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DEDICATION
To my wife Bernadette, my daughters Benita and Grace and my son Bruce«you give me the reason to make this world a better place! I love you. I want you to remember the following wise words:
"I know of no more encouraging fact than the unquestionable ability of man to elevate his life by conscious endeavour". Henry David Thoreau- American philosopher
i
ACKNOWLEDGEMENTS $V6WHSKHQ5&RYH\DXWKRURIWKHµ6HYHQ+DELWVRI+LJKO\HIIHFWLYHSHRSOH¶ put LW³Interdependence is a higher value than independence´WKLVSLHFHRIZRUN benefited from the critical reviews and suggestions for improvement from Prof Frank Tailoka, Prof John Lungu, Dr Thomas Taylor, Mr Nathan DeAssis, Mr Patrick Musokwa and Mr Donald Mwenya, all from the Copperbelt University in Zambia. Among the many institutions that provided the necessary data for the research, gratitude goes to Mr Pinalo Chifwanakeni, Director of the Zambia Institute of Banking and Financial Services, Ms Pricilla Lesa , Chairperson Credit Reference Bureau Initiatives on behalf of The Bankers Association of Zambia, Standard Chartered Bank Plc, staff at the Bank of Zambia, Development Bank of Zambia, Central Statistical Office, Pensions and Insurance Authority and the Global Credit Rating Co. Africa.
ii
PREFACE Availability of credit to individuals and businesses is one of the determinants of growth in any economy and what types of businesses develop. Credit provides WKH SXUFKDVLQJ SRZHU WR ³EX\ QRZ DQG SD\ ODWHU´ EDVHG RQ IXWXUH HDUQLQJV Buying now on credit, rather than waiting until one has saved all the money needed to invest, creates immediate effective demand in any market. This demand creates employment opportunities which offer more people the opportunity to buy now and pay later, thus creating even more jobs. Access to credit is challenging where there is perceived high credit risk when lending to the private sector whether dealing with individuals or small and medium sized enterprises. This book reviews literature from a number of countries in the world on the effect of credit reference services on access to credit and interest rates. Furthermore, as a basis for justifying the creation of credit reference services to mitigate credit default in the financial sector, this book presents evidence from the developing world using Zambia as a case study. Anyone who wishes to know how credit reference services work will find this book useful. Financial services professionals, policy makers and scholars will find this work especially useful because evidence from developing countries is rare on this subject.
Bruce Mwiya
iii
List of Abbreviations and Acronyms BAZ
Bankers Association of Zambia
BFS Act
Banking and Financial Services Act of 1994
BoZ
Bank of Zambia
CAT
Central African Time
CRB
Credit Reference Bureau
CRBAL
Credit Reference Bureau Africa Limited
FIRST
Financial Sector Reform and Strengthening Initiative
FSAP
Financial Sector Assessment Programme
FSDP
Financial Sector Development Plan
GDP
Gross Domestic Product
IMF
International Monetary Fund
LDC
Least Developed Countries
LUSE
Lusaka Stock Exchange
NAPSA
National Pension Scheme Authority
PCR
Public Credit Registers
PRSP
Poverty Reduction Strategy Paper
PSDP
Private Sector Development Plan
SEC
Securities and Exchange Commission
SI
Statutory Instrument
SPC
Supervisory Policy Committee of the Bank of Zambia
SPSS
Statistical Package for Social Sciences version 12.0
USA
United States of America
WDI/WB
World Development Indicators by the World Bank
iv
TABLE OF CONTENT
Table of Content
Page v
List of Appendices
vii
List of Tables
viii
List of Figures
ix
Chapter One: Background Of The Study 1.0
Introduction
1
1.1
History of Credit Default in Zambia
2
1.2
Credit Reference Bureau Initiatives in Zambia
6
1.3
The Problem statement
8
1.4
Research Questions
9
1.5
Objectives of the study
9
1.6
Significance of the study
10
1.7
Scope of Study
11
1.8
Organisation of the rest of the Document
11
Chapter Two: Literature Review 2.0
Introduction
12
2.1
Zambian Credit market
12
2.2
Factors affecting interest rates
14
2.3
Key stakeholders in lowering lending rates
18
2.4
Empirical Work Done in Foreign Credit Markets
20
2.4.1
Empirical models on the role of credit information systems
21
2.4.2
Why would lenders share information
28
2.4.3
Predictive Power of Bureau ±based Risk models
30
2.4.4
Macroeconomic evidence
31
2.4.5
Microeconomic evidence
32
2.4.6.
3ROLF\DQG2SHUDWLRQDOLVVXHVLQ&UHDWLRQRI&5%¶V
35
2.4.7
Designing Information sharing in Developing Countries
41
2.5
Summary
42
v
Chapter Three : Theoretical and Conceptual Framework 3.0
Introduction
44
3.1
Theoretical Case for information Sharing in the Credit Process
44
3.2
Lack of Comprehensive Credit information sharing and its implications
48
3.3
The Cost of Money
49
3.4
Conceptual Frame work
52
3.4.1
Hypotheses
53
3.5
Summary
55
Chapter Four: Methodology and Model specification 4.0
Introduction
56
4.1
Method of Data Collection
56
4.2
Method of Data Analysis
57
4.2.1
Model Specifications
57
4.3
Limitations of Study
60
Chapter Five: Research Findings and Analysis 5.0
Introduction
62
5.1.
Credit Default
62
5.2
Credit Default and Private Sector Credit Extension Relationship
66
5.3
Discussion and Interpretation of Results on Credit Default
69
5.4
Some Possible Causes of Credit Default
75
5.5
Impact of Credit on the National Economy
78
5.6
Summary
84
Chapter Six: Conclusions and Recommendations 6.0
Introduction
85
6.1
Research Objectives, Hypotheses and Methodology
85
6.2
Findings
86
6.3
Conclusions
87
6.4
Recommendations
88
6.5
Area for further Study
89
Bibliography and References
90
vi
List of Appendices
page
Appendix 1 SPSS Regression Results Tables
94
Appendix 2 Time series trend analysis and simple linear regression Model notes
105
Appendix 3 Credit Rating Definitions and Process
108
Appendix 4 Poor Credit Culture in Zambia ±FIRST Project
112
Appendix 5 Glossary of Key Terms
114
vii
List of Tables
page
Table 3.1
The Credit Process
45
Table 3.2
7KH)LYH&¶V2I&UHGLW
46
Tables 5.1
Regression Results of Credit Default by Months
94
Tables 5.2
Regression Results of Credit Default by Private Sector Credit
95
Tables 5.3
Regression Results of Private Credit by Government Credit 96
Tables 5.4
Regression Results of GDP by Government Credit
97
Tables 5.5
Regression Results of GDP by Private Sector Credit
98
Tables 5.6
Regression Results of Private Credit by Months
99
Tables 5.7
Regression Results of Government credit by months
100
Tables 5.8
Regression Results of Credit default by Government credit
101
Tables 5.9
Regression Results of Total Banking Assets by Months
102
viii
List of Figures
Page
Figure 1.1
Government and Private Sector Domestic Borrowing 2003
3
Figure 1.2
Access to loans by Size of private sector firm
4
Figure 2.1
Interest Rate Spread 2002
15
Figure 2.2
Credit Reporting around the World
21
Figure 3.1
Determinants of interest rates
49
Figure 4.1
+DQNH¶V0HWKRGRI'HIODWLQJ7LPH6HULHV'DWD
57
Figure 4.2
Mathematical Model for Credit Default Analysis
58
Figure 4.3
Mathematical Model for Credit Default and Private Sector Credit
59
Figure 4.4
Mathematical Model for GDP and Private Sector Credit
60
Figure 5.1a
Scatter Plot of Credit Default by months
63
Figure 5.1b
Test of Significance Results
65
Figure 5.2a
Scatter Plot of Credit Default by Private Sector Credit
67
Figure 5.2b
Test of Significance Results
69
Figure 5.3
Scatter plot of Private Sector credit by months
70
Figure 5.4
Scatter plot of Govt Credit by months
71
Figure 5.4a
Scatter plot of Private Sector credit by Government Credit
72
Figure 5.4b
Test of Significance Results
74
Figure 5.5
Scatter plot of Credit default by Government Credit
75
Figure 5.6
Scatter plot of GDP by Government Credit
78
Figure 5.7
Test of Significance Results
81
Figure 5.8
Scatter Plot of GDP by Private Sector Credit
81
Figure 5.9
Test of Significance Results
84
Figure 5.10 Scatter Plot of Total Banking Assets by months
ix
104
Chapter One Background of the Study 1.0
Introduction Equity and Debt financing through financial markets for purposes of investment in the private productive sector is one of the key prerequisites to increased Gross Domestic Product (GDP) growth rates in any economy. Other African countries like Ghana have also identified low access to debt finance as a mDMRU FRQVWUDLQW WR SULYDWH LQYHVWPHQWV¶ contribution to GDP (Asante, 2000). 7KHDYDLODELOLW\RIFUHGLWGHWHUPLQHVKRZIDVWDFRXQWU\¶VHFRQRP\JURZV and what type of business develops (Koch, 1988). In other words, credit is one of the key engines that drive economic growth. It provides individuals DQG EXVLQHVVHV ZLWK WKH SXUFKDVLQJ SRZHU WR ³buy now and pay later´ based on future earnings. Buying now on credit, rather than waiting until one has the money to purchase operational inputs and capital goods creates effective demand. The demand creates employment opportunities which offer more people the opportunity to buy now and pay later, thus creating even more jobs. In Zambia, while credit extension to the public (Government) sector was high at 31% of GDP as at 31 December 2003 (National Budget, 2004) credit extension to the private sector was low, estimated at 8% of GDP by the World Bank as at end of 2003 (World Bank Report, 2004). Among the major reasons advanced by the Banking sector to explain this low credit extension was the high credit default risk in the Zambian credit market.
1
1.1
History of Credit Default in Zambia Credit default by borrowers, both individuals and firms, in the Zambian banking sector was cited by financial institutions to be one of the major impediments to extension of credit to the private sector. According to the Banking and Finance Magazine (2004, pg 5), the Zambian banking system had historically been characterized by a long history of credit delinquency, partly perpetuated by the weak legal framework and by the unfavourable economic environment. As a result a belief emerged over time that lending to small and medium scale businesses and individuals was risky. This further led to difficulties for Zambian investors in accessing finance because of the general poor credit history and the lack of infrastructure in the financial sector to trace bad debtors and thereby open the door to good debtors. Information was therefore needed that would enable banks and other non-bank credit grantors to finance credible borrowers. Comprehensive information about the credit history of a borrower can only be obtained from a Credit Reference Bureau, which was non-existent in Zambia. However, others argued that debt delinquency in Zambia had no genetic linkage. It was an offspring of high costs. If the banking sector could narrow the gap between saving and lending rates, delinquency could be reduced. Of course it should be noted that the high lending rates were as a result of several factors including inflation levels, Government bond yields, and not forgetting the perceived risks for lending to particular sectors. Whichever way one looked at this issue, it was a vicious cycle that needed to be addressed. According to a World Bank Report, =DPELD¶V Country Economic Memorandum (2004), the ratio of private sector credit to GDP at eight percent was one of the lowest in subSaharan Africa. The high returns on Government debt caused formal sector finance to switch to treasury bills, crowding out credit to the private sector (see Figure 1.1 below). The document noted that banks preferred
2
to invest in banks abroad and in Government debt adding that by 2003 RYHUKDOIRIFRPPHUFLDOEDQNV¶DJJUHJDWHWRWDODVVHWVZHUHLQ*RYHUQPHQW securities. Figure 1.1 Government and Private Sector Domestic Borrowing 2003
1,623,904,000 ,000.00
6,200,000,000 ,000.00
Government Borrowing 79%
Private Sector Domestic Borrowing 21%
Source: National Budget 2004 and World Bank Report 2004
Increased private sector credit was usually an indicator that businesses were borrowing from the banking system to finance either new investments or expansion of existing projects that enhance job creation. The Financial Sector Development Plan (FSDP, pg 63, 2004) noted that financial intermediation in Zambia was low. Zambian banks were holding a larger proportion of their assets in government securities and foreign assets than other sub-Saharan African countries. Zambian banks held a significant part of their foreign deposits outside the country. This reduced the resources available for lending to the private sector (see Figure 1.2 below). Further, Zambia had one of the highest ratios of public sector 3
credit to total commercial bank assets on the continent, at 53% as at 31 December 2003 (FSDP, 2004). Figure 1.2 Access to Loans by size of private firm in Zambia as a percentage of total number of private sector firms
40 35 30 25 20 15 10 5 0 Total
Small
Medium
Large
Source: Dr Silane Mwenechanya, on PSDP presentation (2004)
The Banking sector was working up to the reality that the days of earning supernormal returns on government bonds and treasury bills were approaching their end. In other words, commercial banks relied on nontraditional forms of generating revenue (such as Treasury Bills and Government Bonds) for a long time. Some of the reasons for this state of affairs were that the risk-free government securities provided commercial banks with supernormal investment opportunity such that banks had not considered it prudent to take on the high risks associated with lending to
4
the private sector (Banking and Finance Magazine, pg 4,2004). The past few years witnessed a situation where the returns on 28-day treasury bills plummeted from 35 percent to 5 percent. At the time of this work, the Treasury bill interest rate hovered around 19 percent, which was about the annual inflation rate at 19.4 percent (CSO, July 2005). Implications for commercial Banks were that they must look at alternative sources of interest income and non-interest income. That is, banks must lend more to the private sector than before. But the problem was that Zambian borrowers were believed to have a weak credit culture. It was possible to default on loans without affecting RQH¶V FUHGLW UDWLQJV ZLWK RWKHU ILQDQFLDO LQVWLWXWLRQV 7KLV GLVFRXUDJHG borrowers from repaying their outstanding loans in both failed and operating banks. Related to the above, there was a tendency among borrowers to have their assets (used as collateral) overvalued in relation to the amount of loans they obtained. This encouraged borrowers to default on their loan repayments. Almost all lending in the commercial banking sector was short-term partially as a measure to control credit risk and partially because sources of finance (deposits) were largely of a short-term nature. The Bank of Zambia was urging the banks through the Bankers Association of Zambia to establish a Credit Reference Bureau in the industry to screen truant and problematic customers in order to address the incidences of loan repayment delinquency. Given the history of credit delinquency by the public, the public could play an important role in ensuring that they developed a culture of paying back loans. This would help reduce credit risks and therefore credit default risk premiums charged by commercial banks on loans to the private sector. The Banking and Finance Magazine (2004) concluded that a Credit Reference Agency would help businesses avoid the serial bad debt 5
culture in Zambia. People and businesses would qualify for financing investment schemes based on their credit worthiness. It further added that one could not begin to count the number of institutions that have collapsed due to a heavy burden of bad debt. It was believed that one of the causes of the collapse of some banks and hire-purchase shops (credit grantors) like Smart Centre, Supreme Furnishers and Hifi was due to nonrepayment of debt by debtors. 1.2
Credit Reference Bureau Initiatives in Zambia Concerned with the limited contribution of the financial sector to economic development, the Government of the Republic of Zambia devised and formulated policy mechanisms for addressing the identified obstacles within the framework of the Poverty Reduction Strategy Paper (PRSP) whose implementation started in 2002. In line with the PRSP framework, the World Bank and the International Monetary Fund (IMF) undertook an assessment of the financial system through the Financial Sector Assessment Programme (FSAP), which identified, inter alia, a poor credit culture in Zambia. Over the years there has been a culture of nonpayments of loans and bank facilities, and when loans and facilities fall into arrears, it becomes an extremely difficult and lengthy process for the banks and other lending institutions to recover their money. The study suggested that if the economy was to grow, this second challenge had to be addressed most effectively in order for more Zambian businesses and individuals to have access to finance. Stemming from the FSAP, the Government developed the first Financial Sector Development Plan (FSDP), which was approved in June 2004. In recognition of the poor credit culture, the FSDP had as one of its recommendations the establishment of a private sector-led Credit Reference Bureau (CRB) by 31 December 2004.
To facilitate the
construction of a CRB, it was recognised that an appropriate regulatory
6
framework for the sector needed to be framed. This was because of the absence in the Zambian law of an explicit framework for licensing, regulating and supervising credit reference activities and service providers. The Bank of Zambia (BoZ) was to provide oversight and a regulatory framework to the industry as provided for under the Banking and Financial Services Act (BFS Act) Chapter 387 of the Laws of Zambia of 1994. The lack of information led the BoZ to undertake a desktop research during the last quarter of 2004. This research reinforced the need for the BoZ to develop an appropriate legislation for this industry. The results of this research were presented to the Supervisory Policy Committee (SPC) meeting on 24 November 2004, which mandated that the BoZ carry out further work in this area. Since the CRB was a private sector led initiative, the Bankers Association of Zambia (BAZ) opted to float a tender for the provision of credit reference services in August 2004.
There were four bidders for the
provision of credit reference services namely Credit Reference Bureau Africa Limited (CRBAL), KreditInform of South Africa, M & N Associates and Transtrust Zambia Limited, out of which CRBAL was selected as a preferred service provider.
