes as important determinants of NPLs, are annual growth in GDP, the annual inflation rate, real effective exchange rate (REER), annual unemployment rate, broad money supply (M2) and GDP per capital (Salas and Saurian, 2002; Rajang; Dhal, 2003; Office, 2005; and Jimenez and Saurina,2005). This study only considers the growth in real GDP, inflation and real interest rate.
A. Real GDP growth: the empirical evidence suggested a negative relationship between the growth in real GDP and NPLs (Salas and Saurian, 2002; Rajang; Dhal, 2003; Office, 2005; and Jimenez and Saurian, 2005).The explanation provided by the literature for this relationship is that strong positive growth in real GDP usually translates into more income, which improves the debt servicing capacity of borrower that in turn contributes to lower NPLs. Hence, a negative relationship between GDP and NPLs is expected in this study. The variable used to capture real GDP growth was constructed by finding the annual percentage change in the real GDP.
B. Real Interest Rates: empirically, several studies report that high real interest rate is positively related to NPLs (Sinker and Greenwell (1991), Office (2005) and Jimenez and Saurian (2005)). The basic argument here is that, as interest rates rise, prudent borrowers are more likely to decide that it would be unwise to borrow, whereas borrowers with the riskiest investment projects are often those who are willing to pay the highest interest rates. Hence, a positive relationship real interest rate and bank’s NPLs is expected in this study. In this study, used for the average lending rate of Ethiopian banks.
C. Inflation: as mentioned in the literature, inflation affects borrowers? debt servicing capacity through different channels and its impact on NPL can be positive or negative. Empirically, Office (2005) found a positive relationship between inflation and NPLs in a number of Sub Saharan African countries with flexible exchange rate regimes. On the other hand, Said (2010) found a negative association between inflation and NPLs in Jordanian commercial banking sector. Hence, the relationship is indifferent in this study. In this study, used for annual inflation rate measured by percentage.
D. Real exchange rate; A fall in the exchange rate of a country’s currency can occur due to market force, where changes in the demand or supply of foreign currency change the equilibrium price. In case of fixed exchange rate, the government can intervene in the foreign exchange market and cause a fall or devaluation in the exchange rate. In either case, the impact of a fall in the value of a currency will be the same. The gradual devaluation of the value of birr has made domestic product more and more chipper for importers from aboard, enhances export, and increased the demand for domestically produced exportable goods. Conversely, real appreciation would make export less competitive in the world market and hence decreased total export. The positive association of real effective exchange rate with NPLs and concluded that the inflationary pressure and increase in real effective Exchange rate contributes to the growth in NPLs. Therefore, we expect the coefficient of real exchange rate to be positive.
E. Unemployment rate; It is defined as the rate of unemployed population from the total number of population in the country. Various studies have found significant positive impact of unemployment on NPLs .The explanation of positive association is that with the increase in unemployment, labor losses their source of income and has no money to repay their loans; as a result, NPLs increases. Conversely, the decline in unemployment rate results in the increase in the number of earning individuals, thus number of debtors having the money to repay the loan increases consequently NPLs declines. Because, researcher expected a positive relationship between non-performing loan and unemployment rate.
The following table 3.1 presents the summary of hypothesized expected sign for the relationship between variables (dependent and independent)
TABLE 3.1. VARIABLES AND THEIR EXPECTED SIGN
Variables Notation Variable description Expected sign
Non-Performing Loans NPLs Dependent
Loan Growth LG Independent +/-
Credit Follow up CF Independent –
Financial Performance FP Independent –
Growth Domestic Product GDP Independent –
Real Interest Rate RIR Independent –
Annual Inflation Rate UR Independent –
Unemployment Rate UR Independent +
Real exchange rate REXR Independent –
3.6. RESEARCH MODEL SPECIFICATION
So as to investigate the bank-specific and macroeconomic determinants of bank NPLs, the following multiple linear regression model were adopted:
Yi, t = ? + ?Xi, t + ?i, t………………………………………………….………….3.1
Where: Yi, t is the NPLs amount of bank i at time t, with i=one… N, t=one…T, ? is a constant term, Xi, t is the explanatory variables (bank specific and macroeconomic variables) of bank i at time t and ?i, t the disturbance term. As noted in Brooks (2008) the rational for the inclusion of disturbance term are: first, even in the general case where there is more than one explanatory variable, some determinants of Yi,t will always in practice be omitted from the model. Second, there may be errors in the way that Yi, t is measured which cannot be modeled. Finally, there are bound to be random outside in?uences on Yi, t that again cannot be modeled. Based on the general model provided above and on the base of selected variables the empirical model used in the study was as follows:
In this research as per the recently studied researches, nonperforming loan depends on Annual growth rate of GDP,Real Interest Rate (RIR), Average Inflation Rate (AIR), Loan Growth (LG), Credit Follow up (CF), Financial Performance(FP),unemployment rate (UR) and real exchange rate(REXR Non-performing function expressed as follows:-
NPls = ?0 + ?1LGt+ ?2CFt + ?3FPt + ?4GDPt+ ?5RIRt+ ?6AIRt+?7URt +REXRt€t…………………………………………………………………………………………3.2
NPLs is Dependent variable and used for Non- performing loans’,
LG=Loan Growth at time t (Independent)
?0=the intercept or constant term
?1, ?2…= Coefficients of the independent variables to be estimated.
CMF=Credit Follow up at time t (Independent variable)
FP=Financial Performance at time t (Independent variable)
GDP=Growth Domestic Product at time t (Independent variable)
RIR=Real Interest Rate at time t (Independent variable)
AIR= Annual Inflation Rate at time t (Independent variable)
UR=Unemployment Rate at time t (Independent variable)
REXR=Real Exchange Rateat time t (Independent variable)
€ is the error term at time t, ? and ?i are parameters (coefficients
Finally, the regression equation for non-performing loans in logarithm (elasticity) form is specified except AIR and RIR as:
LNPLst = B0 +?1LGDPt +?2RIRt+?3AIRt+ ?4LLGt+ ?5LCFt + ?6LFPt + L?7URt + LREXRt + €t
LNPLst, for Non- performing loans’ Dependant variable
L GDPt=Log of Annual growth Rate of GDP at time t measured in percentage (Independent)
AIRt= Average inflation rate at time t measured in percentage (Independent variable)
RIRt= Real Interest Rate at time t measured in percentage (Independent variable)
LLGt t= log of Loan Growth at time t measured in percentage (Independent variable)
LLCFt= log of Credit follow up at time t measured in percentage (Independent variable)
LLFPt= log of Financial Performance at time t measured percentage (Independent variable)
LUR= log Unemployment Rate at time t (Independent variable)
LREXR=log Real Exchange Rate at time t (Independent variable)
€ is the error term at time t, B0 and ?i (1, 2,3,4,5,6,7,8, and 9) are parameters (coefficients) and tis time period.
3.6.1. JOHANSEN APPROACH
Johansen and Julius (1990) formulated a general framework for examining multiple co integrating vector, which allows the estimation of all possible co – integrating relationships, exists among the variables. Its main advantage is that it allows number of co- integrating vectors to be determined empirically. It also generates maximum likelihood estimates of the integrating vector. The following vector Autoregressive (VAR) model is basis of multivariate co- integration of Johansen maximum likelihood approach.
3.6.2. UNIT ROOT TEST
A commonly applied formal test for the existences of a unit root in the data is the Dickey- fuller (DF) test, which is a simple being the Augmented Dickey Fuller (ADF) test. The augmentation is adding lagged values (p) of first different of the dependent variable as additional repressor, which are required to account for possible occurrence of autocorrelation. In this study, the Augmented Dickey fuller test was applied which involves estimating the regression. Testing for unit roots assumes that the underlying data generating process has no intercept term and time trend. To account for the existence of an intercept term.
3.6.3. CO-INTEGRATION TESTS
In the different literature, there are two major approaches to test co-integration. These include residual-based ADF- approach proposed by Engle and Granger (Engle and Granger, 1987) and Johansen’s full information maximum Likelihood (FIML) approach (Johansen and Mueslis, 1990). In the Engle and Granger approach, first step is to test co- integration and then in the second step residual are used in an error correction model to get information on speed of adjustment in the end. The major weakness of this approach includes its low power and finite sample biased. Beside this approach cannot be used in a situation where there are more than two variables (Doladoetal., 1991). As a result, Johansen’s approach is preferred over Engle and Granger’s approach.
Johansen and Juselius (1990) described two likelihood ratio test, trace and maximal Eigen value tests, which provided co – integration rank and estimate long run parameter matrix. The trace test is based on stochastic matrix and defined as:
The null hypothesis of this test is that number of distinct co-integrating vector is less than or equal to r (i.e. no co-integrating vector) against alternative of r > 0 (i.e. one of more co integrating vector). The second test, which is called maximal Eigen value test, used for detecting the presence of a single co-integrating vector is based on the following form.