According to the tender documents the
CRBAL was supposed to provide negative-only information for a closeduser-group of commercial banks (BoZ, 2005). This was to be at variance with international best practice that requires that both positive and negative information be reported and that all credit grantors provide information to the CRB. Following various meetings with the BAZ and CRBAL, the BoZ approached FIRST Initiative for Technical Assistance on credit reference services during January of 2005 (www.firstinitiative.org, 11 November 2005 12:01 hours and also see Appendix 4). The consultant contracted 7
by FIRST Initiative was in the country for two weeks and held consultations
with
various
stakeholders. The
BoZ
arranged
two
presentations on credit reference services to stakeholders and Chief Executive Officers of commercial banks on 28 February and 4 March 2005, respectively. Subsequent to the presentations and consultations with various stakeholders, the consultant prepared a report that was submitted to the BoZ. This report recommended the following; x
That a law specific to CRBs be developed before the establishment of a CRB
x
The establishment of a private CRB as opposed to a public registry
x
That the CRB reports both positive and negative information
x
That a CRB model be adopted instead of a closed-user-group
x
%R= XQGHUWDNHV VWXG\ WRXUV RI MXULVGLFWLRQV ZLWK PDWXUH &5%¶V DQG those in the process of implementing credit reporting systems.
x
Development of a consumer education program especially for a cash society like Zambia.
At the time of this work, the BoZ was in the process of drafting the legal framework for operations of Credit Reference Bureaus. 1.3
The Problem Statement From the foregoing, the problem studied in this research was that Zambian private borrowers, both individuals and firms, were perceived to have a bad credit culture and therefore creditors perceived great credit risk in lending to the private sector. Because of this perception, there was low credit extension to the private sector. An increase in access to credit could contribute to increased investment and boost consumer demand resulting in increased economic activity. The level of credit extension to the Zambian private sector from Banks was the lowest in the Sub-Saharan Africa at 8 percent of GDP. To achieve any sustainable and meaningful growth in the economy, both equity and debt capital for productive 8
capacity expansion were major prerequisites. However, because of perceived high credit default risk, financial institutions would rather lend to less risky sectors such as Government through purchase of government securities and offshore investments. Whether this perception of high credit default rate was real or just imaginary was yet to be empirically proven. Hitherto, there had not been an analysis and determination of the rate and trend of credit default in the banking sector in Zambia. Neither had there been an empirical analysis of the relationship between credit default and private sector credit extension in Zambia. 1.4
Research Questions Arising from the background literature and the statement of the problem, this work raised the following research questions:
x What was the rate of credit default in the Zambian banking sector? Was credit default increasing or decreasing?
x Was there a relationship between Credit default and bank credit extension to the private sector? How strong or weak was the relationship?
x Was there a relationship between economic activity and access to credit by the private sector?
x In credit markets that introduced credit-reporting systems, what was the impact on access to credit by the private sector? 1.5
Objectives of the Study Arising from the research questions above, this work had the following objectives: x
To determine the Credit default rate and trend in the banking sector
x
To determine whether there was a relationship between credit default and the level of private sector credit extension in the banking sector. 9
x
To establish if there was a relationship between economic activity and access to credit by the private sector.
x
To establish from the literature review what the effect of credit reporting systems was on private sector access to credit in markets that had introduced them.
x
To confirm or disapprove the establishment of the Credit Reference Bureau.
1.6
Significance of the Study To begin with, establishing a positive linkage between credit information sharing and increased access to credit would provide impetus to efforts in the banking sector to establish a comprehensive credit reporting system. This would result in higher GDP growth as a ripple effect of heightened economic activity due to increased access to finance. Secondly, time and again the banking sector said that the credit culture in Zambia was poor. However there had not been an empirical study to provide the financial market participants with a sector analysis of credit default rate and trends in Zambia. This research was a first in that regard. In addition, the financial sector regulators would have an additional basis as they seek to persuade the executive and legislative arms of government to speed up changes in the legal framework to improve depth and efficiency in the sector. The work would also serve as a reference document and basis for other sectors to participate in the work of setting up a credit reference bureau. These sectors include Utility companies (Electricity, water, and telephone), hire-purchase firms and other credit grantors that wish to mitigate credit default. Lastly, this being a new area of research in Zambia, the work would serve as literature review for further studies to be undertaken in the field. 10
1.7
Scope of Study This study focused on determining the credit default rate and trend and the relationship between credit default and extension of credit to the private sector in the Zambian banking sector. The study used secondary data from the banking sector as a basis for statistical analysis.
1.8
Organisation of the Work This chapter has shown the background of difficulties faced by the Zambian private sector in accessing credit. The Chapter has also introduced the happenings in the Zambian banking sector regarding efforts towards credit information sharing among lenders as one of the means to reducing credit default risk. The problem statement was made in the context of prevailing low levels of private sector access to finance. The significance of the study was highlighted, preceded by research questions and objectives. Chapter two deals with detailed literature review both within and outside Zambia regarding the role of credit information sharing in credit markets. Chapter three deals with the conceptual and theoretical framework of the study to arrive at hypotheses that guided data collection and analysis. Chapter four outlines method of data collection, method of data analysis and limitations of the study. Chapter five presents, analyses and interprets the findings. Lastly Chapter six concludes and makes recommendations.
11
Chapter Two Literature Review 2.0
Introduction Chapter one presented the problem background including objectives of the study. This chapter seeks to review literature on publications regarding the subject of credit information sharing among lenders and how this sharing impacts on access to credit by borrowers in a financial system. It was worth noting that in Zambia, not much publication had been done on the subject matter and so the bulk of the literature available for review was from studies undertaken outside Zambia. The chapter ends with conclusions drawn from the literature review.
2.1
The Zambian Credit Market According to the Banking and Finance Magazine (2004), the financial services sector was lacking in the presence of a credit reporting system to facilitate comprehensive credit information sharing. Plans were still in the pipeline by the Bankers Association of Zambia (BAZ) to establish a private Credit Reference Bureau (CRB). Potential users of a CRB were banks, nonbanks like insurance companies, pension funds, and leasing companies. Utility companies, other service providers and lenders involved in hire purchase credit schemes could also use services of a CRB. The idea to form a CRB in Zambia was conceived in 1996/97 for purposes of establishing a repository for negative customer information to keep track of bad borrowers in the banking system. Subsequently, a company was LQFRUSRUDWHG WR WDNH FDUH RI WKH EXVLQHVV RI ³ILQDQFLDO FOHDULQJ´ EXW WKH LGHD was later dropped in preference to outsourcing (BAZ, CRB ±USA Study Tour Report, 2005). Motivations for creation of a CRB were: 12
x That the Zambian banks were potentially at risk in their lending portfolios as a result of lending to persons that had possibly performed badly at other banks due to lack of a tracking mechanism for delinquent borrowers. x This state of affairs existed because there was no means available to collect, interpret, and disseminate intelligence for the joint benefit of all Zambian financial institutions. Although there was liaison between the branches of banks, this was not sufficient to identify and control unsuitable borrowers. In contrast to the Zambian situation, Europe and America have access to VHYHUDO FUHGLW EXUHDXV OLNH 'XQ DQG %UDGVWUHHW 6WDQGDUG DQG 3RRU¶V DQG 0RRG\¶V 6HYHUDO FUHGLW UHSRUWLQJ DJHQFies such as Joint Bank Clearance (JBC) and International Trust Company (ITC) serve the South African credit market. Credit Bureaus are independent agencies that collate information on individuals, organisations, industries and countries (Edward, 1990). In other words, a Credit Bureau is a business that provides information to lenders on past behaviour of clients to help lenders make decisions in providing credit facilities to customers. Credit Bureaus do not decide on who gets credit, neither do they grant credit. They are in business to help consumers get credit cards, mortgages and other loans (BAZ, CRB ±USA Study Tour Report, 2005). 7KH EXUHDX ZRUNV DV D GDWD EDQN RI ³EODFN LQIRUPDWLRQ´ DQG ³ZKLWH LQIRUPDWLRQ´ DERXW ERUURZHUV¶ FUHGLW KLVWRULHV DQG FKDUDcteristics.
Black
information is negative information. For instance, information regarding default history. White information is positive information. For instance, information regarding other borrower characteristics and good credit repayment history. Users of information from Credit Bureaus have to subscribe to access this information (BAZ, CRB ±USA Study Tour Report, 2005). 13
Once a CRB is established in Zambia, it will be a useful tool in curbing money laundering, as customers will be vigorously screened in adherence to the ³NQRZ \RXU &XVWRPHU 3ROLF\´ DPRQJ EDQNV DQG RWKHU OHQGHUV &UHGLWRUV ZLOO have access to real time financial opportunities. This will give them updated information to aid lending decisions thereby increasing credit market efficiency. Lastly, the Banking and Finance Magazine (2004) noted that there will be a reduction of firms and individuals becoming serial bad debtors as any negative information held by the CRB will impact future borrowing opportunities. The Government of the Republic of Zambia made mention of the relevance of D &5% LQWKH 1DWLRQDO %XGJHW RI VD\LQJ « ³2QH of the weaknesses highlighted in the financial sector is the poor credit culture. Therefore, there is need to restore public confidence in the sector. The Bank of Zambia in consultation with the Bankers Association of Zambia is working on initiatives to establish Credit Information and Rating Bureau in Zambia as a step towards improving the credit culture in the financial sector. Currently, many new viable investment plans cannot be financed due to the past high debt GHOLQTXHQF\OHYHOV´ 1DWLRQDO%XGJHW 2.2
Factors Affecting Interest rates in Zambia According to a World Bank Report (2004), Zambia had about the highest interest rate spread in the Sub-Saharan African (SSA) region as depicted from the average spread in Figure 2.1 below. According to the Banking and Finance Magazine (2004) interest rates play a cardinal role in economic development, as they are a price for loanable funds, which the productive private sector needs to improve productivity and expand production. Increased private sector credit is usually an indicator that businesses are borrowing from the banking system to finance either new investment or expansion of existing projects that enhance job creation.
14
Figure 2.1 Interest rate spread 2002
25.00% 20.00% 15.00%
SSA Average
10.00% 5.00% 0.00%
Za
ia mb
Ke
a ny
an Ug
da
ia ue an biq nz m a a T z Mo
S.
ia As
E.
ia As
Source: World Bank WDI 2004
There were several factors that could explain the high levels of lending rates in Zambia: Yield Rates on Government Securities Commercial banks normally anchor lending base rates to inflation. However, with the declining levels of inflation, after 1993, commercial banks have in recent times anchored their lending rates to yield rates on government securities, particularly the 12-month government bond. Yield rates on government bonds have been sustained at high levels mainly on account of JRYHUQPHQW¶V KLJK EDQN ERUURZLQJ OHYHOV HVWLPDWHG DW SHUFHQW RI domestic credit) to fund government operations (Banking and Finance, 2004). Given a certain level of loanable resources, commercial banks have at least two options; either to lend risk-free to government or to take a risk and lend to 15
non-government clients using an appropriate yield rate plus a premium for risks undertaken (Banking and Finance, 2004). Level of Statutory Reserve Requirements At 17.5 percent, the opportunity cost of funds held at BoZ as statutory reserves were considered to be too high as it was included in the cost of conducting banking business. In light of this, the Bank of Zambia decided to adjust the ratio downwards to 14 percent effective 31 October 2003 with the view to increasing the availability of loanable funds and consequently contributing to lowering of lending rates (Bank of Zambia quarterly Report, 2003). Persistent High Inflation The expected path of future inflation made banks and other suppliers of credit apprehensive about the future value of their loans at maturity. Banks therefore tended to increase their lending rates in order to maintain the value of their loans. Weak Policy Credibility In Zambia, macro-economic policy still suffered weak credibility. This was because many economic agents doubt the commitment of policy makers to their own policy pronouncements. When credibility is left wanting, inflation expectations can easily be translated into actual inflation whenever a single commitment is abrogated. In such an environment bankers become risk averse and respond by resisting a reduction in their lending rates. History of High Credit Default According to the Banking and Finance Magazine (2004), the Zambian banking system was historically characterized by a long history of credit delinquency, partly perpetuated by the weak legal framework and by the unfavourable economic environment. As a result, a belief emerged that
16
lending to small and medium scale businesses and individuals had a lot of risks. This led to further difficulties for Zambian businesses and individuals in accessing finance because of the general poor credit history and the lack of infrastructure in the financial sector to trace bad debtors and thereby open the door to good debtors. Information was therefore needed that would enable banks and other non-bank credit grantors to finance credible borrowers (The Post, April 14, 2005). Political factors The foregoing history of credit default was being exacerbated by the fact that trends of credit default seemed to largely be perpetrated by people in leadership especially the politicians. It was worth noting that problems of nonrepayment of loans by politicians began in the second republic when loans were openly viewed as disguised grants exchanged for political support, with repayment never seriously envisaged. It is sad that the situation has not changed in the third republic as evidenced by the number of high profile politicians who seem to have vowed not to pay back loans unless dragged to court (Post, November 1,2005). This conduct notwithstanding, these defaulting politicians continued to hold political offices. Political commitment to fight credit default is one of the major prerequisites for the effectiveness of credit reference bureaus. As noted earlier, the high lending rates were as a result of several factors including inflation levels, Government bond yields, and not forgetting the perceived risks in lending to private borrowers. Whichever way one looked at this issue, it was a vicious cycle that needed to be addressed. According to a :RUOG%DQN5HSRUW RQ=DPELD¶V&RXQWU\(FRQRPLF0HPorandum, the eight percent ratio of private sector credit to GDP in Zambia was one of the lowest in sub-Saharan Africa. The high returns on Government debt caused formal sector finance to switch to treasury bills, crowding out credit to the private sector. The document noted that banks preferred to invest in banks 17
abroad and in Government debt adding that by 2003 over half of commercial EDQNV¶DJJUHJDWHWRWDODVVHWVZHUHLQ*RYHUQPHQWVHFXULWLHV
2.3
Key Stakeholders in Lowering Lending Rates The key players in the determination of interest rates are the government, the central bank, commercial banks and the public. These stakeholders have to play their role if interest rates are to reduce. A reduction in the statutory reserve ratio alone was not adequate to make borrowing affordable. In this regard, the Bank of Zambia was urging other key players in reducing the level of interest rates to do their part to contribute to the lowering of the cost of borrowing. In recent past, the government also committed itself to reducing its borrowing from the banking system in the form of government securities. Government Securities Yield Rates The Government securities yield rates are interest rates that a government pays to the holders of government securities such as treasury bills and government bonds. To reduce these interest rates, the focus must be reducing both the level of government borrowing and the existing stock of domestic debt. Governments such as those of Tanzania and Botswana that had followed this path recorded low lending rates (The Post, 15 May, 2005). Following the decrease in the Statutory Reserve Ratio and the subsequent increase in excess liquidity in the banking system, Treasury bill yield rates trended downwards from 33.8 percent in December 2002 to 23.5 percent as at 8 December 2003. In 2005, the rates hovered around 16 percent ±20 percent due to reduced Government borrowing. The Government bond rates hovered around 22 percent in 2005 (Bank of Zambia April Quarterly Report, 2005). Bank of Zambia 7KH %DQN RI =DPELD¶V REMHFWLYH KDV EHHQ WR DWWDLQ DQG VXVWDLQ SUHIHUDEO\ single digit annual inflation, thereby contributing to the lowering of lending 18
rates. According to the Bank of Zambia website accessed on April 20,2005 (www.boz.zm), other measures undertaken by the Bank of Zambia to reduce interest rates included; x
Call for more competition in the banking sector The Bank of Zambia observed that the banking system was not competitive. As a result, it requested Zambia Competition Commission to look into this issue (as it was not under the jurisdiction of BOZ) with the aim of promoting a competitive banking system.
x
Establishment of a Credit Reference Bureau The Bank of Zambia was urging the banks through the Bankers Association of Zambia to establish a Credit Reference Bureau in the industry to screen truant and problematic customers in order to address the current incidence of loan repayment delinquency.
x
Conversion of Statutory Reserves The conversion of statutory reserves in foreign currency deposits to US dollars released kwacha into the financial system. This was aimed at increasing loanable funds to commercial banks; thereby contributing to lowering interest rates through demand and supply forces. &RPPHUFLDO%DQN¶V,QWHUHVW5DWHV In line with movements in government bond rates, commercial banks interest UDWHVWUHQGHGGRZQZDUGVDVZHOO7KLVZDVUHIOHFWHGLQWKHFRPPHUFLDOEDQNV¶ weighted average lending base rate, which declined by 4.8 percentage points to 37.7 percent in November 2003 from 42.5 percent in December 2002. In 2005, the weighted average base rates hovered around 28 percent. This followed the Bank of Zambia reduction of statutory reserve ratio to contribute to lowering of interest rates. Commercial Banks should play their part by reducing lending rates to affordable levels especially considering that their
19
cost of doing business were far less than the lending rates and that the Bank of Zambia reduced statutory reserve ratios. Role of the Public Given the history of credit delinquency by the public, the public could play an important role in ensuring that they developed a culture of paying back loans. This would help reduce the risks and therefore premiums charged by commercial banks on loans to the private sector. The BAZ (2005) CRB ±USA study tour report concluded that the benefits that would accrue to Zambia due to credit information sharing include: R Increased access to finance for the private sector since lenders would find it less risky to lend to them because they will be able to distinguish between bad payers and good payers. R Lowering of interest rates due to reduction in risk premium R It leads to sustainable debt levels R Encourages borrowers to pay on time R Reduces the cost of investigation and credit decisions R Lowers credit risk R Leads to increased lending R Increases profits to lenders due to increased efficiency in lending R Increased demand for goods and services thereby fuelling growth of the economy, job creation due to an active lending environment. 2.4
Empirical Work Done on Foreign Credit Markets Literature on foreign credit markets began by noting the trends in the use of credit reporting registers in the world as depicted in Figure 2.2 below. Countries that have embraced credit information sharing quickly have increased access to finance in their credit markets. Japelli and Pagano (1999) provided evidence on the evolution of Credit Bureaus.