This statistic tests the null hypothesis that number of co- integrating vector is r against specific alternative (r+1) co-integrating vector. The distribution of these statistics depends on number of non – stationary components (i.e. the number of variables we are testing for co- integration) define by (n-r). Monte Carlo has derived critical value for the above tests. Which are simulated and tabulated by Johansen (1998), Harris (1995) considered trace test more powerful than maximal Eigen value test.
3.6.4. ERROR CORRECTION MECHANISM AND GRANGER CAUSALITY
Error correction mechanism explains dynamics of short run adjustment towards long run equilibrium. When variables are co -integrated there is general and systematic tendency for the series to return to their equilibrium value. It means that short run discrepancies may be constantly occurring but cannot grow indefinitely which shows that adjustment dynamics is intrinsically embodies in the co-integration theory. The theorem of Granger representation states that if a set of variables is co-integrated, it implies that residual of co-integrating regression is of order I (0), thus there exists an ECM describing that relationship. This theorem explains that co integrations and ECM can be used as a unified theoretical and empirical framework analyzing both short run and long run behavior. The ECM specification is based on idea that adjustments are made to get closer to long run equilibrium relationship. Hence, link between co-integrated series and ECM is intuitive; an error correction behavior induces co-integrated series and ECM is intuitive; an error correction behavior induces co-integrated stationary relationship and vice versa (McKay et al., 1998).
3.7. CONCEPTUAL FRAMEWORK
A conceptual framework is a conceptual model of how one theory makes a logical sense of the relationships among the several factors that have been identified as important to the problem. It discusses the interrelationships among the variables that are deemed integral to the dynamics of the situation being investigated.
Figure 01; Conceptual Framework
ESTIMATIONS OF THE MODEL AND ANALYSIS OF RESULTS.
4.1. DATA DESCRIPTION
Before starting analysis, it is often useful to see data properties like; minimum and maximum values, means value, standard deviation and the correlation of variables. The descriptive, statistics and correlation matrix of the variables used in benchmark model is represented as follows.
TABEL 4.1 Descriptive statics of variables
Variables Obs. Mean Median Max Min Std.Dev.
NPLs 38 1.262834 1.120787 6.147871 0.186507 1.038019
LG 38 1.290217 1.322219 1.643453 0.845098 0.185081
GDP 38 5.356401 5.259281 6.047835 5.007761 0.305264
FP 38 1.910632 1.929419 2.070998 0.090325 6.622250
CF 38 1.860181 1.875061 1.977724 1.544068 0.083864
AIFR 38 9.274053 0.090325 6.622250 0.540429 10.49127
RIR 38 0.090325 0.076552 0.315970 -0.349163 0.179926
REXR 38 6.622250 7.767662 44.39128 -27.76044 12.43822
AUR 38 0.540429 0.153527 1.522960 0.088246 0.616078
Sources: Author’s own estimation
Table 4.2: Correlation matrix of the variables includes in the model
NPL LG GDP FP CF AIFR REXR RIR UR
NPL 1.0000 0.184 -0.662 0.328 -0.082 -0.352 -0.238 -0.254 -0.148
LG 0.184 1.0000 0.045 -0.038 -0.027 -0.520 0.043 -0.351 0.020
GDP -0.663 0.0456 1.0000 -0.156 -0.074 -0.061 0.011 0.242 -0.049
FP 0.328 -0.038 -0.156 1.0000 0.040 -0.008 0.331 0.191 0.311
CF -0.082 -0.027 -0.074 0.040 1.0000 -0.275 0.366 -0.134 0.347
AIFR -0.352 -0.520 -0.061 -0.008 -0.275 1.0000 -0.027 0.472 -0.010
REXR -0.238 0.043 0.011 0.331 0.366 -0.027 1.0000 -0.101 0.975
RIR -0.254 -0.351 0.242 0.191 -0.134 0.472 -0.101 1.0000 -0.157
UR -0.148 0.020 -0.049 0.313 0.347 -0.010 0.975 -0.157 1.0000
Sources: Author’s own estimation from Eviews8 VAR result.
The correlation matrix shown in Table (4.2) gives approximation of the relationship between non-performing loan and its determinants. The table confirms, as expected, that log of nonperforming loan have a negative correlation with GDP, CF, AIFR,REXR,RIR,URand average inflation rate. While log of NPLs has a positively correlated with log of LG &FP. However, the table illustrates that the relationship between DBE Loan growth and nonperforming loans is particularly strong relationship.
4.2. TESTS OF STATIONARY
In any time sires data, before to proceed to estimate the model first cheek the stationary and non-stationary of variables used for the model. In order to test the presence if unit root and other properties of time series data under investigation, Augmented Dickey Fuller (ADF) test is used. Between the testing mechanism of stationary and non-stationary of variables, this method is better than others to easily detect it.
4.2.1. UNIT ROOT TEST
In the case of dickey fuller test, there may create a problem of autocorrelation problem. To confrontation autocorrelation problem, dickey fuller has developed a test called augmented dickey fuller test on three equations are:
1. Delta*Yt=?1+dYt-1+ai+et——————————— (equation 1) intercept only
2. Delta*Yt=?1+?2t+dYt-1+ai+et—————————- (equation 2) Trend and intercept
3. Delta*Yt=dYt-1+ai+et————————————— (equation 3) No Trend no intercept
Null hypothesis H0: variable is not stationary or unit root
Alternative Hypotheses H1: Stationary or not unit root
Augmented Dickey – Fuller test was conducted for testing unit roots of variables. The study checked null of the unit root against the alternative hypothesis of stationary by the ADF regressions including an intercept but not a trend and with an intercept and a linear trend. Akaike information criterion (AIC) was used to determine the optimal lag length for the augmented terms. The computed absolute value of the test statistics (Dickely-Fuller statistics) was checked against the maximum values of these criteria with the 5 percent absolute critical value for the Augmented Dickey-Fuller statistic. If the computed absolute test statistic value was greater than the absolute critical value, then we rejected the null of unit root, which means stationary in the time series. On the other hand, if absolute test statistics value was less than absolute critical value then we fail to rejected null of the unit root concluding the series
Table 4.3.unit root test result of variables at level &difference tests
Variables Level 1st differ. 2nd differ
Intercept Trend None Intercept Trend None Inter Trd none
t-st. P.va t-st P.v t-st P.val t-st P.v t-sts P.v t-st P.v t-st p t-st p t-st p
NPL -2.94 0.001 -3.53 0.089 -1.95 0.126 -2.94 0.0 3.54 0.0 -1.9 0.0 -1.9 0.0 -3.5 0.0 -1.95 0.0
LG -2.94 .0028 -3.53 0.016 -1.95 0.696 -2.94 0.0 3.53 0.0 1.9 0.0 -1.9 0.0 -3.5 0.0 -1.95 0.0
GDP -2.94 1 -3.53 0.995 -1.95 0.99 -2.94 0.0 3.54 0.0 1.9 0.0 -1.9 0.0-3.5 -3.5 0.0 -1.95 0.0
FP -2.94 0.034 -3.53 0.045 -1.95 0.84 -2.94 0.0 3.54 0.0 -1.9 0.0 -2.95 0.0 3.55 0.0 -1.95 0.0
CF -2.94 0.001 -3.53 0.008 -1.95 0.85 -2.94 0.0 -3.54 0.0 -1.9 0.0 -2.95 0.0 -3.55 0.0 -1.95 0.0
RIR -2.94 0.001 -3.53 0.005 -1.95 0.120 -2.94 0.0 -3.54 0.0 -1.9 0.0 -2.96 0.0 -3.54 0.0 -195 0.0
REXR -2.94 0.012 -3.53 0.002 -1.95 0.23 -2.94 0.0 3.54 0.0 -1.9 0.0 -2.95 0.0 -3.54 0.0 -1.95 0.0
AIFR -2.94 0.20 -3.53 0.546 -1.95 0.035 -2.94 0.0 -2.95 0.0 -3.5 0.04 -2.95 0.0 -3.54 0.0 -1.95 0.0
UR -2.94 0.62 -3.53 0.74 -1.95 0.10 -2.94 0.0 -2.95 0.0 -3.5 0.01 -2.95 0.0 -3.54 0.0 -1.95 0.0
Critical values 5%=-2.93 5%= -3.50 5%= -1.95
Sources: Author’s own estimation
The table indicates that, all variables are non-stationary at level of none of tests because of the p value is high at 5 percent of interval. While growth domestic product, average inflation rate, real exchange rate ; unemployment rate are non-stationary at intercept and trend and all the remaining variables are stationary for all tests model at 5 percent critical value. Table (4.3) result confirmed that all the variables of data series were stationary at first and second different of the test this is the next steps of testing the all series should be stationary according to Augmented Dickey Fuller (ADF) test model of eviews8 software analysis.