20
Figure 2.2 Credit Reporting around the World 80%
Proportion of Countries
70% 60% 50% 40% 30% 20% 10% 0% Before 1950
1950-59
1960-69
1970-79
Public Credit Registers
1980-89 1990-1999 2000-03
Private Credit Registers
Source: Pagano and Japelli (2003)
The researchers provided one of the very few attempts to test the predictions of theoretical models regarding the impact of information sharing on lending activity. They compiled a unique data set describing the nature and extent of information sharing arrangements in 43 countries. Consistent with theoretical models, they found that the breadth and depth of credit markets was significantly related to information sharing. Specifically, total bank lending to the private sector was larger in countries that had a greater degree of information sharing, even after controlling for country size, growth rates and variables capturing the legal environment and protection of creditor rights. The authors also found that greater information sharing reduced defaults. 2.4.1 Empirical Models on the Role of Credit Information Systems According to Pagano and Japelli (2005), exchanging information about ERUURZHUV FDQ KDYH IRXU HIIHFWV )LUVW FUHGLW EXUHDXV LPSURYH EDQNV¶ NQRZOHGJHRIDSSOLFDQWV¶FKDUDFWHULVWLFVDQGSHUPLWPRUHDFFXUDWHSUHGLFWLRQ 21
of repayment probability. This allows lenders to target and price their loans better, easing adverse selection problems. 6HFRQG FUHGLW EXUHDXV UHGXFH WKH ³LQIRUPDWLRQDO UHQWV´ WKDW EDQNV FRXOG otherwise extract from their customers. When a bank has superior knowledge about a borrower, it can charge him interest rates just slightly below those offered by an uninformed competitor and earn a rent from its information. Pooling information with other banks reduces this advantage and the implied rent, by forcing each lender to price loans more competitively. Lower interest UDWHVLQFUHDVHERUURZHUV¶QHWUHWXUQDQGDXJPHQWWKHLULQFHQWLYHWRSHUIRUP Third, credit bureaus work as a borrower discipline device; every borrower knows that if they default, their reputation with all other potential lenders is ruined, cutting them off from credit or making it more expensive. This PHFKDQLVP DOVR KHLJKWHQV ERUURZHUV¶ LQFHQWLYH WR UHSD\ UHGXFLQJ PRUDO hazard. Fourth, and finally, borrowers have the incentive to become over-indebted if they draw credit simultaneously from many banks without any of them realizing. Credit bureaus and public credit registers disclose to lenders the overall indebtedness of borrowers, and thereby eliminate this incentive, and the implied inefficiency in the provision of credit. The Problem of Adverse Selection Lending markets almost always display some degree of information asymmetry between borrowers and lenders. Borrowers typically have more accurate information than lenders about their willingness and ability to repay a loan. Since the expected gains from the loan contract are a function of both the pricing and the probability of repayment, lenders invest resources to try DQG GHWHUPLQH D ERUURZHU¶V OLNHOLKRRG RI UHSD\PHQW )RU WKH VDPH UHDVon, borrowers may also have incentive to signal their true riskiness (if it is low) or disguise it (if it is high). The actions of borrowers and lenders as they try to 22
reduce the information asymmetry has significant consequences for the operation of credit markets and give rise to a variety of institutions intended to minimize the associated costs. A large theoretical and empirical literature about the consequences of such information asymmetry has developed over the past 25 years. For purposes of this research, Stiglitz and Weiss (1981) provide the conceptual launching point for explaining the evolution of credit bureaus. This research focuses on lending markets without information sharing and theoretically describes the adverse selection problem that reduces the gains to both borrowers and lenders. Simply put, ZKHQOHQGHUVFDQ¶WGLVWLQJXLVKJRRGERUURZHUVIURP bad borrowers all borrowers are charged an average interest rate that reflects their pooled experience. But, this rate is higher than good borrowers warrant and causes some good borrowers to drop out of the market, thereby shrinking the customer base and further raising the average rate charged to remaining borrowers. The adverse selection argument embodies the intuition about why better information makes lending markets work more efficiently. Better information allows lenders to more accurately measure borrower risk and set loan terms accordingly. Higher-risk borrowers can be accommodated, but lenders recognize who they are and can set appropriately higher interest rates. Consequently, fewer high-risk borrowers are rationed out of the market. Lower-risk borrowers are offered more attractive rates, which further stimulates the quantity of loans demanded. For both reasons, the volume of lending H[SDQGV UHODWLYH WR WKH ³OLPLWHG-LQIRUPDWLRQ´ VFHQDULR ZLWK DYHUDJH pricing. In the pure adverse selection model developed by Pagano and Jappelli (1993), information sharing improves the pool of borrowers, decreases defaults and reduces the average interest rate. In the model, each bank has private information about the credit worthiness of credit applicants who reside 23
in their market area but has no information about credit applicants who have recently moved into its market area. The latter therefore face adverse selection problems. However, they have borrowed in the past from the bank of their area of origin and therefore are known to that bank. If banks exchange their private LQIRUPDWLRQ DERXW WKHLU FOLHQWV¶ TXDOLW\ WKH\ FDQ LGHQWLI\ ZKLFK RI WKH FUedit seekers who have newly moved into their market area are creditworthy, and lend to them as safely as they do with their long-standing clients. As a result, the default rate decreases. Banking competition strengthens the positive effect of information sharing on lending: when credit markets are contestable, information sharing reduces informational rents and increases banking competition, which in turn leads to greater lending. 7KLV PRGHO DOVR GHOLYHUV SUHGLFWLRQV DERXW OHQGHUV¶ LQFHQWLYHV WR FUHDWH D credit bureau. Lenders have a greater incentive to share information when the mobility of credit seekers is high and the potential demand for loans is large. Technical innovations that reduce the cost of filing, organizing and distributing information shouOG IRVWHU FUHGLW EXUHDXV¶ DFWLYLW\ %DQNLQJ FRPSHWLWLRQ LQ contrast, may inhibit the appearance of credit bureaus: with free entry, a bank that supplies information about its customers to a credit bureau is in effect helping other lenders to compete more aggressively. Pagano and Jappelli (1993) bring international and historical evidence to bear on these predictions. 5HGXFLQJ%RUURZHUV¶+ROGXS The exchange of information between banks reduces the informational rents that banks can extract from their clients within lending relationships. Padilla and Pagano (1997) make this point in the context of a two-period model where banks are endowed with private information about their borrowers. This informational advantage confers to banks some market power over their
24
customers, and thereby generates a hold-up problem: anticipating that banks will charge predatory rates in the future, borrowers exert low effort to perform. This leads to high default and interest rates, and possibly to collapse of the credit market. ,I WKH\ FRPPLW WR H[FKDQJH LQIRUPDWLRQ DERXW ERUURZHUV¶ W\SHV KRZHYHU banks restrain their own future ability to extract informational rents. This implies that a larger portion of the total surplus generated by the financed projects will be left to entrepreneurs. As a result, these will have a greater incentive to invest effort in their project to ensure their success. This reduces the probability of default on their loans. The interest rate charged by banks will be reduced in step with the default rate, and total lending will increase relative to the regime without information sharing. Disciplinary Effect of Default Disclosure An effect on incentives exists even when there is no hold-up problem. This effect is present when banks, instead of exchanging information about ERUURZHUV¶ TXDOLW\ FRPPXQLFDWH WR HDFK RWKHU GDWD DERXW SDVW GHIDXOWV Padilla and Pagano (1999) show that this creates a disciplinary effect. When banks share default information, default becomes a signal of bad quality for outside banks and carries the penalty of higher interest rates. To avoid this penalty, entrepreneurs exert more effort, leading to lower default and interest rates and to more lending. The disciplinary effect arises only from the exchange of default information. 7R WKH H[WHQW WKDW EDQNV DOVR VKDUH GDWD RQ ERUURZHUV¶ FKDUDFWHULVWLFV WKH\ actually reduce the disciplinary effect of information sharing: a high-quality borrower will not be concerned about his default being reported to outside banks if these are also told that he is a high-quality client. But, as discussed DERYH H[FKDQJLQJ LQIRUPDWLRQ DERXW ERUURZHUV¶ FKDUDFWHULVWLFV PD\ UHGXFH adverse selection or temper hold-up problems in credit markets, and thereby reduce default rates. 25
Eliminating Incentives to Over-Borrow from Multiple Lenders The previous three effects arise even if households and firms apply for credit ZLWK RQH OHQGHUDW D WLPH ³([FOXVLYH OHQGLQJ´LVD PDLQWDLQHG DVVXPSWLRQ LQ all the models mentioned so far. But in practice credit seekers may apply simultaneously for credit from several lenders and often manage to get loans and lines of credit from more than one. As shown by Ongena and Smith (1998), multiple bank relationships are commonplace in most countries, especially for large companies. Their number is relatively low in some countries (on average, less than three for firms in the UK, Norway and Sweden, and between three and four in Ireland, Hungary, Poland, the Netherlands, Switzerland and Finland) but very large in others (ten or more in Italy, France, Spain, Portugal, Belgium). Maintaining multiple bank relationships has several advantages from the standpoint of a borrower. First, it may help reduce the cost of credit, by forcing the various providers of credit to compete. Second, each of the lenders will have to bear a smaller amount of credit risk, and therefore will require a lower risk premium in the interest it charges. Third, being able to obtain credit from multiple lenders insures the borrower against the risk that any of the lenders may suddenly call back his loan or withdraw his line of credit, for instance because of a liquidity shock, as argued by Detragiache, Garella and Guiso (1997). Multiple bank relationships are also costly, however. They discourage each bank from monitoring the borrower closely (lest other lenders free-ride on its monitoring effort) as argued by Petersen and Rajan (1994). The costs of multiple lending relationships escalate if each potential lender has no clear information about how much credit the borrower has already obtained or will EH DEOH WR REWDLQ IURP RWKHU OHQGHUV $ ERUURZHU¶V GHIDXOW ULVN IURP WKH viewpoint of a given lender, depends on the overall indebtedness of the borrower when his obligation towards that lender will mature. If this 26
information is unavailable to the lender, however, the borrower has the incentive to over-borrow. To understand why, consider a borrower seeking credit from two banks, which do not tell each other how much he borrows from each. Assume that his probability of default is an increasing function of his indebtedness. When he applies for a loan from each of the two banks, each additional dollar he borrows reduces the probability of repayment of the capital and interest to the other bank, which cannot modify the terms of its loan contract in response to such behavior. Thus, his expected interest burden per dollar of total debt is a decreasing function of his total debt and he has the incentive to over-borrow. Anticipating this moral hazard, lenders will ration the amount of credit supplied and/or require a higher interest rate, or even deny all credit unless assisted by collateral or by covenants restricting total debt. Notice that a OHQGHU LV QRW RQO\ WKUHDWHQHG E\ WKH ERUURZHU¶V SULRU GHEW FRPPLWPHQWV EXW also by those that he may contract in the future, as shown by Bizer and DeMarzo (1992). The available evidence (Ongena and Smith, 1998, ) indeed suggests that the number of bank relationships has a negative impact on the availability of credit, whereas is ambiguous regarding its impact on interest rates. This particular form of moral hazard is eliminated, instead, if lenders agree to reveal to each other the magnitude of the loans and lines of credit that they have extended to each client. This suggests that when lenders share information about outstanding loans they can be expected to increase the supply of lending and/or improve the interest rates offered to credit seekers. Borrowers will therefore prefer these lenders to those that do not agree to communicate to each other such information. This explains why banks may want to pool data about the amount lent to each of their client. Bennardo and Pagano (1999) develop this argument formally in work in progress. A side effect of this type of information sharing is to reduce the implied cost of
27
entertaining a large number of credit relationships, and therefore to make them more attractive to borrowers, on balance. This may partly explain why firms have a relatively large number of credit relationships in Italy, France, Spain, Portugal, and Belgium, where public credit registers provide very accurate information about the overall indebtedness of firms. Banks operating in those countries may feel confident to provide credit to companies that are also served by many other lenders, since they can easily keep a tab on their overall indebtedness. 2.4.2
Why Would Lenders Share Information? The next step in explaining the evolution of credit bureaus was provided by Pagano and Japelli (1993). Their theoretical development explains the factors encouraging voluntary information sharing among lenders, as well as those conditions that deter voluntary information sharing. Where Stiglitz and Weiss showed how adverse selection could impair markets, Pagano and Japelli show how information sharing can reduce the problem and increase the volume of lending. Their theoretical model generates the following implications.
Incentives for lenders to share information about borrowers
(about payment experience, current obligations and exposure) are positively related to the mobility and heterogeneity of borrowers, to the size of the credit market, and to advances in information technology. Working in the opposite direction (discouraging the sharing of information about borrowers) is the fear of competition from additional entrants. The intuition is straightforward. Mobility and heterogeneity of borrowers reduce the feasibility of a lender relying solely on its own experience to guide its portfolio management. Thus, these factors increase the demand for LQIRUPDWLRQDERXWDERUURZHU¶VH[SHULHQFHZLWKRWKHUOHQGHUV7KHQHHGIRU LQIRUPDWLRQ WR VXSSOHPHQW D OHQGHU¶V RZQ H[SHULHQFH JURZV ZLWK WKH VL]H RI market. In addition, any declines in the cost of sharing information (perhaps through technological improvements) boost the net gains from sharing.
28
The need for information sharing among lenders having been established, the next conceptual step was to rationalize the existence of a credit bureau. Padilla and Pagano (1997) develop a theoretical rationale for credit bureaus as an integral third-party participant in credit markets. They explain the conditions under which lenders agree to share information about borrowers via a third party which can penalize those institutions who do not report accurately. As noted in Pagano and Japelli (1993), information sharing has direct benefits to lenders by reducing the impact of adverse selection (average interest rates tend to drive out low-risk borrowers leaving only the high-risk borrowers remaining), and moral hazard (borrower has incentive to default unless there are consequences for future applications for credit). However, information sharing stimulates competition for good borrowers over time, which erodes the informational rents enjoyed by incumbent lenders (who have already identified and service the good customers, the very ones which competitors would like to identify and recruit). In this paper the authors discussed an additional problem that can arise out of the informational asymmetry between borrowers and lenders. As noted above, as a lender establishes relationships with customers it becomes able to distinguish good borrowers from bad borrowers. At that point, the lender has an incentive to either hold back information about the good borrowers or purposely spread false information about them in order to discourage competitors from making overtures. Borrowers know this, and so have less incentive to perform well under the loan contract, because such efforts will not be rewarded with lower interest rates in the future (and may be exploited with higher rates and/or spread of misinformation). This tendency to under perform is reversed if borrowers perceive some gain to signalling they are good ERUURZHUV &RQVHTXHQWO\ D OHQGHU¶V FRPPLtment to share accurate 29
information with other lenders, coupled with an enforcement mechanism that ensures that accuracy, can actually benefit all parties. The third-party credit bureau fills the role of both clearinghouse and enforcer. As a consequence, Padilla and Pagano show that if they share information, interest rates and default rates are lower, on average, and interest rates decrease over the course of the relationship with each client and his bank. In addition, the volume of lending may increase as information sharing expands the customer base. 2.4.3 Predictive Power of Bureau-Based Risk Models The conceptual case that information sharing leads to more efficient lending markets hinges on the assertion that data about past payment behavior is useful for predicting future performance. Of course, the entire credit scoring industry stands as testimony to this premise. However, among the few published attempts to document the gains from utilizing increasingly detailed credit history data are two papers, Chandler and Parker (1989), and Chandler and Johnson (1992). In the earlier paper, the authors document the ability of U.S. credit bureau data to outperform application data in predicting risk. Their analysis was based on comparing credit bureau against application data in scoring three categories of credit card applications: bank credit card, retail store card and non-revolving charge card. In their study, application information included variables such as the DSSOLFDQW¶V DJH WLPH DW FXUUHQW/previous residence, time at current/previous job, housing status, occupation group, income, number of dependents, presence of telephone at residence, banking relationship, debt ratio, and credit references. Variable values were coded straight from the credit card application, without independent verification. Using models built to score bank card applicants, the authors found that the application data without the credit bureau data yielded the lowest predictive power and did not fare well when compared with predictions based on any 30
level of credit bureau data. The predictive power increased substantially at higher levels of credit bureau detail, with the most detailed model exhibiting predictive power 52% greater than the simple credit bureau treatment. In fact, a model incorporating the detailed credit bureau data plus application data actually performed worse than a model based on the detailed credit bureau data alone. Perhaps this is not surprising given that most application data on bank card products is not verified because of the cost and consequent delay in the accept/reject decision.
The bottom line: the more
LQIRUPDWLRQ DYDLODEOH DERXW D ERUURZHU¶V FXUUHQW DQG SDVW FUHGLW SURILOH WKH JUHDWHUZDVWKHDELOLW\RIWKHVFRULQJPRGHOWRVHSDUDWHµgRRGVIURPEDGV¶. 2.4.4
Macroeconomic Evidence The predictions about the effects of information sharing are tested in Jappelli and Pagano (2002 and 1999) on cross-country data. The question was how well do the implications of these theoretical models explain the evolution of credit bureaus and the lending markets they support? Japelli and Pagano (1999) provide one of the very few attempts to test the predictions of the theoretical models regarding the impact of information sharing on lending activity. The authors compiled a unique dataset describing the nature and extent of information sharing arrangements in 43 countries. Consistent with the theoretical models, the authors found that the breadth and depth of credit markets was significantly related to information sharing. Specifically, total bank lending to the private sector is larger in countries that have a greater degree of information sharing, even after controlling for country size, growth rates and variables capturing the legal environment and protection of creditor rights. The authors also found that greater information sharing reduced defaults, though the relationship was somewhat weaker than the link to additional lending.
31
2.4.5 Microeconomic Evidence Chandler and Parker, (1992) and Barron and Staten (2003) analysed the effectiveness of credit bureaus, and generally found that credit reports were an important tool to assess consumer credit risk. This was confirmed by Kalberg and Udell (2003), who documented that trade credit history in Dun & %UDGVWUHHW¶V UHSRUWV LPSURYHG GHIDXOW SUHGLFWLRQV UHODWLYH WR ILQDQFLDO statements alone. Also Cowan and De Gregorio (2003) found that in Chile positive and negative information in credit reports contributes to predicting defaults. This improved assessment of credit risk appears to translate into higher lending. Furthermore, Galindo and Miller (2001) found a positive relation between access to finance (debt) and an index of information sharing in the Worldscope database, using the firm-level sensitivity of investment to cash flow as a proxy of credit constraints. They observed that well-performing credit reporting systems reduce the sensitivity of investment to cash flows. Love and Mylenko (2003) combined firm-level data from the World Bank Business Environment Survey with aggregate data on private and public registers collected in Miller (2003) and found that private credit bureaus are associated with lower perceived financing constraints and a higher share of bank financing. However, the existence of public credit registers does not have a significant effect on financing constraints. In addition, the individual country studies and World Bank projects brim with interesting evidence on the effect of information sharing on specific credit markets, highlightLQJ SDUWLFXODUO\ LWV ³GLVFLSOLQDU\ UROH´ 3LQKHLUR DQG &DEUDO (2001) reported that in Brazil the whole postdated check market (whose size is of the same order of magnitude as the stock of household credit) operates without collateral, without personal guarantees, and without legal sanctions of any type. Its only foundation is its information-VKDULQJ PHFKDQLVP D ³EODFN OLVW´ RI SHRSOH LVVXLQJ FKHFNV ZLWKRXW IXQGV 7KLV PHFKDQLVP DORQH DOVR 32
explains why the interest rate charged by factoring companies that operate in this market is much lower than that charged by credit card companies. Similar evidence is reported for Chile, where department stores seeking to collect an unpaid loan send the relevant information both to a collection agency and to the main Chilean credit bureau. Apparently, notifying the bureau is a very effective way of securing immediate repayment, since delinquent customers see their credit dry up with all the stores that they patronize. Moreover, the degree and sophistication of information sharing arrangements appear to be synchronized with those of the financial system as a whole. For instance, Costa Rica, which has one of the most sophisticated credit markets in the region, also has an impressive and keenly competing set of private credit bureaus covering the majority of the population of the country, with different bureaus specializing in different services. The development of information sharing mechanisms appears in turn to prompt lenders to move towards more refined screening and monitoring practices. This is witnessed by the central role that information-sharing systems have taken in borrower selection in Peru, especially after the development of a public credit register in that country. As explained by Trivelli, Alvarado and Galarza (2001), this has encouraged lenders to shift away from exclusive reliance on collateral towards information-based lending. In a study of loan losses of US banks, McGoven (1993) argued that µFKDUDFWHU¶ KDV KLVWRULFDOO\ EHHQ D SDUDPRXQW IDFWRU RI FUHGLW DQd a major determinant in the decision to lend money. Banks have suffered loan losses through relaxed lending standards, unguaranteed credits, the influence of the VFXOWXUHDQGWKHERUURZHUV¶SHUFHSWLRQV,WZDVVXJJHVWHGWKDWEDQNHUV should make a fairly accurate personality-morale profile assessment of prospective and current borrowers and guarantors. Besides considering personal interaction, the banker should: 33
x Try to draw some conclusions about staff morale and loyalty, x 6WXG\WKHSHUVRQ¶VSHUVRQDO x Do
credit report,
trade-credit reference checking,
x Check references from present and former bankers, and x Determine how the borrower handles stress. ,Q DGGLWLRQ EDQNV FDQ PLQLPLVH ULVNV E\ VHFXULQJ WKH ERUURZHU¶V JXDUDQWHH using Government guaranteed loan programs, and requiring conservative loan-to-value ratios. Fuentes and Maquieira (1998) undertook an indepth analysis of loan losses due to the composition of lending by type of contract, volume of lending, cost of credit and default rates in the Chilean credit market. Their empirical analysis examined different variables, which may affect loan repayment: x Limitations on the access to credit; x Macroeconomic stability; x Collection technology; x Bankruptcy code; x Information sharing; x The judicial system; x pre-screening techniques; and x Major changes in financial market regulation. They concluded that a satisfactory performance of the Chilean credit market, in terms of loan repayments hinges on a good information sharing system, an advanced collection technology, macroeconomic performance and major changes in the financial market regulation. In another study of Chile, Fuentes and Maquieira (2003) analysed the effect of legal reforms and institutional changes on credit market development and the low level of unpaid debt in the Chilean banking sector. Using time series 34
data on yearly basis (1960-1997), they concluded that both information sharing and deep financial market liberalisation were positively related to the credit market development. They also reported less dependence of unpaid loans with respect to the business cycle compared to interest rate of the Chilean economy.
2.4.6 3ROLF\DQG2SHUDWLRQDO,VVXHVLQ&UHDWLRQRI&5%¶V 2Q WKH SROLF\ DQG RSHUDWLRQDO LVVXHV LQ WKH FUHDWLRQ RI &5%¶V 3DJDQR and Japelli (2005) undertook
to answer the four key questions: under which
circumstances should public policy create a credit reporting system, by mandating banks to disclose their private information? And if so, which information should be pooled and which should be kept confidential? For how long should information remain available in a credit reporting system? These are just some of the many policy issues that arise in the creation, design and regulation of information exchange in credit markets. This section takes up the most salient issues, building on the above discussion of the effects of information sharing on the performance of credit markets. Relationship between Private and Public Systems Information sharing arrangements are often created spontaneously by groups of lenders or individual entrepreneurs, in the form of credit bureaus or of rating agencies. The design of a public credit registry (PCR) cannot disregard how much information sharing the private sector is already exchanging spontaneously. Clearly, the case for the introduction of a PCR is comparatively stronger in countries where private information sharing arrangements among lenders do not exist, or are primitive and limited in coverage and scope. In fact, empirically the probability that a PCR is introduced is lower in countries with pre-existing private information-sharing arrangements. Private and public arrangements are substitutes in this area. %\ WKH VDPH WRNHQ KRZHYHU SXEOLF DUUDQJHPHQWV FDQ ³FURZG RXW´ SULYDWH ones. 35
The introduction of a cost-effective PCR can put existing credit bureaus out of business or discourage the creation of new ones. In this respect, the crucial parameter in the design of a PCR is the minimum reporting threshold, since it effectively delimits the market segment left to the operation of private credit bureaus. In countries where an effective PCR operates, credit bureaus tend to specialize in loans to households and to small businesses, whose size is typically below the reporting threshold of the PCR. The higher this threshold, the larger the scope for private initiative in the industry (Pagano and Japelli, 2005) The
substitutability
between
public
and
private
information
sharing
arrangements, however, should not be exaggerated. There are reasons why the two sources of information may be complements. For instance, credit bureaus may provide a greater degree of detail than PCRs may merge other types of information with banking records or may provide credit-scoring services to lenders. Therefore, a lender may obtain a clearer assessment of a FUHGLW DSSOLFDQW¶V VROYHQF\ E\ DFFHVVLQJ ERWK WKH UHOHYDQW 3&5 DQG D FUHGLW bureau than by confining himself to only one of these two sources of information (Pagano and Japelli, 2005) Dosage of Negative and Positive Information The type of data reported is another key element in the design of credit information system. Pagano and Japelli (2005) identified three options: x 7KH VLPSOHVW DQG PRVW LQH[SHQVLYH V\VWHPV DUH ³EODFN OLVWV´ ZKLFK contain information only on defaulters. These are most effective in correcting moral hazard problems in the credit market, owing to their disciplinary effect via reputational mechanisms. x Intermediate systems also include reporting of loan amounts, so that lenders may form a more precise estimate of the total indebtedness of credit seekers. Such information helps to correct the moral hazard 36
problems that may arise if loan contracts are non-exclusive as explained earlier. x The most sophisticated systems also include other forms of positive LQIRUPDWLRQ DERXW ERUURZHUV¶ FKDUDFWHULVWLFV VXFK DV GHPRJUDSKLF information for households and accounting information for firms. However, LQ WKLV DUHD ³PRUH´ LV QRW DOZD\V ³EHWWHU´ $ V\VWHP WKDW SURYLGHV PXFK LQIRUPDWLRQ DERXW ERUURZHUV¶ FKDUDFWHULVWLFV may lead banks to identify high-quality borrowers more easily, but by the same token such borrowers will be less worried to be reported as defaulters, trusting that their reputation will not be stained by such an event. As a result, they may exert less effort to avoid default. In summary, information collected by the Credit agencies may include: Personal details, Credit rating ± IRU H[DPSOH 6WDQGDUG DQG 6SRRU¶V KDYH D rating of D to AAA (with AAA being the best score and D the worst See Appendix 2.1 for definitions of ratings), Financial statements, Clubs and Associations belonged to, current credit obligations and repayment records, defaults on any accounts and a record of any cheques referred, civil judgments and any legal cases, etc. Positive or white information helps borrowers obtain credit from banks and other credit grantors. Negative or black information indicates that the borrower could be a credit risk. This information could form the basis for denying credit. Negative information includes unpaid bills, defaulted loans, repossessions, unpaid taxes, judgments, unpaid cheques, bankruptcies etc. Memory of the System 7KH QXPEHU RI \HDUV D FUHGLW LQIRUPDWLRQ V\VWHP ³UHPHPEHUV´ GHIDXOW RU arrears by a given borrower is another important parameter in the design of a credit information system. More specifically, in setting the memory of the system, one has to ask two distinct but related questions. First, how long are default records kept? Second, are they removed after (late) repayment? Both 37
of these feDWXUHV LPSLQJH RQ ZKDW ZH ZLOO FDOO WKH ³IRUJLYHQHVV´ RU forgetfulness) of the system. At one extreme, a system with infinite memory, where borrowers have no FKDQFH WR H[LW IURP WKH ³EODFN OLVW´ HYHQ DIWHU ODWH UHSD\PHQW PD\ FUHDWH D high incentive to repay on time, but may ex ante deter the decision to take any debt. The risk of being eternally black listed in case of default may be so large as to deter from borrowing even individuals with relatively solid prospects. Ex post, a black list with extremely long memory may prevent defaulted debtors from ever making a comeback. Upon default, entrepreneurs may never have a chance to get new loans and start a new business, and therefore to repay their past debts. Furthermore, even if a borrower has the money to repay a defaulted loan, he may have little incentive to do so because in any event his reputation is permanently marred. In this sense, a black list with very long memory can contribute to the well-known problem of ³GHEW RYHUKDQJ´E\ ZKLFK GHIDXOWHG GHEW becomes a permanent obstacle to the resumption of subsequent economic activity. At the other extreme, a system where records are kept for a very short time and immediately erased upon late repayment would exert very little discipline on borrowers and correspondingly provide very little information on their track UHFRUG WR OHQGHUV7KH GHVLUDEOH GHJUHH RI PHPRU\ DQG ³IRUJLYHQHVV´ RI WKH system lies between these two extremes. The system should trade off the need to discipline borrowers and the need to giYH WKHP D ³VHFRQG FKDQFH´ The optimal degree of forgiveness depends on many features of reality, including for example the persistence of default-inducing shocks, and generally differs from country to country. Where creditor rights are less well protected, for instance because of poor judicial enforcement, the need to discipline borrowers may be more pressing than elsewhere, and therefore one may want to make the memory of the system longer and less forgiving.
38
A particularly interesting memory design is found in the Belgian Central Office for Credit to Private Individuals, a PCR that records only default information concerning household debt.
Borrowers who redeem their debt disappear
more quickly from the register than borrowers for whom a repayment commitment continues to exist. If arrears are repaid then the information is automatically removed after one year; if the debt is repaid after default, it is removed only after 2 years. Irrespective of the type and status of the obligation, the database does not keep any record for more than 10 years. So ³SXQLVKPHQW´ LV VWULFWHU IRU PRUH VHULRXV PLVFRQGXFW GHIDXOWV DUH SXQLVKHG more than arrears), but eventually there is forgiveness for everybody. Apart from its role in the design of a PCR, this parameter is also a public policy variable, insofar as policy-makers may limit the memory of private credit bureaus by regulation. For instance, Danish credit bureaus are entitled to register and distribute at most 5 years of data that is relevant to assess the financial situation of businesses or individuals; the 1970 U.S. Fair Credit Reporting Act, as amended in 1996, prohibits dissemination of adverse information (such as bankruptcy) after more than 7 years. Privacy Protection Credit information provision finds an obvious limit in the set of legal provisions designed to protect confidential information, or individual privacy. Such provisions differ widely both within Europe and between the U.S. and European countries and these differences appear to have had profound effects on the development of credit information systems (see Jappelli and 3DJDQR )RU LQVWDQFH )UDQFH¶V VWULFW SULYDF\ SURWHFWLRQ ODZV KDYH prevented the development of private credit bureaus in that country. The degree of privacy protection accorded to prospective borrowers has historically affected the development of credit bureaus. The activities of credit bureaus are regulated almost everywhere so as to prevent violation of privacy and civil liberties. Privacy laws effect a wide range of consumer guarantees, 39
such as limits on access to files by potential users, bans on white information (e.g., in Finland and Australia), compulsory elimination of individual files after a set time (e.g., 7 years in the United States, 5 in Australia), bans on gathering certain kinds of information (race, religion, political views, etc.) and WKHULJKWWRDFFHVVFKHFNDQGFRUUHFWRQH¶VRZQILOH As far as access limits are concerned, there appear to be three levels of privacy protection. There are low-protection countries, such as Argentina, ZKHUH DQ\RQH FDQ DFFHVV DOO GHEWRUV¶ GDWD UHJDUGOHVV RI WKH SXUSRVH RI investigation. In medium-protection countries as the United States, data can EH DFFHVVHG RQO\ IRU DQ ³DGPLVVLEOH SXUSRVH´ HVVHQWLDOO\ WKH JUDQWLQJ RI credit. A higher level of privacy protection may be embodied in the further UHTXLUHPHQWRIWKHERUURZHU¶VH[SOLFLWFRQVHQWWRDFFHVVKLVILOH7KLVSULQFLSOH is enshrined in the legislation of several European countries and in the Directive 95/46 of the European 3DUOLDPHQW RQ ³WKH SURWHFWLRQ RI LQGLYLGXDOV with regard to the processing of personal data and on the free movement of VXFK GDWD´ ,Q VRPH FRXQWULHV VXFK DV )UDQFH ,VUDHO DQG 7KDLODQG safeguards for consumer privacy are so strong that regulation has impeded the emergence of private credit bureaus. However, one should not necessarily take a negative view of the effect of privacy laws on credit information systems. As already pointed in the discussion of the desirable memory of such systems, divulging certain types RI LQIRUPDWLRQ PD\ OHDG SHRSOH WR EHFRPH ³WRR FDXWLRXV´ WKDW LV LW PD\ reduce risk taking and entrepreneurship below the socially desirable level. Therefore, a moderate concern for privacy may also indirectly serve economic efficiency. In addition, there is one privacy-protection rule that directly improves the accuracy of the data stored by credit information systems: entitling individuals with the right to inspect and correct mistaken information about them. Such feedback not only improves the quality of information, but also helps to 40
correct the negative bias in reporting that credit bureaus are often blamed for. Such bias is easily explained: when a negative credit report is mistakenly filed, the lender will generally deny credit and therefore is unlikely to ever find out about the mistaken information, while the opposite would happen if a positive report was filed for a bad credit risk. Therefore, credit bureaus prefer to err on the negative side. 2.4.7 Designing Information Sharing Systems in Developing Countries Some issues in the design of credit information systems are particularly relevant for developing countries, where these systems are often still being designed. First, in most developing countries, the role of informal lending is much larger than in developed economies. Since, typically, both credit bureaus and PCRs base their information on data reported by formal lenders, their utility is much reduced in these countries. This limitation of information sharing systems could be overcome by allowing informal lenders - such the non-governmental organizations (NGOs) that manage microcredit programs ± to access PCRs. For instance, Trivelli, Alvarado and Galarza (2001) report that one of the main limitations of the Peruvian PCR is its insufficient coverage of data about debts with informal and rural lenders, because the majority of such lenders have never had any relation with the formal system. A second issue is that PCRs are more important in countries where creditor rights receive relatively poor protection and the law is less effectively enforced in this sense, PCRs appear to act as a partial substitute for the lack of good judicial enforcement. Credit bureaus can of course play this role too. The disciplinary role of negative information can be particularly important in this respect. For instance, in Brazil information sharing mechanisms allow widespread reliance on post-dated checks. Pinheiro and Cabral (2001) report: ³(DV\ORZ-cost information on the person writing the check and the high cost WR WKH FRQVXPHU RI EHLQJ SODFHG RQ D µEODFN OLVW¶ IRU ZULWLQJ D FKHFN ZLWKRXW
41
funds have made post-dated checks the most widely used form of FRQVXPSWLRQILQDQFLQJ´ 3LQKHLURHWDOS Thirdly, in LDCs the availability of information provided by PCRs can HIIHFWLYHO\ LQGXFH FKDQJHV LQ EDQNV¶ OHQGLQJ SROLFLHV VKLIWLQJ IURP D collateral-based lending policy to an information-based one. In many developing economies, it is often complained that formal lenders request their loans to be assisted by collateral whose value greatly exceeds the loan and pay little attention to the prospective cash flows of the project they are financing. The availability of more readily usable information, together with knowledge of credit scoring techniques, may contribute to a shift in lending strategies. Finally, in developing countries, credit information systems should be designed so as to be accessible by relatively unsophisticated bank personnel, and avoid importing too sophisticated systems, which presuppose very detailed positive information or rely on complex scoring techniques. Most LDCs may usefully start with simple negative information systems, possibly complemented by data on loan exposure, and later proceed to enrich them with additional data on corporate accounts and management and personal information. 2.5
Summary The breadth and depth of the literature reviewed shows that information sharing among lenders attenuates adverse selection problems and moral hazard, and can therefore increase lending and reduce default rates. Bank lending is higher and proxies for default rates are lower in countries where lenders share information, regardless of the private or public nature of the information sharing mechanism. Therefore, Information sharinJ DERXW ERUURZHUV¶ FKDUDFWHULVWLFV DQG WKHLU indebtedness can have important effects on credit markets activity. First, it 42
LPSURYHV WKH EDQNV¶ NQRZOHGJH RI DSSOLFDQWV¶ FKDUDFWHULVWLFV DQG SHUPLWV D more accurate prediction of their repayment probabilities. Second, it reduces the informational rents that banks could otherwise extract from their customers. Third, it can operate as a borrower discipline device. Finally, it HOLPLQDWHV ERUURZHUV¶ LQFHQWLYH WR EHFRPH RYHU-indebted by drawing credit simultaneously
from
many
banks
without
any
of
them
realizing.