4.2.2. CO-INTEGRATION AND ERROR CORRECTION MODEL
Once the researcher tested the unit roots for the given data series, the next step was to estimate the co-integrating regression between the variables to check the long run relation between them. Two conditions must be fulfilled for the variables to be co-integrated. First, all the individual variables should be integrated of the same order and secondly the linear combination of these variables should be integrated to an order lower than the order of integration of the individual variables. The present study used the Johansen full information Maximum likelihood (FIML) approach. This approach, in first step, identified the order of vector Auto Regressive (VAR) and then checked the number of co integration vector among the series where it also produced long run elasticity’s. After establishing the co-integration among the variables, the study used the error correction model (ECM) to estimate the short run elasticity’s .This analysis also showed adjustment mechanism of the system to any short run shock. Lastly, Granger Causality was also estimated to check the direction of causation between the variables.
Table 4.4: Selecting the Order of VAR
VAR Lag Order Selection Criteria
Endogenous variables: NPL LG GDP FP CF AIFR REXR RIR UR
Exogenous variables: C
Sample: 1980 2017
Included observations: 36
Lag LogL LR FPE AIC SC HQ
0 -144.8569 NA 4.17e-08 8.547606 8.943486 8.685779
1. 97.63364 350.2641 6.06e-12 -0.424091 3.534706* 0.957635
2. 210.2722 106.3809* 2.65e-12* -2.181791* 5.339924 0.443489*
* indicates lag order selected by the criterion.
LR: sequential modified LR test statistic (each test at 5% level)
In the first stage of this analysis, order of VAR was identified using Schwarz basic information criterion (SBIC), Hanna-Quinn information criterion (HQIC),Akaike information criterion (AIC), and final prediction error (FPE) criteria with a maximum of their lags. In this process variables, which were include in the VAR ,were loan growth, real interest rate, average exchange rate, annual growth rate of gross domestic product, unemployment rate, average inflation rate, financial performance and credit follow up because these variables were(2) as shown earlier .As table 4.4 indicates all of the four criteria’s FPE,AIC, HQIC and SBIC recommend using one lag in the system equation model that is in the Johansen test of co-integration and vector error correction. The second step in Johansson’s procedures is to test the presence and the number of co- integrating vectors among the series in the model. The rank of the co- integrating that is the number of the co integrating vectors selected using the maximal Eigen and the Trace value test statistics.
Table 4.5: Number of co-integration vector Based on Trace statistics
Maximum Rank Eigen value Trace statistic 5% Critical value
Hypothesized Trace 0.05
No. of CE(s) Eigenvalue Statistic Critical Value Prob.**
None * 0.961576 339.8932 159.5297 0.0000
At most 1 * 0.867723 225.8258 125.6154 0.0000
At most 2 * 0.800912 155.0258 95.75366 0.0000
At most 3 * 0.743445 98.53545 69.81889 0.0001
At most 4 * 0.520955 50.92104 47.85613 0.0250
At most 5 0.358443 25.16242 29.79 0.1557
At most 6 0.240405 9.627441 15.49471 0.3105
At most 7 9.98E-05 0.003494 3.841466 0.9511
Sources: Author’s own estimation
Trace test indicates 5integratingeqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Table 4.6: Number of co-integration vector Based on Maximal Eigen Values
Maximum rank Eigen value max statistic 5% critical value
Hypothesized Max-Eigen 0.05
No. of CE(s) Eigenvalue Statistic Critical Value Prob.**
None * 0.961576 114.0674 52.36261 0.0000
At most 1 * 0.867723 70.80002 46.23142 0.0000
At most 2 * 0.800912 56.49037 40.07757 0.0003
At most 3 * 0.743445 47.61442 33.87687 0.0007
At most 4 0.520955 25.75861 27.58434 0.0841
At most 5 0.358443 15.53498 21.13162 0.2532
At most 6 0.240405 9.623946 14.26460 0.2378
At most 7 9.98E-05 0.003494 3.841466 0.9511
Sources: Author’s own estimation
Max-eigenvalue test indicates fourintegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Based on the results of trace statistic value of test statistic Table 4.5 the hypothesis of no co-integration was rejected and the study accepted the alternative hypothesis of existence of co-integration among the series. This suggests that there exist precisely one co- integrating vector in the estimated model. Hence, we can conclude that there is long run relationship between the variables, which is explained by a linear combination of four (4) variables.
Results of the Trace test confirmed that, the result obtained through maximal Eigen value test and gave us one co- integrating vector because test showed that, the value were significant at 5% level. For test statistics of the first statistic, value for the tests was greater than the 95 percent critical value.
4.2.3. ESTIMATES OF LONG RUN AND ERROR CORRECTION MODEL
Co-integration analysis offers an improved method to estimate the long run dynamic relationship among time series economic variables. The Johannes method is a form of an Error correction model (ECM) and in the presences or existence of one co-integrating vector, its parameters can be interpreted as estimates of the long run co-integrating relationship among the series (HallamandZonoli, 1993). The concepts of co-integration and error correction modeling are closely correlated as the method brings together short run and long run information in modeling time series data through an error correction model (ECM) (Ericsson, 1992). The co-integrating, once established among the variables include in the present study , the dynamic ECM structure was then considered for analysis as it saved from the estimation of counterfeit regression among the variables and also provided information about the adjustment speed to long run equilibrium (Engle and Granger , 1987).
In the estimation of an ECM for nonperforming loans, we included the same number of lags as were taken in the tests of co-integration that is one lag. The parameters from the Johansen co-integration regression were the estimates of the long run elasticity’s whereas the coefficients of the differences terms in the error correction model were the estimates of the short run elasticity. The variable of unemployment rate was significant in the long run and in the short run since the t-critical value 0.0002) were less than t-statics value for the long run 0.68 and short run 0.901respectively. The direction of these variables in the long run and in the short run was consistent as it showed positive sign with the non-performing loans, but its effects to the non-performing loans was inelastic in the long run and while elastic in the short run. The elasticity coefficient for this variable in the long run explained that one percent increase in the amount of foreign direct investment brought0.68 percent increase in the Non-performing loans in Development bank of Ethiopia.
Whereas this elasticity coefficient increased to 2.809 in the short, run which indicated that one percent increase in the amount of unemployment rate introduced 2.809percentincrease in nonperforming loans however, keeping all other factors constant. A relatively larger short run elasticity coefficient for non-performing loans with respects to unemployment rate is logical since if there is high amount unemployment rate today affect the existence of nonperforming loan currently than in the future because in the future the economy adjusted to minimize the current non-performing loan impact than today.
4.2.4. GRANGER CAUSALITY TEST
Causality means the direction of cause from one variable to other variable, which is regressed
Individually on each other. In this regard, three cases can be identified. The first type of causality is unidirectional causality from the first variable to second variable. The second type is bilateral causality and last one is the independence of variables from each other (Gujarati, 1995).Regression was run separately for each of the explanatory variable which is I (1) with the dependent variable of nonperforming loans (NPLs) and checks the Granger Causality.
Table 4.7.Pairwise Granger Causality Tests
Null Hypothesis: Obs F-Statistic Prob.
LG does not Granger Cause NPL 36 0.53189 0.5928
NPL does not Granger Cause LG 4.88294 0.0143
GDP does not Granger Cause NPL 36 2.34158 0.1130
NPL does not Granger Cause GDP 1.36718 0.2698
FP does not Granger Cause NPL 36 0.99352 0.3818
NPL does not Granger Cause FP 1.17283 0.3228
CF does not Granger Cause NPL 36 2.59037 0.0911
NPL does not Granger Cause CF 0.54315 0.5863
AIFR does not Granger Cause NPL 36 1.20372 0.3137
NPL does not Granger Cause AIFR 1.53552 0.2313
REXR does not Granger Cause NPL 36 57.7782 3.E-11
NPL does not Granger Cause REXR 0.45706 0.6373
RIR does not Granger Cause NPL 36 2.98245 0.0654
NPL does not Granger Cause RIR 0.83548 0.4432
UR does not Granger Cause NPL 36 28.6021 9.E-08
NPL does not Granger Cause UR 0.16283 0.8505
Results in Table 4.7 suggested that the listed variables have no causality between Non-performing loan and other explanatory variables.
4.2.5. DIAGNOSTIC TESTS
Diagnostics test are usually undertaken to detect model misspecification and as a guide for model improvement. In addition, it is necessary to test data for different diseases, which would mislead the output and end up with wrong interpretations and conclusions. To this end different tests namely: Breach Godfrey LM test for autocorrelation, Jarque-Bera Normality test , Chow breakpoint Test for stability of parameters , Breusc- pagan / cook-Weisberg test for heteroskedasticity , Ramsey RESET test and ARCH , were employed to assure the robustness of the model.