Understanding the effects of information sharing also helps to shed light on some key issues in the design of a credit information system, such as the relationship between public and private mechanisms, the dosage between EODFNDQGZKLWHLQIRUPDWLRQVKDULQJDQGWKH³PHPRU\´RIWKHV\VWHP. Having reviewed literature on theories and empirical work done in different credit markets regarding credit information sharing, the next Chapter develops the theoretical and conceptual framework to arrive at research hypotheses.
43
Chapter Three Theoretical and Conceptual Framework
3.0
Introduction This Chapter seeks to embed the research problem in a theoretical and conceptual framework. The framework basically emanates from the theories of the credit process, that is, how credit is extended to business and individuals, the cost of money and information exchange among lenders in credit extension as discussed in the preceding chapter on literature review. Hypotheses are then developed and operationalised before concluding this chapter.
3.1
The Theoretical Case for Information Sharing in the Credit Process For lending institutions to meet borrower demand for increased credit, it is necessary that factual information on the paying habits of borrowers be available to lenders to assist them achieve precision in identifying deserving borrowers. This helps lenders keep credit risks to a minimum thus lowering write offs and interest rates. The fundamental objective of commercial and consumer lending is to make profit through increase in volume of business with minimal credit risk. In the case of banks, the twin goals are loan volume and loan quality. The credit SURFHVV UHOLHV RQ HDFK EDQN¶VV\VWHPV DQG FRQWUROV WKDW DOORZ PDQDJHPent and credit officers to evaluate risk and return trade-offs. The credit process includes three functions; business development and credit analysis, credit execution and administration, and credit review as depicted in Table 3.1 below.
44
Table 3.1
The Credit Process and the need for Information
The Credit Process Business Development and Credit Analysis
Credit Execution and Administration
Credit Review
Market Research Advertising,Public Relations Officer Call Programmes Obtain Formal loan request Obtain financial statements, borrowing resolution,credit reports Financial statement and cashflow analysis Evaluate Collateral Line Officer makes recommendation on accepting or rejecting loan request
Loan/credit committee reviews proposal/recommendations Accept/reject decision made, terms negoatiated Loan agreement prepared with collateral documentation Borrower signs agreement ,turns over collateral;receives loan proceeds Perfect security interest File materials in credit file Process loan repayments,obtain periodic financial statements call on borrower
Review loan documentation Monitor compliance with loan agreement: Positive and negative covenants Deliquencies in loan repayments Discuss nature of deliquency or other problems with borrower Institute corrective action: Modify credit terms Obtain additional capital, collateral ,guarantees Call loan
Source: Koch (1988)
In the credit process, money and information are the two basic inputs of lenders. The very survival of a bank in the market place depends on its ability to collect and process information efficiently in screening credit applicants and in monitoring their performance. At the screening stage, lenders need LQIRUPDWLRQ DERXW ERUURZHUV¶ FKDUDFWHULVWLFV LQFOXGLQJ WKH ULVNLQHVV RI WKHLU investment projects. Credit analysis is essentially default risk analysis in ZKLFK D OHQGHU DWWHPSWV WR HYDOXDWH D ERUURZHU¶V DELOLW\ DQG ZLOOLQJQHVV WR repay. Traditionally NH\ ULVN IDFWRUV DUH FODVVLILHG DV WKH ILYH &¶V RI FUHGLW Character, Capital, Capacity, Conditions and Collateral (Brigham et al, 1996) as summarised in Table 3.2 below.
45
Table 3.2 The Five C's of Credit Character
Capital
Capacity
Conditions
Collateral
Integrity Honesty Track record with other creditors & suppliers Past bad debts
Financial Strength Wealthy position Balance sheet How much of own resources in the project?
Financial Strength Cashflow analysis Ratio Analysis Customer base Management expertise Legal standing
The economy The industry Competition Supplier base Boom Slump
Alternative source of repayment Marketability Value location
Source: Adapted from Brigham et al (1996)
According to Brigham et al (1996), credit analysis considers the following five perspectives on prospective borrower and requested loan characteristics: Character Character refers to the likelihood that a credit customer will try to repay the debt. Every credit transaction implies a promise to pay. The question is: will the borrower make an honest effort to pay the debt, or is the borrower likely to try to get away with something? Experienced credit managers frequently insist that the moral character of a borrower is the most important single issue in credit evaluation. Thus, credit reports ought to be used to provide background information on past performances, both for businesses and individuals. No matter how character is determined, it is clear that credit history and reputation are extremely important in determining whether credit is granted ± both businesses and individuals should thus strive to maintain good credit reputations. &KDUDFWHUDOVRUHODWHVWRWKHERUURZHU¶VKRQHVW\$QDQDO\VWPXVWDVVHVVWKH ERUURZHU¶V LQWHJULW\ DQG VXEVHTXHQW LQWHQW WR UHSD\ ,I WKHUH DUH DQ\ VHULRXV doubts, the loan or credit request should be rejected. Questions asked should include: Does the borrower have a good borrowing track record? Are there previous bad debts outstanding? Has the borrower defaulted on a previous loan? Of course one cannot expect potential clients to reveal information that may work against them. Nevertheless, the banker must run a check on the 46
borrowers to ascertain that they have not been involved in leaving bad debts or defaulting with suppliers. For this sort of comprehensive credit check, a bank needs a credit reference bureau. This is currently non-existent in Zambia. Capital Capital is measured by the general financial condition of a borrower as indicated by an analysis of financial statements. Special emphasis is given to the risk ratios such as the debt /asset ratio, the current ratio and the timesinterest earned ratio. Can the firm or individual withstand any deterioration in its/his financial position? How much is the borrower going to put into the business from their own resources? Capacity Capacity is a measure of the ability of the credit customer to generate sufficient cash to service the debt. Therefore evaluation of this factor is based SULPDULO\RQWKHFDVKLQFRPHE\WKHERUURZHU,WLVJDXJHGE\WKHFXVWRPHU¶V past record and business methods and for some firms it might be VXSSOHPHQWHG E\ SK\VLFDO REVHUYDWLRQ RI WKHILUP¶VSODQWVDQG VWRUHV &UHGLW analysts obtain information from different sources to determine if the client is not highly geared. Credit reports are useful here as well. Capacity alsRLQYROYHVERWKWKHERUURZHU¶VOHJDOVWDQGLQJDQGPDQDJHPHQW¶V competence in maintaining and enhancing current operations so that the firm or individual can repay its debt obligations. A business must have identifiable cashflow or alternative sources of cash to repay debt. An individual must be able to generate income. Conditions Conditions refer to both general economic trends and to special developments in certain geographic regions or sectors of the economy that PLJKWDIIHFWWKHERUURZHU¶VDELOLW\WRPeet its obligations. Conditions also refer 47
to the economic environment or industry, specific supply, production and GLVWULEXWLRQ IDFWRUV LQIOXHQFLQJ D ILUP¶V RSHUDWLRQV 5HSD\PHQW VRXUFHV RI cash often vary with the business cycle or consumer demand. Collateral &ROODWHUDOLVWKHOHQGHU¶VVHFRQGDU\VRXUFHRIUHSD\PHQWRUVHFXULW\LQFDVHRI default. The assets the borrower offers as security in order to obtain credit represent the necessary collateral. Credit Review and Monitoring After credit is granted, lenders need information to control the actions taken by the borrower until the debt is completely repaid. The borrower may relax their efforts to avoid default or may hide the proceeds of their business to avoid repaying his debts or may go to other lenders for more credit without the knowledge of the first lender. 3.2
Lack of Comprehensive Credit Information and its Implications Unfortunately and in general the data needed to screen credit applications and to monitor borrowers are not freely available to banks. To the extent that a EDQN GRHV QRW KDYH VXFK LQIRUPDWLRQ LW IDFHV ³DGYHUVH VHOHFWLRQ´ RU ³PRUDO KD]DUG´ SUREOHPV LQ LWV OHQGLQJ DFWLYLW\ $GYHUVH VHOHFWLRQ DULVHV ZKHQ VRPH LQIRUPDWLRQ DERXW WKH ERUURZHU¶V FKDUDFWHULVWLFV UHPDLQ KLGGHQ WR WKH lender (hidden information), and can lead to an inefficient allocation of credit, for LQVWDQFHWRLWVUDWLRQLQJ0RUDOKD]DUGDULVHVLQVWHDGIURPWKHOHQGHU¶VLQDELOLW\ WR REVHUYH WKH ERUURZHU¶V DFWLRQV WKDW DIIHFW WKH SUREDELOLW\ RI UHSD\PHQW IRU instance, about the level of effort that the borrower exerts to manage his project and avoid default on his debt (hidden action). This creates the danger of opportunistic behavior - or moral hazard - by the borrower. This type of informational disadvantage by the bank leads to an inefficient allocation of credit and possibly to credit rationing. To a certain extent, these adverse selection and moral hazard problems can be mitigated if the 48
borrower can pledge collateral that the lender can seize in case of default, or if he has a considerable equity stake in the project or a good reputation to VDIHJXDUG LQ WKH EXVLQHVV FRPPXQLW\ ,Q DOO WKHVH FDVHV WKH ERUURZHU¶V incentives are well aligned with those of his creditors, and in some cases his intrinsic characteristics can be credibly communicated to lenders. But these mitigating factors are of no avail to many credit applicants, especially to young and small firms that typically lack sufficient collateral and equity capital and have a short track record. 3.3
The Cost of Money In a market-driven economy, funds are allocated through the price system. The interest rate is the price paid to borrow funds, whereas in the case of equity capital, investors expect to receive dividends and capital gains. In general, the quoted (or nominal) interest rate on borrowed funds, K, is composed of a real risk-free rate of interest, k*, plus several premiums that reflect inflation, the riskiness of the borrower and the specific credit facility as depicted in figure 2.1 below. Figure 2.1 Determinants of interest rates
Quoted interest rate =k= K* + IP + DRP + LP + MRP +CF where k = the quoted or nominal rate of interest on a given security. There are many different securities; hence many different quoted interest rates. K* = the real risk-IUHHUDWHRILQWHUHVW . LVSURQRXQFHG³N-star) IP = Inflation Premium DRP = Default Risk Premium LP = Liquidity or Marketability Premium MRP = Maturity Risk Premium CF= Cost of Funds Source: Brigham et al (1999)
49
The above components, whose sum makes up the quoted or nominal rate on a given credit facility are explained as follows: The Risk-Free Rate of Interest The real risk-free rate of interest, k*, is defined as the interest rate that would exist on a loan or security with a guaranteed payoff (termed a risk-less, or risk-free security) if inflation was expected to be zero during the investment period. An example is the GRZ Treasury bill rate that is short term. The riskfree rate changes over time depending on economic conditions, especially (i) on the rate of return corporations and other borrowers are willing to pay to ERUURZ IXQGV DQG LL RQ SHRSOH¶V WLPH SUHIHUHQFHV IRU FXUUHQW YHUVXs future consumption. The Nominal, or quoted, Risk-Free Rate of Interest, KRF. This is the real riskfree rate plus a premium for expected inflation KRF = K* + IP. If K* and IP are combined and let this sum equal to KRF then the equation in figure 2.1 above becomes: K = KRF + DRP + IP + MRP+ CF To be strictly correct, the risk free rate should mean the interest rate on a security that has absolutely no risk at all ± one that has no liquidity risk, and no risk of loss if inflation increases. No such security exists in the real world; hence, there is no observable truly risk-free rate. However, there is one security that is free of most risks- a GRZ Treasury bill, a short-term security issued by the Government of Zambia. Treasury bonds, longer-term government securities, are free of default and liquidity risks, but treasury bonds are exposed to some risk due to changes in the general level of interest rates.
50
Inflation Premium Inflation has a major impact on interest rates because it erodes the purchasing power of the currency and lowers the real rate of return on an investment. Investors and lenders are aware of this and so when they lend money they build in an inflation premium (IP) equal to the average inflation rate expected over the life of the security. Therefore if the real risk-free rate K* is 2 percent and if inflation is expected to be 18 percent during the next year then the quoted rate of interest on one year treasury bills would be 20 percent. So IP is a premium for expected inflation that investors and lenders add to the real risk-free rate of return. Default Risk Premium The risk that a borrower will default on a loan is the probability that a borrower will not pay the interest or the principal on a debt. This affects the market interest rate on a security. The greater the default risk, the higher the interest rate lenders charge (demand). Treasury securities have no or minimal default risk; thus they generally carry the lowest interest rates on taxable securities in Zambia. For corporate bondV WKH EHWWHU WKH ERQG¶V RYHUDOO FUHGLW UDWLQJ WKH lower its default risk, and consequently, the lower its interest rate. The difference between the quoted interest rate on a government bond and that on a corporate bond with similar maturity, liquidity and other features is the default risk premium (DRP). Liquidity Premium Liquidity generally is defined as the ability to convert an asset to cash on VKRUW QRWLFH DQG ³UHDVRQDEO\´ UHFRYHU WKH DPRXQW LQLWLDOO\ LQYHVWHG $VVHWV have varying degrees of liquidity, depending on the characteristics of the market in which they are traded. For instance, there exists a very active and easily accessible secondary market for financial assets like government bonds, treasury bills, stocks and bonds of large corporations, but the markets 51
for real estate are limited because they are geographically constrained. LP is therefore a premium added to the rate on a security if the security cannot be converted to cash on short notice and at close to the original cost. Maturity Risk Premium Prices of long-term bonds\securities decline sharply whenever interest rates rise and because interest rates can and do occasionally rise, all long-term financial assets, even government bonds, have an element of risk called interest rate risk (the risk of capital losses to which investors are exposed because of changing interest rates). Therefore, a MRP, which is higher the longer the years to maturity, must be included in the required interest rate. The effect of maturity risk premium is to raise interest rates on long-term bonds relative to those on short-term securities. This premium, like the others, is extremely difficult to measure but it seems to vary over time, rising when interest rates are more volatile and uncertain, and then falling when interest rates are more stable. Cost of Funds Cost of funds reflects the cost structure of the lending institution as well as targeted return on investment.
3.4
Conceptual Framework When evaluating loan or credit requests, lenders can make two types of errors .The first error is extending credit to a borrower who ultimately defaults. The second error is denying a credit request from a borrower who ultimately would repay the debt. In both cases a lender loses a customer and its profits are less and the level of economic activity is adversely affected. Many lenders focus on eliminating the first type of error, applying rigid credit evaluation criteria and rejecting applicants who do not fit the characteristics of the ideal borrower. In a credit market where credit information sharing is not 52
comprehensive among lenders, it is difficult for lenders to distinguish between good and bad payers. Lenders therefore resort to credit rationing against the sectors that are believed to be risky. In the Zambian situation, it is the private sector, both individuals and businesses, which are rationed out of credit. In addition, when lenders cannot distinguish good borrowers from bad borrowers, all borrowers are charged an average interest rate that reflects their pooled experience. But this rate is higher than good borrowers warrant and causes some good borrowers to drop out of the market, thereby shrinking the customer base and further raising the average rate charged to remaining borrowers. The perception by lenders of high credit risk in the private sector could explain the higher default risk premiums in Zambia thereby raising interest rates on credit to the private sector. On the basis of the foregoing theoretical and empirical considerations, the study arrived at the following hypotheses: 3.4.1
Hypotheses 1.
Default Rate and Trend Ho: Credit default in the banking sector was not increasing H1: Credit default in the banking sector was increasing
2.
Default rate vs. Private sector credit levels Ho: There was no relationship between Credit default and private sector credit H1: There was a relationship between Credit default and private sector credit
3.
Impact of credit on economic growth H0:There was no relationship between credit extension and economic activity H1: There was a relationship between credit and economic activity
53
The independent variables and dependent variables in the above hypotheses regarding credit extension and credit default were operationalised as follows: Private sector credit extension This was taken to mean total Banking and Non Bank Financial institutions sector lending to the private sector comprising both individuals and businesses. The proxy used was as follows: x
The total bank lending to the private sector was captured as Aggregate Loans, leases and advances (ALLA) as a percentage of Total Banking Assets (TBA). This was captured as time series data on a monthly basis from January 2000 to 2005 February inclusive. This resulted in 62 observations.
x
This variable carried the symbol ALLA/TBA in this study. In Pajelli and Pagano (2005), bank lending to the private sector was scaled by GDP. In Zambia, GDP was only captured on annual basis and if the study analysed credit extension on an annual basis, the number of observations would only be seven (7). This study avoided this limitation.