4.3. SERIAL CORRELATION LM TEST
Unlike the Durbin-Watson statistic for AR(1) errors , the LM test may be used to test for higher order ARMA errors and is applicable whether or not there are lagged dependent variables. Therefore, we recommended its use (in preference to the DW statistic) whenever you are concerned with the possibility that your errors exhibit autocorrelation. The null hypothesis of the LM test is that there is no Auto correlation up to lag order p, where P is a pre-specified integer .The null hypothesis of the LM test is not rejected since prob. chi square LM test of serial correlation is 0.5325, which is greater than 0.05
Table;4.9.Serial Correlation LM Test
Breach-Godfrey Serial Correlation LM Test:
F-statistic 0.272072 Prob. F (2, 15) 0.7655
Obs*R-squared 1.260230 Prob. Chi-Square (2) 0.5325
4.3.1. HETEROSKEDASTICITY TEST:
as noted in Brooks(2008), the variance of the errors must be constant (homoscedasticity). If the errors do not have a constant variance, they are said to be heteroscedastic. If this problem ignored, the standard errors could be wrong and hence any inferences made could be misleading. Hence, To test for the presence of heteroscedasticity, the popular white test was employed in this study (Brooks 2008).
F-statistic 0.737407 Prob. F(18,17) 0.7364
Obs*R-squared 15.78418 Prob. Chi-Square (18) 0.6076
Scaled explained SS 2.545417 Prob. Chi-Square (18) 1.0000
4.3.2. TEST FOR NORMALITY
In this study, the normality of the data was checked with the popular Bera-Jarque test statistic (Brooks 2008). According to Bera-Jarque test statistic, normally distributed data is not skewed and has a coefficient kurtosis of three. As shown in figure 4.1, the coefficient kurtosis(2.44) of the data in this particular study was approximate to three, and the BeraJarquestatistic had a P-value of 0.565 implying that there was no evidence for the presence of abnormality in the data. Thus, the null hypothesis that the data is normally distributed should not be rejected since the p-value was considerably in excess of 0.05.
Figure 4.1 Normality test for residuals: Bera-Jarque
Figure; 4.2Residual Graph
CONCLUSION AND POLICY IMPLICATION
The main aim of the current study is to investigate the determinants of NPLs by using from 1980 to 2017 (38years) time serious data of macro and bank specific factors of variables. In the current study, secondary data sources used in quantitative approach, both descriptive and explanatory research design, multivariate time serious models of vector error correction (VECM) vector autoregressive (VAR) model was used in addition Johansson approach is applied by using one dependent and eight independent variables: Prior to the estimation of the specified model by using Eviews8 software of multiple linear regression analysis, test for stationary were carried out using the Augmented Dickey-Fuller tests. The results from the unit root testing revealed that the entire variable used in the estimation are integrated of order one. The order of vector Autoregressive was identified using Schwarz information criterion (SBC). Hannan- Quinn information criterion (HQIC), Akaike information criterion (AIC), and final prediction error (FPE) criteria and the result reveled to use one lag. Johansen’s procedure is used to test the presence and the number of co integrating vectors among the series in the model , and results of maximal Eigen values and trace value suggested a single co-integrating vector, the existence of this single co-integrating vector leads to the estimation of the model using an error correction model. The previous studies were found different result on these variables. For example, zelalemTsigieAddis Ababa University (2013) on 8 Ethiopian private banks, Monica wanjiru at 43 commercial bank of Kenya using for these variables got same result. However, this study has signicantly different from the previous study mainly some variables correlation result to nonperforming loan.forinstance,from the previous study conclusion, average inflation and GDP had positive relation to NPL.while,the result of this study proved that totally eight macroeconomic and bank specific factors of variables on determinants of NPLs at DBE (i.e. Growth domestic product (GDP),,average inflation rate(AIFR),real exchange rate (REXR), development bank of Ethiopia loan growth(LG),Financial Performance(FP),Credit Follow up (CF) are significantly associated with NPLs. whereas two variables (i.e. unemployment rate and Real interest rate (RIR) are insignificantly associated while, RIR Sign cant Associated With NPLs but has positive or direct relation with NPL.This suggests that seven variables have significant negative relation in affecting the level of NPLs whereas unemployment rate have no impact on NPLs. The significant negative relation between growth rate in GDP and NPLs suggest that increase in
Economic growth results in the increase debt paying ability of individuals and projects because of the greater economic activities and earnings of the individuals and projects, consequently resulting in the decline of NPLs. The negative association of exports with NPLs suggests that with the increase in exports, economic activities in the economy increases, resulting in the income growth of individuals and profits of investors. Thus, individuals and investors have the fund to repay the loans, resulting in the decline of NPLs. Furthermore, the negative relation between average inflation and NPLs suggests that with the inflation the equity value of the banks declines, resulting in the growth of banks riskiness. Banks in order to improve their equity value show short-term profitability by extensive lending and cost efficiency by reducing their expenses on loan allocation, monitoring and controlling, which leads to the decline in NPLs.