Credit default Credit default refers to any repayments of debt (principal and interest) that fell due but the borrower did not honour their obligation at all, or the borrower did not honour their obligation on time. The proxy used in the study was NonPerforming loans (NPL) in the consolidated balance sheet of the commercial banks and non-bank financial institutions prepared by the Bank of Zambia. A NPL was defined in the BFS Act of 1994 as a loan in respect of which any principal or interest is in arrears in excess of ninety (90) days. x
This credit default proxy was taken as a percentage of total banking sector lending to the private sector (ALLA). This was captured as time series data on a monthly basis from January 2000 to February 2005 inclusive.
54
x
This variable carried the symbol NPL/ALLA in this study. It was also analyzed on its own so as to determine credit default rate and trend as a time series dependent variable, with time as the independent variable.
Economic activity Economic activity was taken to mean the value of goods and services produced in the country in a particular year. The proxy used in this study was real Gross Domestic Product (GDP). This data was captured on an annual basis from 2000 to 2005, with 2005 value taken to be forecast value using linear regression. Government Credit Extension The total bank lending to the Government was captured as a percentage of Total Banking Assets (TBA). This was captured as time series data on a monthly basis from January 2000 to 2005 February inclusive. This resulted in 62 observations. This variable carried the symbol Govt/TBA in this study. 3.5
Summary Theory predicts that information sharing among lenders attenuates adverse selection problems and moral hazard and thus could help increase lending and reduce credit default rates. The study hypothesised that credit default was high in Zambia and that it had an effect on credit extension to the private sector. The Zambian credit market was rendered inefficient by the lack of comprehensive information sharing among lenders. With the research hypotheses developed and research variables operationally defined, Chapter four
outlines
methods
of
data
collection
and
mathematical
model
specifications for analysing the data in order to test the hypotheses. It should be noted that banking sector in this study also includes non-bank financial institutions under the supervision of the Bank of Zambia.
55
Chapter Four Methodology and Model Specifications 4.0
Introduction The previous chapter operationalised the predictor and dependent variables emanating from the research hypotheses. This chapter seeks to outline methods of data collection and the models used in testing the research hypotheses.
4.1
Data Collection Secondary Data was collected from the Internet, textbooks and publications made in the local and foreign credit markets. The quantitative data needed for the study taken monthly from January 2000 to February 2005 giving a total of sixty-two (62) observations were collected. The deflated data were presented as ratios except for aggregate Total Banking Assets (TBA) figures. The nominal figures obtained from the Bank of Zambia (BoZ) were deflated using the Consumer Price Indices (CPI) obtained from the Central statistical Office (CSO) and re-based so as to have 2000 as the base year (2000=100). According to Hanke and Reitsch (1991), the primary reason for finding or calculating a good index number is to deflate time series data that is measured in any currency. The analyst often would want to remove the effects of price or value of inflation before subjecting the series to further analysis. Accordingly, they recommended the steps in Figure 4.1 for deflating time series data:
56
)LJXUH+DQNH¶V0HWKRGRIGHIODWLQJ7LPH6HULHVGDWD Step 1 Find an appropriate Price Index Step 2 Move the decimal point in the price index two places to the left Step 3 Divide each value in the time series by the price index for that period
Source: Hanke and Reitsch (1991)
In addition to credit default, credit extension and CPI data, Gross Domestic Product (GDP) data for 6 years taken annually were obtained for the period 2000 to 2005 (2005 value was regression analysis forecast). 4.2
Method of Data Analysis To establish the trend and rate of credit default in the Banking sector and to determine whether there was a relationship between credit default and private sector credit, the study used time series secular trend analysis and simple linear regression analysis and thus employed the Ordinary Least Squares (OLS) method. The data were processed using a statistical package called Statistical Package for Social Sciences (SPSS 12). The resulting estimated regression equations and the coefficients of correlation and determination indicated the direction and strength of the relationships between the independent and dependent variables. Furthermore, t-tests were carried out at a confidence level of 95 percent (significance level of 5 percent) to make conclusions about the direction and strength of the relationships between the dependent and independent variables.
4.2.1 Model Specifications The least square procedure was used to find the straight line that best fits the observed time series data. Appendix 2 explains Time series trend analysis and regression analysis as well as the interpretation thereof.
57
Trend analysis For credit default data, the proxy used was Non Performing loans (NPL) captured as a percentage of the banking sector Aggregates of Loans, Leases and Advances (NPL/ALLA). The equation in Figure 4.2 below describes the model. This is the same procedure used in simple linear regression analysis. For time series analysis, Credit Default (NPL/ALLA) was the variable being analysed, and t was a coded value to represent the month concerned (Hanke and Reitch, 1991). Figure 4.2 Mathematical model for credit default analysis
NPL/ALLAt = bo +b1t Where i
NPL/ALLAt= Forecast trend value of Credit Default as a percentage of Aggregate Loans,Leases and Advances (ALLA) for selected coded time period t
i
t=Value of time selected
i
bo=constant or value of NPL/ALLA when t is coded zero.
i
b1=Slope of the trend line (rate of change)
Source: Formulated by Author
Regression Analysis Credit Default by Private Sector Credit extension The relationship between Credit default (NPL/ALLA) and private sector credit extension represented by Aggregate leases, loans and Advances to the private sector (ALLA) as percentage of Total Banking Assets (TBA) was also analyzed. 58
The equation in Figure 4.3 below describes the predictor/independent and GHSHQGHQW YDULDEOHV¶ UHODWLRQVKLS 7KLV ZDV D VLPSOH OLQHDU UHJUHVVLRQ analysis where private sector credit (ALLA/TBA) was the dependent variable while Credit default to Aggregate Leases, Loans and Advances ratio (NPL/ALLA) was the independent variable and vice versa (Hanke and Reitch, 1991)
Figure 4.3 Relationship between credit default and credit to the private sector
ALLA/TBA t = bo +b1 NPL/ALLAt Where
i
ALLA/TBA t = Forecast trend value of private sector credit for a given credit default (NPL/ALLA) level.
i
NPL /ALLAt=Credit default at time t
i
bo=constant.
i
b1=Slope of the trend line ( rate of change )
Source: Formulated by Author
GDP by Private sector Credit extension The relationship between GDP and private sector credit extension represented by Aggregate Leases, loans and Advances to the private sector (ALLA) was also analyzed.
59
The equation in Figure 4.4 below describes the predictor/independent and GHSHQGHQW YDULDEOHV¶ UHODWLRQVKLS 7KLV ZDV D VLPSOH OLQHDU UHJUHVVLRQ analysis where private sector credit (ALLA) was the predictor variable while GDP was the dependent variable (Hanke and Reitch, 1991)
Figure 4.4 Relationship between GDP and credit to the private sector
GDP t = bo +b1 ALLA t Where
i
GDP t = Forecast trend value of GDP for a given t private sector credit ALLA level.
i
ALLA t = private sector credit at time t
i
bo=constant.
i
b1=Slope of the trend line ( rate of change )
Source: Formulated by Author
4.3
Limitations of Study Every research has limitations. This particular one was no exception. Any VFLHQFH WKDW GHSHQGV RQ SHRSOH¶V EHKDYLRXU DV ZHOO DV FRPSOH[ interrelationships between variables is marred by uncertainties and error margins. Therefore every statistical model is a compromise. However, any model should be easy and simple to use and interpret. It should also be capable of describing the reality and being able to forecast behaviour with reasonable accuracy. The time series and simple regression models that the research employed may not have captured other factors that may affect credit 60
default and other factors that may affect GDP and credit extension to the private sector. Sweeney et al (2002) recommended caution in dealing with tests of significance involving one dependent variable and one independent variable. The caution is fully explained in appendix 2 of the study. In addition, according to Anderson (2002), the accuracy of data compiled in developing countries may bias results. The accuracy of the findings of this study depended on the accuracy of the data obtained from the Bank of Zambia, Central Statistical Office and other relevant institutions. Only monthly credit extension figures and credit default figures after December 1999 were available. On the relationship between private sector credit and gross domestic product, the observations from 2000 to 2005 were too few. Therefore the results should be treated with caution because if the observations were more, the result may have been different. Furthermore, the study only covered the banking and non-bank financial institutions sector because information was readily available at the BoZ. A survey of the whole banking sector, all capital market players and other credit grantors other than financial institutions in the country would have given a full picture of the level and rate of credit default in the Zambian economy.
Some of the limitations cited above are due to time, financial and other resource constraints. The next chapter presents and interprets the findings of the study.
61
Chapter Five Research Findings and Interpretation 5.0
Introduction Chapter four discussed data collection methods and specified models used in data analysis. This chapter discusses the results of the time series trend and regression analyses using the Statistical Package for Social Sciences version twelve (SPSS 12). It also interprets the results in the context of the research problem statement, objectives, questions and hypotheses. The key results are outlined in the sections below while the set of the scatter plots and regression analysis results alluded to but not outlined or shown in detail in the chapter are included in Appendix 1.
5.1
Credit Default The proxy for the dependent variable of credit default was taken to be NonPerforming Loans (NPL) as a percentage of Aggregate Loans, Leases and Advances (ALLA) to the private sector, that is NPL/ALLA. This being time series trend analysis, the independent variable was taken to be time in months. The scatter plot and regression analysis of credit default by time in months revealed the results in Tables 5.1 (Appendix 1) and Figure 5.1 below. From the scatter plot, credit default exhibited a decreasing trend over time. This relationship between credit default and time in months appeared to be approximated by a straight line; indeed a negative linear relationship existed between time in months and credit default.
The simple linear regression
model was chosen to represent the relationship on the basis of the scatter plot results. The regression analysis produced the following estimated simple linear regression equation:
62
NPL/ALLAt = 0.288-0.004 t The above equation was from the linear model y =b0 +b1x
where
y = estimated value of the dependent variable, credit default, for month t. x = coded value of independent variable month t b0= constant .The y intercept of the estimated regression line representing value of y at time 0 . b1= the slope of the estimated regression equation. This is the rate of change in the y dependent variable with 1 unit change in the independent variable x. Figure 5.1a Scatter Plot of Credit Default by time in months 0.40
CreditDefault
0.30
0.20
0.10
R Sq Linear = 0.796
0.00
10.00
20.00
30.00
40.00
Months
63
50.00
60.00
70.00
From Tables 5.1 in Appendix 1, the negative Pearson correlation coefficient of R= -0.892 meant that there was strong negative relationship between credit default and time in months. In the same vein, the negative slope b1= -0.004 meant that as time t in months increased, credit default was reducing. Therefore, it could be concluded that credit default, (NPL/ALLA), was decreasing by 0.004 from January 2000 and February 2005. The coefficient of determination
R2 =0.796 implies that 79.6% of the variability in credit
default was explained by the fitted least square line, while the other 20.4 % was explained by other factors. Test of significance (t-test) In a simple linear regression model, if x and y are linearly related, B1 z 0 , that is , the gradient is not equal to zero. The purpose of this test was to see whether or not it could be concluded that a negative linear relationship existed between the dependent and independent variables. Hence the Hypotheses: H0 : B1 = 0 , the gradient was 0 thus no relationship between the variables Ha : B1 ¢ 0 , the slope is less than 0 proving negative linear relationship The test statistic was t= b 1 Sb1
where
Sb1 = The standard deviation or error of the slope b1. b1= slope . The rate of change of dependent variable as independent variable changes From the regression analysis results, calculated t = -15.308
64
Rejection rule Using test statistic:
Reject H0 if t ¢ - tD
Where tD is based on t- distribution with n-2 degrees of freedom
From statistical tables, t0.05 = -1.671
at degrees of freedom 60 and
D 0.05 , that is , significance level of 5%. Since -15.308 ¢ -1.671, H0 was rejected and conclusion made at 5% level of significance that B1 ¢ 0 . See Figure 5.1b below. There was sufficient statistical evidence based on the given data to conclude that a significant negative linear relationship existed between credit default and time in months. It could thus be concluded at 95% confidence level that credit default was decreasing in the banking sector over time. Figure 5.1b Test of significance- rejection or acceptance region Rejection region
-15.308
Acceptance Region
-1.671
0
65
5.2
Credit Default and Private Sector Credit Extension Relationship The proxy for the dependent variable of credit default was taken to be NonPerforming Loans (NPL) as a percentage of Aggregate Loans, Leases and Advances (ALLA) to the private sector, that is NPL/ALLA. The proxy for the independent variable of private sector credit extension was taken to be Aggregate Loans, Leases and Advances (ALLA) to the private sector as a percentage of Total Banking Assets (TBA). The scatter plot and regression analysis of credit default by private sector credit for 62 months revealed the results in Tables 5.2 (Appendix 1) and Figure 5.2a below. From the scatter plot, credit default exhibited an increasing trend as private sector credit extension increased. This relationship between credit default and private sector credit levels appeared to be approximated by a straight line; indeed a positive linear relationship existed between credit default and private sector credit. The simple linear regression model was chosen to represent the relationship on the basis of the scatter plot results. The regression analysis produced the following estimated simple linear regression equation: NPL/ALLAt = -.273+1.365 ALLA/TBAt The above equation was from the linear model y =b0 +b1x where y = estimated value of the dependent variable credit default for month t. x = coded value of independent variable private sector credit in month t b0= Constant . The y intercept of the estimated regression line representing the value of y when private sector credit ,x, was 0 . b1= the slope of the estimated regression equation. This was the rate of change in the y dependent variable with 1 unit change in the independent variable x.
66
Figure 5.2a Scatter Plot of Credit Default by private sector credit 0.40
CreditDefault
0.30
0.20
0.10
R Sq Linear = 0.502
0.22
0.24
0.26
0.28
0.30
0.32
0.34
0.36
0.38
PrivateSectorCredit
)URP7DEOHVLQ$SSHQGL[WKHSRVLWLYH3HDUVRQ¶VFRUUHODWLRQFRHIILFLHQW of R= +0.708 meant that there was a strong positive relationship between credit default and private sector credit extension. In the same vein, the positive slope b1= 1.365 meant that as private sector credit levels increased, credit default was increasing from January 2000 and February 2005 by 1.365. The coefficient of determination R2 =0.502 implied that 50.2% of variability in credit default was explained by credit extension to the private sector while the other 49.8 % was explained by other factors. 67
Test of significance (t-test) In a simple linear regression model, if x and y are linearly related,
B1 z 0
,that is , the gradient is not equal to zero. The purpose of this test was to see whether or not it could be concluded that a linear relationship existed between the dependent and independent variables. Hence the Hypotheses H0 : B1 = 0
, the gradient is 0 thus no linear relationship between the
variables Ha : B1 ² 0 , the slope is greater than 0 proving positive linear relationship between variables The test statistic was t= b 1 Sb1
where
Sb1 = The standard deviation or error of the slope b1. b1= slope . The rate of change of dependent variable as independent variable changes From the regression analysis results, calculated t = 7.77 Rejection rule Using test statistic: Where
if t ² tD
Reject H0 if
tD is based on t- distribution with n-2 degrees of freedom
From statistical tables, t0.05 = 1.671
at degrees of freedom 60 and D 0.05
,that is , significance level 5% . Since 7.77 ² 1.671, H0 was rejected and conclusion made at 5% level of significance that B1 ² 0. See Figure 5.2b below.
68
The statistical evidence was sufficient for concluding that a significant linear relationship existed between credit default and private sector credit extension. It could thus be concluded at 95% confidence level that credit default was increasing as credit extension to the private sector increased in the banking sector. Figure 5.2b Test of significance-rejection or acceptance region Rejection Acceptance Region
0
5.3
region
1.671
7.773
Discussions and Interpretation of Results on Credit Default The results have shown that credit default in the banking sector was decreasing contrary to perceptions held by the Banks and Non Bank Financial institutions. However, in tandem with this decreasing credit default was a corresponding decreasing trend in credit extension to the private sector as was observed in Figure 5.3 below (see also regression results Tables 5.6 in Appendix 1):
69
Figure 5.3 Scatter Plot of Private sector credit (ALLA/TBA) by months
0.38
0.36
PrivateSectorCredit
0.34
0.32
0.30
0.28
0.26
0.24
R Sq Linear = 0.296
0.22 0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
Months
While credit extension to the private sector exhibited a downward trend, credit extension to the Government exhibited an upward trend as depicted in Figure 5.4 below (see also regression results Tables 5.7 in Appendix 1) :
70
Figure 5.4 Scatter Plot of Government Credit (Govtcredit/TBA) by months
0.50
0.48
GovtFFI
0.46
0.44
0.42
0.40
0.38
R Sq Linear = 0.078
0.36 0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
Months
From another perspective, while credit extension to the private sector was reducing, credit extension to the Government and Foreign Financial institutions was increasing as depicted by Figure 5.4a below:
71
Figure 5.4a Scatter Plot of Private sector Credit (ALLA/TBA) by Government Credit
0.38
0.36
PrivateSectorCredit
0.34
0.32
R Sq Linear = 0.679
0.30
0.28
0.26
0.24
0.22 0.36
0.38
0.40
0.42
0.44
0.46
0.48
0.50
GovtFFI
The Scatter plot above showed 68% coefficient of determination between Private sector credit and Government credit extension. The Implication was that the more government borrowed from the banking sector, the less the private sector had access to credit. The full regression results for this relationship were in the Tables 5.3 in Appendix 1 and the estimated regression equation was as follows: ALLA/TBA t = 0.765 -1.050GovtFFI/TBA t 72
The estimated equation above is from the linear model y =b0 +b1x where y = estimated value of the dependent variable private sector credit at a given level of government credit . x = coded value of independent variable ,Government credit level. b0= constant . The y intercept of the estimated regression line representing the value of y when x is 0 . b1= the slope of the estimated regression equation. This is the rate of change in the y dependent variable with 1 unit change in the independent variable x. From Tables 5.3 in Appendix 1, the negative Pearson correlation coefficient of R= -0.824 meant that there was strong negative relationship between private sector credit and Government levels of credit. In the same vein, the negative slope b1= -1.050 meant that as government credit increased, private credit was reducing. The coefficient of determination R2 =0.679 implies that 67.9% of variability in private credit was explained by change in government credit levels while the other 32.1 % was explained by other factors. Test of significance (t-test) In a simple linear regression model, if x and y are linearly related,
B1 z 0
,that is , the gradient is not equal to zero. The purpose of this test was to see whether or not it could be concluded that a linear relationship existed between the dependent and independent variables. Hence the Hypotheses: H0 : B1 = 0 ,
the gradient is 0 thus no linear relationship between the
variables Ha : B1 ¢ 0 , the slope is less than 0 proving negative linear relationship between variables . The test statistic was t= b 1 where Sb1 Sb1 = The standard deviation or error of the slope b1. 73
b1= slope . The rate of change of dependent variable as independent variable changes From the regression analysis results, calculated t = -11.278 Rejection rule: using test statistic, Reject H0 if t ¢ - tD Where tD is based on t- distribution with n-2 degrees of freedom. From the statistical tables , t0.05 = -1.671 at degrees of freedom 60 and D 0.05 ,that is significance level 5%. Since -11.278 ¢ -1.671
H0 is rejected and it should be concluded at 5%
level of significance that B1 ¢ 0 . The statistical evidence is sufficient for concluding that a significant linear relationship existed between private sector credit and government and foreign financial institutions credit. It could thus be concluded at 95% confidence level that private credit was decreasing in the banking sector as government credit increased. Figure 5.4b Test of significance- rejection or acceptance region Rejection region
-11.278
Acceptance Region
-1.671
0
Source: Compiled by Author from SPSS12 results
The results further show that lending institutions experienced decreased credit default because of lending more to the government than the private sector. This was because credit risk levels were very low in lending to the 74
government. In this regard credit default was showing a decreasing trend because lending to the private sector was decreasing while government credit was increasing as depicted in the scatter plot below (see also regression results Tables 5.8 in Appendix 1) Figure 5.5 Scatter Plot of credit default by Government Credit 0.40
CreditDefault
0.30
0.20
R Sq Linear = 0.205
0.10
0.36
0.38
0.40
0.42
0.44
0.46
0.48
0.50
GovtFFI
5.4
Some Possible Causes of Credit Default Credit Default could be caused by many factors. One appropriate way to explain causes of credit default was to use the theory of determinants of interest rates (Brigham et al, 1999). The concept was fully explained in Chapter
75
three of this study regarding the cost of money. The composition of quoted interest rates is normally as follows:
Quoted interest rate =k= K* + IP + DRP + LP + MRP +CF where R k = the quoted or nominal rate of interest on a given credit facility . There are many different credit facilities; hence many quoted interest rates. R K* = the real risk-IUHHUDWHRILQWHUHVW . LVSURQRXQFHG³N-star) R IP = Inflation Premium R DRP = Default Risk Premium R LP = Liquidity or Marketability Premium R MRP = Maturity Risk Premium R CF= Cost of Funds (includes the cost structure of the lender) Going by the above composition of quoted interest rates, where the risk-free rate is high, interest rates generally in that economy are likely to be high. The risk free rate is the rate at which government borrows from the market through Treasury bills and Government bonds. The higher the risk free rate, the higher the interest rates to the private sector in an economy because most lenders would rather lend to the government since the risk of default is low. This crowds out the private sector, raising interest rates in the process if the supply of loanable funds to the private sector is lower than demand. From another perspective, the higher the interest rates, the higher the cost of borrowing for the private sector, the harder it is for private borrowers to pay back loans thus increasing the credit default rate. Thus to reduce credit default through reduced interest rates on credit to the private sector, government ought to reduce not only its borrowing from the financial sector but also reduce the interest rates at which it borrows through treasury bills and government bonds. Furthermore, expected path of future persistent Inflation tends to make banks and other suppliers of credit apprehensive about the future value of their loans to maturity. So lenders tend 76
to increase their lending rates in order to maintain the value of their loans thus increasing the cost of borrowing and in turn the likelihood of credit default.
The size of statutory reserve ratio (SSR) at 14 percent of demand and time deposits, at the time of this work, implied an opportunity cost of funds held at BoZ. The other prudential benchmark banks ought to meet on the same deposits was the core liquid assets ratio (CLAR) at 35 percent, which was made up of cash in Zambian Kwacha, treasury bills and other balances due from the central bank excluding SRR balances and GRZ bonds. This left only 51 Percent that banks could freely lend. So for any say K1 million collected in deposits, K490,000 will have to be used to meet regulatory requirements explained above (Post ,Tuesday 15 November 2005) . Due to the high dependence on interest income and low fee based service income, banks have to raise lending rates in order to meet their profitability targets (Post, Tuesday 15 November 2005). This increased the cost of borrowing, which in turn increased the likelihood of credit default. Thus lowering these bench-marks would reduce the cost structure of banks. History of credit default perpetuated by both unfavourable economic environment (characterised by high unemployment, low disposable incomes, low purchasing power, high cost of energy, etc) and lack of infrastructure in the financial sector to trace bad debtors also contributed to increased default risk premiums. This further increased the cost of borrowing and in turn increasing the likelihood of credit default .To reduce credit risk premium in the interest rates Credit Reference Bureaus would help in comprehensively screening truant and problematic customers in order to address some of the incidences of loan repayment delinquency and pave way for increased credit to credible private sector borrowers. See detailed account of the role of Credit Reference Bureaus in the Literature Review Chapter two of this study. From empirical literature surveyed in Chapter two of this study, it was clear that in credit markets were credit information sharing is comprehensive, credit 77
extension to the private sector was high and interest rates and credit default were lower. This was because of efficiencies achieved in lending decisions and accuracy in credit default predictions. But an appropriate legal framework and political commitment were necessary for effectiveness of a CRB.
Impact of Credit on the National Economy Both private sector credit and Government credit have an impact on the level of economic growth.
Regression analyses were conducted to determine
WKHVH UHODWLRQVKLSV )LUVWO\ *RYHUQPHQW FUHGLW¶V UHODWLRQVKLS ZLWK *URVV Domestic Product was analyzed .The results were as follows: Figure 5.6 Scatter Plot of GDP by Government Credit 12500.00
12000.00
11500.00
GDP
5.5
11000.00
10500.00
R Sq Linear = 0.46
10000.00
9500.00 900.00
1000.00
1100.00
1200.00
1300.00
GovtCredit
78
1400.00
1500.00
1600.00
The Scatter plot above showed 46% coefficient of determination between Gross Domestic Product and Government credit extension. The Implication was that the more government borrowed from the financial sector, the more growth was observed in the gross domestic product. The full regression results for this relationship were in the tables 5.4 in Appendix 1 and the estimated simple linear regression equation as follows: GDP t = 6533.427 +3.385Govtcreditt The above equation was from the linear model y =b0 +b1x where y = estimated value of the dependent variable GDP at a given level of government credit . x = coded value of independent variable Government credit level b0= Constant. The y intercept of the estimated regression line representing the value of y when x is 0 . b1= the slope of the estimated regression equation. This is the rate of change in the y dependent variable with 1 unit change in the independent variable x. From Tables 5.4 in Appendix 1, the positive Pearson correlation coefficient of R= +0.678 means that there was relatively strong positive relationship between GDP and Government levels of credit. In the same vein, the positive slope b1=3.385 means that as government credit increased, GDP was increasing. The coefficient of determination R2 =0.46 implies that 46% of the variability in GDP was explained by the fitted least square line , while 64 % was explained by other factors. Test of significance (t-test) In a simple linear regression model, if x and y are linearly related, B1 z 0 i.e the gradient is not equal to zero. The purpose of this test was to see whether or not it could be concluded that a linear relationship existed between the dependent and independent variables. Hence the Hypotheses 79
H0 : B1 = 0
, the gradient is 0 thus no linear relationship between the
variables Ha : B1 z 0 , the slope is not equal to 0 proving linear relationship between variables The test statistic was t= b 1 Sb1
where
Sb1 = The standard deviation or error of the slope b1. b1= slope . The rate of change of dependent variable as independent variable changes From the regression analysis results, calculated t = 1.846 Rejection rule Using test statistic:
Where
tD
2
t ²t D
Reject H0 if
2
or t
¢ -t D 2
is based on t- distribution with n-2 degrees of freedom
From the statistical tables , t0.025 = 2.776
at degrees of freedom 4 and
D 0.05 ,that is , significance level 5% .
Since 1.846
¢ 2.776
H0 is accepted and it should be concluded at 5% level
of significance that B1 =0 . Based on the data captured, the statistical evidence was sufficient for concluding that a significant linear relationship did not exist between GDP and government credit.
80
Figure 5.7 Test of significance- rejection or acceptance region Rejection Rejection region
Acceptance Region
-2.776
0
region
1.846 2.776
Figure 5.8 Scatter Plot of GDP by Private sector credit extension 12500.00
12000.00
GDP
11500.00
11000.00
10500.00
R Sq Linear = 0.016
10000.00
9500.00 700.00
800.00
900.00
PSCE
81
1000.00
1100.00
The Scatter plot above showed 1.6% coefficient of determination between Gross Domestic Product and private sector credit extension. The Implication was that private sector credit impacted positively on growth of GDP. The full regression results for this relationship were in the Tables 5.5 in Appendix 1 and the estimated simple linear regression equation was as follows: GDP t = 10062.943 +0.923 ALLAt The equation above was from the linear model y =b0 +b1x where y = estimated value of the dependent variable GDP at a given level of private sector credit . x = coded value of independent variable ,private sector credit level. b0= Constant. The y intercept of the estimated regression line representing the value of y when x is 0 . b1= the slope of the estimated regression equation. This is the rate of change in the y dependent variable with 1 unit change in the independent variable x. From Tables 5.5 in Appendix 1, the positive Pearson correlation coefficient of R= +0.126 means that there was relatively weak positive linear relationship between GDP and private sector levels of credit. In the same vein, the positive slope b1=0.923 means that as private credit increased, GDP was increasing. The coefficient of determination R2 =0.016 implies that 1.6% of change in GDP was explained by change in private credit while the other 98.4 % was explained by other factors. Test of significance (t-test) In a simple linear regression model, if x and y are linearly related, B1 z 0 i.e the gradient is not equal to zero. The purpose of this test was to see whether or not it could be concluded that a linear relationship existed between the dependent and independent variables. Hence the following Hypotheses: 82
H0 : B1 = 0
, the gradient is 0 thus no linear relationship between the
variables Ha : B1 z 0 , the slope is not equal to 0 proving linear relationship between variables The test statistic was t= b 1 Sb1
where
Sb1 = The standard deviation or error of the slope b1. b1= slope. The rate of change of dependent variable as independent variable changes From the regression analysis results, calculated t = 0.254 Rejection rule Using test statistic:
Where
tD
2
t ²t D
Reject H0 if
2
or t
¢ -t D 2
is based on t- distribution with n-2 degrees of freedom
From the statistical tables , t0.025 = 2.776
at degrees of freedom 4 and
D 0.05 ,that is,. significance level 5% . Since 0.254
¢ 2.776
H0 is accepted and it should be concluded at 5% level
of significance that B1 =0 . Based on the data captured, the statistical evidence was sufficient for concluding that a significant linear relationship did not exist between GDP and private sector credit.
83
Figure 5.9 Test of significance - rejection or acceptance region Rejection Rejection region
Acceptance Region
-2.776
0
region
0.254 2.776
The results indicated that there was positive relationship between credit extensions to the Government and the private sector and GDP. Meaning that as credit extension to both the private sector and government sector increased, there was positive growth in the gross domestic product. However, the relationships, though positive, could not be said to be linear. 5.6
Summary Based on the results above, there was need to be concerned when access to credit by either the private sector or the government was limited because the rate of growth in GDP was adversely affected. Furthermore, the results showed that lending institutions experienced decreased credit default because of lending more to the government than to the private sector. The consolidated Banking assets were growing at a fast rate as depicted by the scatter plot in Figure 5.10 and Tables 5.9 in appendix 1. However, as already noted credit to the private sector was declining while credit to the Government was growing. To increase lending to the private sector, the players in the financial system should address causes of credit default identified in section 5.4 of this Chapter. Having discussed and interpreted the above findings, the next chapter makes conclusions based on the study objectives, the methods employed to achieve the objectives and the findings of the study. 84
Chapter Six Conclusions and Recommendations 6.0
Introduction Chapter four presented and interpreted the findings of the study. This chapter seeks to relate the objectives and hypotheses of the study to the findings in light of the methods used to achieve the set objectives. The Chapter then draws conclusions from the findings and ends by making recommendations and identifying area for further study.
6.1
Research Objectives, Hypotheses and Methodology In Chapter one, the study set out to achieve the following objectives: x
To determine the Credit default rate and trend in the banking sector
x
To determine whether there was a relationship between credit default and the level of private sector credit extension from the banking sector.
x
To establish if there was a relationship between economic activity and access to credit by the private sector.
x
To establish from the literature review what the effect of credit reporting systems was on private sector access to credit in markets that had introduced them.
x
To confirm or disapprove the establishment of the credit Reference Bureau.
To accomplish the above objectives, the study obtained secondary data from the Internet, textbooks and publications in both the local and foreign credit markets on the subject of credit information sharing in Chapter two. This led to the theoretical and conceptual framework in chapter three used to arrive at the following hypotheses: 85
1.
Default Rate and Trend Ho: Credit default in the banking sector was not increasing H1: Credit default in the banking sector was increasing
2.
Default rate vs. Private sector credit levels Ho: There was no relationship between Credit default and private sector credit H1: There was a relationship between Credit default and private sector credit
3.
Impact of credit on economic growth H0:There was no relationship between credit extension and economic activity H1: There was a relationship between credit and economic activity
After operationalising the above hypotheses' dependent and predictor variables, quantitative data on credit default, private sector and government sector credit extension, consumer price index and Gross Domestic Product were collected as discussed in Chapter four. The deflated data were subjected to time series secular trend and regression analyses. This was done with the help of the statistical package for social sciences version 12.0 (SPSS12) .The resulting estimated regression equations and the coefficients of correlation and determination indicated the direction and strength of the relationships between the predictor and dependent variables. Furthermore, ttests were carried out at significance level of 5 percent to make conclusions about the direction and strength of the relationships between the dependent and predictor variables. 6.2
Findings In Chapter five, the study established that credit default in the banking sector was decreasing and not increasing. The study also established that there was a positive relationship between credit default and private sector credit extension; it was observed that as credit to the private sector increased credit default also increased. Conversely as credit extension to the private sector reduced, credit default also reduced. This confirmed the perception that the lenders had that the private sector in Zambia posed high credit default risk. It 86
also confirmed the perception that the credit culture in Zambia was poor. No wonder the private sector was being rationed out of credit. Furthermore, the study established that there was a negative relationship between credit extension to the private sector and credit extension to the government and foreign financial institutions; as credit to the government increased, credit to the private sector reduced. It followed that if credit to the private sector was to increase government borrowing from the local banking sector had to reduce. This confirmed the concept that government borrowing crowds out the private productive sector from access to debt finance. From another perspective, the study established that there was a positive relationship between credit extension to the public sector and Gross Domestic Product. Similarly, the study also established that there was a positive relationship between credit extension to the private sector and Gross Domestic Product. Albeit, these relationships were not linear. That caution notwithstanding, the Zambian economy would be impacted positively if the level of credit extension to the private sector increased. Lastly the study established that in credit markets where credit information sharing was comprehensive among lenders, lending to the private sector was high, credit default rates were low and interest rates were lower. 6.3
Conclusions On the basis of the findings above, not only were the research objectives achieved but the research questions were also answered. In summary, the study made the following conclusions: i In the first set of hypotheses, the study failed to reject the null hypothesis that credit default was not increasing. The conclusion was that credit default was decreasing in banking sector over time.
87
i In the second set of hypotheses, the study rejected the null hypothesis that there was no relationship between credit default and private sector credit extension. Instead the study concluded that there was a positive relationship between credit default and private sector credit extension; as private sector credit increased, credit default also increased. i In the third set of hypotheses, the study rejected the null hypotheses that there was no relationship between credit extension and economic activity. The study concluded that there was a positive relationship between credit extension (whether government or private sector) and economic activity. The economy is impacted positively if credit extension increased. i Lastly, the study established that in credit markets were credit information sharing was comprehensive among lenders, lending to the private sector was higher, credit default rates and interest rates were lower. i The study managed to justify the establishment of a Credit Reference Bureau on the basis of the above findings and conclusions. 6.4
Recommendations Seeing that credit default increased as credit extension to the private sector increased, it would make the credit processes more efficient in deciding who to lend to and who not to lend to if the banking sector had a comprehensive credit information sharing system than what currently exists. It was recommended therefore that a Credit Reference Bureau be established in the banking sector. This would enable the lenders to distinguish between good borrowers and bad borrowers easily and thus reduce credit default risk and incidences of loan repayment delinquency. This would help the lending institutions increase credit to credible borrowers in the private sector thereby impacting positively on economic growth.
88
However, as discussed in chapter two, credit-reporting systems require not only specific and appropriate legal framework but also political good will to operate effectively and efficiently. This is so because in developing countries, politics have an influence on every aspect of the economy. In fact politicians are major credit defaulters, apparently with impunity (Post, Tuesday November 1,2005). Thus the study recommended that a legal framework be speedily put in place for the Credit Reference Bureaus and that there be political commitment not to interfere with the operations of the CRB. Having established a positive relationship between private sector credit and economic activity, it was recommended that other options be explored that would result in the private sector having more access to credit. This could include reduction in government borrowing to reduce the crowding out effect on the private sector. It could also include all measures intended to reduce borrowing interest rates so that borrowing could become more affordable to the private sector thereby reducing the likelihood of credit default.