There is a significant positive association between interest rate and NPLs, with the increase in interest rate the difference between deposits rate and lending rate increases. Only low quality borrowers show willingness to pay high interest rate, thus banks in order to earn lend more funds to the low quality borrowers. Low quality borrowers by using bribes to bank officials and other corruption supported practices do not repay the loan, consequently results in the growth of NPLs in addition As the interest rate increases in the economy, the low quality borrowers (both individuals and investors) have no income and profit to pay back there loans and defaults leading to the growth in NPLs.
Finally, the progression of bank loan growth to provide to their borrowers/clients has positive association with NPLs. As credit growth of banks increased, the probability of increasing of nonperforming loan also increases which leads to increase the amount of nonperforming loans of the bank. When we see that as per the findings, the amount of nonperforming loan has negative impact on the performance and sustainability of Development bank of Ethiopia.
More over when we see the short run and long run relationship of variables, as per the Estimation of Error Correction Model and Estimation of long run elasticity respectively indicates that, GDP growth rate, Real interest rate, average inflation rate, average exchange rate, development bank of Ethiopia credit/ loan growth, and total annual exports has both long run and short run effect on NPLS, However unemployment rate has no impact either in the short run or long run.
5.2. RELATED POLICY IMPLICATIONS (RECOMMENDATION)
The findings of the determinants have policy related implications for the development banks of Ethiopia (DBE). DBE can use the findings of macroeconomic model to predict changes in the NPLs to take precautionary measures to prevent any financial crisis. The bank can use the performance of economy, interest rate level, and inflation rate, exchange rate while providing their lending or allocating loans. Banks can look for the growth in economy while extending their loans or at the time of extensive lending because dining the downturn of economy the level of NPLs can increase.
DBE should do regular loans supervision and review of the interest rate charged on the loans
because with the increase in real interest rate lending of the banks declines. In order to prevent any bad loan banks should strictly follow standard procedures of credit allocation and lend only to good credit worthy borrowers in order to prevent NPLs. Furthermore, banks do extensive lending during inflation to utilize the funds but lend also to the low quality borrowers, who defaults when interest increases. Thus, banks should not go for extensive lending during inflation in order to prevent the future NPLs. Furthermore, banks should follow credit allocation process during allocations of fluids.
Finally, banks can lend to the investors during the high exports because of the high economic
Activities, but should not lend at the time of low or no exports time.
DBE should develop a framework, which can include the macroeconomic and bank specific factors such as GDP growth, real interest rate, inflation, exchange rate, loan growth and unemployment rate to monitor the stability and soundness of the banking sector.
The government can also play important role in improving the level of NPLs in the economy by influencing the macroeconomic variables. For instance, government should create conducive policy for thus projects that create forward and back ward linkage, low level of unemployment, economic activities in the economy and high exports. In order to increase the exports of tile country government can provide incentives to the manufacturer by developing basic infrastructure, reducing taxes, providing low cost loans and can help exporters in exploring new international markets. The government can increase the economic activities, employment rate, production level and exports by doing special agreements with the neighboring countries for free trade.
Currently this study has used nine macroeconomic variables to investigate their impact/relationship with on NPLs. whereas future studies can use other macroeconomic variables (like, total import, investment, and consumption) and borrower and bank specific determinants of nonperforming loans to investigate NPLs behavior. The results of such studies will be beneficial for the policy makers, because it can help to anticipate any adverse effect of each variable on the level of NPLs.
The finding of current study and future studies by using above-mentioned variables can be helpful in predicting, controlling banking crisis in the development bank of Ethiopia in particular, and Ethiopian banks in general. Furthermore, current study provides the impact of each variable on NPLs by using 38years time serious data. Future studies can use the paneled
data of Ethiopian banks to investigate incorporate with additional determinants of NPLs in the country.
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urces in the current document.