6.5
Area for Further Research This work focused on determining credit default rates and trends in relation to credit extension to the private sector and government sector and the impact of credit on GDP as a basis for justifying the establishment of a Credit Reference Bureau in the Banking Sector.The area identified for further detailed research was an empirical analysis of the causes of credit default in the banking sector as a basis for influencing public policy in further developing the financial sector.
Lastly, once the credit reference bureau is
established, there is need to conduct studies to see how the presence of credit reference services will affect the default prevalence in Zambia. This suggested study could also be undertaken in other parts of Africa and indeed other developing countries that have implemented credit reference services.
89
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The
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(UE%&ODXGH&DPSEHOO5+DUYH\DQG7DGDV(9LVNDQWD ³Political Risk, Economic Risk, and Financial Risk´ Financial Analyst Journal, November/December, 29-45. (QJHOPDQQ%(+D\GHQDQG'7DVFKH ³Measuring the discriminative power of rating systems´Discussion paper,Series 2: Banking and Financial Supervision, no 01/2003,Deutsche Bundesbank. Fabozzi F and Mogdilliani M (2002), The Foundations of Financial Markets & Institutions, 3rd ed, Prentice Hall, New York. Financial Sector Development Plan (2004) Fuentes, R and Carlos Maquieira (1998) Determinants of loan repayment in Chile, School of Business and Economics, Universidad de Chile. Fuentes, R and Carlos Maquieira(2003) Institutional arrangements, credit market Development and loan repayment in Chile, School of Business and Economics,Universidad de Chile. *DOLQGR$UWXURDQG0DUJDUHW-0LOOHU ³Can Credit Registries Reduce Credit Constraints? Empirical Evidence on the Role of Credit Registries in Firm Investment Decisions´3DSHUSUHSDUHGIRUWKH$QQXDO0HHWLQJVRIWKH,QWHUAmerican Development Bank, Santiago, Chile, March. *DOLQGR $ DQG 0 - 0LOOHU ³&DQ FUHGLW UHJLVWULHV UHGXFH FUHGLW FRQVWUDLQWV" Empirical evidence on the role of credit registULHV LQ ILUP LQYHVWPHQW GHFLVLRQV´ Annual Meeting, Inter-American Development Bank, March, pp 1-26. *RRGKDUW&$( ³The organisational structure of banking supervision´ FSI Occasional Papers, no 1, Financial Stability Institute. Hoggarth, G anG96DSRUWD ³Costs of banking system instability: some empirical evidence´Financial Stability Review, Bank of England, June, pp 148-65. Hanke J and Reistch A (1991), Understanding Business Statistics, Urwin, Bolston ,USA. Jappelli, Tullio and MDUFR 3DJDQR ³Information Sharing, Lending and Defaults:Cross-Country Evidence´ Journal of Banking and Finance, October, 26(10), 2017-45. -DSSHOOL 7XOOLR DQG 0DUFR 3DJDQR ³Public Information: A European Perspective´LQCredit Reporting Systems and the International Economy, Margaret Miller, ed. Cambridge: MIT Press 91
-DSSHOOL 7 DQG 0 3DJDQR ³Information sharing in credit markets: a survey´CSEF Working Papers, no 36, March, pp 4-25. Kallberg, Jarl G. and Gregory F. Udell (2003)³The Value of Private Sector Credit Information: the U.S. Case´Journal of Banking and Finance, 27(3), 449-69. Koch ,B (1988),'Bank Management' Dryden Press,New Jersey ,USA /RYH ,QHVVD DQG 1DWDOL\D 0\OHQNR ³Credit reporting and financing constraints´:RUOG%DQN3ROLF\5HVHDUFK:RUNLQJ3DSHUQ2FWREHU 0LOOHU0- ³Credit reporting systems around the globe: the state of the art in public credit registries and private credit reporting firms´ LQ 0 - 0LOOHU (ed), Credit reporting systems and the international economy, MIT Press 0F*RYHQ -RKQ µWhy bad loans happen to good banks¶, The Journal of Commercial Lending. Philadelphia: Feb 1993. Vol. 75, Issue. 6 Miller, Margaret J. (2003), Credit Reporting Systems and the International Economy (ed.).Cambridge: MIT Press. 2QJHQD 6WHYHQ DQG 'DYLG & 6PLWK ³Bank Relationships: A Review´ December,forthcoming in The Performance of Financial Institutions, P. Harker and S. A. Zenios editors, Cambridge University Press. Padilla, A. Jorge and Marco Pagano (1997) ³Endogenous Communication among Lenders and Entrepreneurial Incentives´ The Review of Financial Studies 10 (1), Winter, 205-236. 3DGLOOD $ -RUJH DQG 0DUFR 3DJDQR ³Sharing Default Information as a Borrower Discipline Device´ European Economic Review 44(10), 1951-1980.
Post Newspapers (Issue no. 3301-24) Tuesday November 01, 2005 Business Article by Kingsley Kaswende µ3RRU&UHGLW&XOWXUH,Q=DPELD:RUULHV)XQGDQJD¶
Post Newspaper (Issue No. 3316-26) Tuesday November 15, 2005 Business Article by Kingsley Kaswende µ%R=WRIDFLOLWDWHLQFUHDVHGOHQGLQJWRSULYDWHVHFWRUV¶ Post Newspaper (Issue No. 3316-26) Tuesday November 15, 2005 Business Article by Mwila Nkonge µ&RPPHUFLDOEDQNV¶UHOLDQFHRQLQWHUHVWLQFRPHVWLOOVWURQJ¶ Private Sector Development Programme (PSDP) 2004, Presentation by Dr. Silane Mwenechanya on "Business Constraints in Zambia.' 92
Ross Stephen (1999), Essentials of Corporate Finance ,2nd Ed , Prentice Hall ,New York. Sobehart, J, S Keenan and R Stein (2000): Benchmarking quantitative default risk models: a validation methodology0RRG\¶V,QYHVWRU6HUYLFH WWW.economist.com, 20 April 2005, 10:30 hours CAT www.firstinitiative.org , 11 November 2005, 12.01 CAT www.luse.co.zm, 4 may 2005 ,11:05 hours CAT www.boz.zm , 26 September 2005, 11:40 hours CAT www.zamstats.org.zm , 27 September 2005 ,14:15 CAT www.investorwords.com, 21 June 2005, 15:20 CAT Zambia Institute of Bankers, Banking & Finance Magazine, Vol 3, 2003. Zambia Institute of Bankers, Banking & Finance Magazine, Vol 4, 2004. Zambia Institute of Bankers, Banking & Finance Magazine, Vol 5, 2004
93
Appendices APPENDIX 1
Tables And Scatter Plots of SPSS Regression Results Tables 5.1 Regression analysis Results of Credit Default by time in months Descriptive Statistics Mean .1521
Std. Deviation .08724
N 62
31.5000
18.04162
62
CreditDefault Months
Correlations CreditDefault
Months
Pearson Correlation
CreditDefault
1.000
-.892
Months
-.892
1.000
Sig. (1-tailed)
CreditDefault
.
Months N
.000
.000
.
CreditDefault
62
62
Months
62
62
Model Summary(b)
Model 1
R .892(a)
R Square .796
Std. Error of the Estimate .03971
Adjusted R Square .793
a Predictors: (Constant), Months b Dependent Variable: CreditDefault ANOVA(b)
Model 1
Sum of Squares
df
Regression
.370
Residual
.095
60
Total
.464
61
Mean Square
F
Sig.
.370
234.335
.000(a)
1
.002
a Predictors: (Constant), Months b Dependent Variable: CreditDefault
94
Coefficients(a) Unstandardized Coefficients
Model
1
(Constant)
B .288
Months
-.004 a Dependent Variable: CreditDefault
Standardized Coefficients
Std. Error .010
Beta
.000
-.892
t
Sig.
28.206
.000
-15.308
.000
Tables 5.2 Regression Results of Credit Default by private sector Credit Descriptive Statistics
CreditDefault
Mean .1521
Std. Deviation .08724
N 62
PrivateSectorCredit
.3115
.04526
62
Correlations
Pearson Correlation
CreditDefault PrivateSectorCredit
Sig. (1-tailed)
.708
CreditDefault
1.000 .
PrivateSectorCredit N
PrivateSectorC redit .708
CreditDefault 1.000
R
.
CreditDefault
62
62
PrivateSectorCredit
62
62
Model Summary(b)
Model 1
.000
.000
R Square
Adjusted R Square
.708(a) .502 .493 a Predictors: (Constant), PrivateSectorCredit b Dependent Variable: CreditDefault
Std. Error of the Estimate .06209
95
ANOVA(b)
Model 1
Sum of Squares Regressio n Residual
df
.233
Total
Mean Square
F
Sig.
.233
60.420
.000(a)
1
.231
60
.464
61
.004
a Predictors: (Constant), PrivateSectorCredit b Dependent Variable: CreditDefault Coefficients(a) Unstandardized Coefficients Std. B Error
Model
1
(Constant)
-.273
.055
PrivateSectorCredit
1.365
.176
Standardized Coefficients
t
Sig.
-4.941
.000
7.773
.000
Beta .708
a Dependent Variable: CreditDefault
Tables 5.3 Regression Results of private sector Credit by Government Credit Descriptive Statistics Mean
Std. Deviation
N
PrivateSectorCredit
.3115
.04526
62
GovtFFI
.4323
.03555
62
Correlations
PrivateSectorCredit Pearson Correlation Sig. (1-tailed)
1.000
-.824
GovtFFI
-.824
1.000
PrivateSectorCredit GovtFFI
N
.
R
.000
.000
.
PrivateSectorCredit
62
62
GovtFFI
62
62
Model Summary(b)
Model 1
GovtFFI
PrivateSectorCredit
R Square
Adjusted R Square
.824(a) .679 .674 a Predictors: (Constant), GovtFFI b Dependent Variable: PrivateSectorCredit
Std. Error of the Estimate .02584
96
ANOVA(b) Sum of Squares
Model 1 Regression Residual
df
Mean Square
F
Sig.
.085
1
.085
127.185
.000(a)
.040
60
.001
t
Sig.
18.957
.000
-11.278
.000
.125 61 Total a Predictors: (Constant), GovtFFI b Dependent Variable: PrivateSectorCredit Coefficients(a) Unstandardized Coefficients
Model
1
B
Std. Error
(Constant)
.765
.040
GovtFFI
-1.050
.093
Standardized Coefficients Beta -.824
a Dependent Variable: PrivateSectorCredit
Source: Compiled by Author from SPSS12 results
Tables 5.4 Regression Results of GDP by Government Credit Descriptive Statistics
Mean GDP GovtCredit
Std. Deviation
N
10943.4721
993.16306
6
1302.8601
198.99370
6
Model Summary(b)
Model 1
R
R Square
.678(a) .460 a Predictors: (Constant), GovtCredit b Dependent Variable: GDP
Adjusted R Square .325
Std. Error of the Estimate 815.99064
97
ANOVA(b)
Regression
Sum of Squares 2268501.438
df 1
Mean Square 2268501.438
Residual
2663362.867
4
665840.717
4931864.305 a Predictors: (Constant), GovtCredit b Dependent Variable: GDP
5
Model 1
Total
F 3.407
Sig. .139(a)
Coefficients(a) Unstandardized Coefficients
Model
1
Standardized Coefficients
(Constant)
B 6533.427
Std. Error 2412.346
GovtCredit
3.385
1.834
t
Sig.
Beta .678
2.708
.054
Lower Bound -164.318
Upper Bound 13231.172
1.846
.139
-1.707
8.476
a Dependent Variable: GDP
Tables 5.5 Regression Results of GDP by Private Sector Credit Descriptive Statistics
GDP PSCE
Mean 10943.4721
Std. Deviation 993.16306
954.0309
135.41918
N 6 6
Model Summary(b)
Model 1
R
R Square
.126(a) .016 a Predictors: (Constant), PSCE b Dependent Variable: GDP
Adjusted R Square -.230
Std. Error of the Estimate 1101.56216
98
95% Confidence Interval for B
ANOVA(b)
Model 1
Sum of Squares 78107.579
df 1
Mean Square 78107.579
Residual
4853756.727
4
1213439.182
Total
4931864.305
5
Regression
F .064
Sig. .812(a)
a Predictors: (Constant), PSCE b Dependent Variable: GDP
Coefficients(a)
Unstandardized Coefficients
Model
1
(Constant)
B 10062.943
Std. Error 3499.628
.923
3.638
PSCE
Standardized Coefficients
t
Sig.
Beta 2.875
.045
Lower Bound 346.417
Upper Bound 19779.468
.254
.812
-9.177
11.023
.126
a Dependent Variable: GDP
Tables 5.6 Regression Results Of Private Sector Credit By Months Descriptive Statistics
PrivateSectorCredit Months
Mean .3115
Std. Deviation .04526
N 62
31.5000
18.04162
62
Correlations
Pearson Correlation
PrivateSectorCredit Months
Sig. (1-tailed)
Months -.544
-.544
PrivateSectorCredit Months
N
PrivateSectorCredit 1.000
1.000 .
.000
.000
.
PrivateSectorCredit
62
62
Months
62
62
99
95% Confidence Interval for B
Model Summary(b)
Model 1
R
R Square
Std. Error of the Estimate
Adjusted R Square
.544(a) .296 .284 a Predictors: (Constant), Months b Dependent Variable: PrivateSectorCredit
.03830
ANOVA(b)
Model 1
Sum of Squares .037
Regression Residual
df 1
.088
Mean Square .037
60
F 25.185
Sig. .000(a)
.001
Total
.125 61 a Predictors: (Constant), Months b Dependent Variable: PrivateSectorCredit
Coefficients(a) Unstandardized Coefficients
Model
1
(Constant)
Standardized Coefficients
B
Std. Error
.354
.010
t
Sig.
35.991
.000
.335
.374
-5.018
.000
-.002
-.001
Beta
Months
-.001 .000 a Dependent Variable: PrivateSectorCredit
-.544
95% Confidence Interval for B Lower Bound
Tables 5.7 Regression Results of Government Credit by Months Descriptive Statistics Mean
Std. Deviation
N
GovtFFI
.4323
.03555
62
Months
31.5000
18.04162
62
Correlations
Pearson Correlation
GovtFFI
GovtFFI 1.000
Months
.280
Sig. (1-tailed)
GovtFFI
N
Months .280 1.000 .
Months
.014
GovtFFI
62
Months
62
.014 . 62 62
100
Upper Bound
Model Summary(b)
Model 1
R .280(a)
Std. Error of the Estimate .03441
Adjusted R Square .063
R Square .078
a Predictors: (Constant), Months b Dependent Variable: GovtFFI ANOVA(b)
Model 1
Sum of Squares
df
Regression
.006
Residual
.071
60
Total
.077
61
Mean Square
F
Sig.
.006
5.110
.027(a)
1
.001
a Predictors: (Constant), Months b Dependent Variable: GovtFFI Coefficients(a) Unstandardized Coefficients
Model
B
Std. Error
.415
.009
.001 a Dependent Variable: GovtFFI
.000
1
(Constant) Months
Standardized Coefficients
t
Sig.
Lower Bound
Upper Bound
46.897
.000
.397
.433
2.261
.027
.000
.001
Beta .280
95% Confidence Interval for B
Tables 5.8 Regression Results of Credit Default by Government Credit
Descriptive Statistics Mean
Std. Deviation
N
CreditDefault
.1521
.08724
62
GovtFFI
.4323
.03555
62
101
Correlations CreditDefault
GovtFFI
Pearson Correlation
CreditDefault
1.000
-.453
GovtFFI
-.453
1.000
Sig. (1-tailed)
CreditDefault
.
GovtFFI N
.000
.000
.
CreditDefault
62
62
GovtFFI
62
62
Model Summary(b)
Model 1
R
R Square
.453(a) .205 a Predictors: (Constant), GovtFFI b Dependent Variable: CreditDefault
Adjusted R Square
Std. Error of the Estimate
.192
.07842
ANOVA(b)
Model 1
Regression Residual
Sum of Squares .095 .369
Total
.464 a Predictors: (Constant), GovtFFI b Dependent Variable: CreditDefault
df 1
Mean Square .095
60
F 15.492
Sig. .000(a)
.006
61
Coefficients(a) Unstandardized Coefficients
Model
1
(Constant)
B .633
GovtFFI -1.112 a Dependent Variable: CreditDefault
Standardized Coefficients
Std. Error .122
Beta
.282
-.453
t
Sig.
95% Confidence Interval for B
5.165
.000
Lower Bound .388
Upper Bound .878
-3.936
.000
-1.677
-.547
Tables 5.9 Regression Results of Total Banking Assets by Months Descriptive Statistics
TotalBankingAssets Months
Mean
Std. Deviation
N
4349.0516
1566.10947
62
31.5000
18.04162
62
102
Correlations
Pearson Correlation
TotalBankingAssets
TotalBankingA ssets 1.000
Months .993
.993
1.000
Months Sig. (1-tailed)
TotalBankingAssets
.
Months N
.000
.000
.
TotalBankingAssets
62
62
Months
62
62
Model Summary(b)
Model 1
R .993(a)
R Square .987
Adjusted R Square .987
Std. Error of the Estimate 179.91663
a Predictors: (Constant), Months b Dependent Variable: TotalBankingAssets ANOVA(b)
Model 1
df
Regression
Sum of Squares 47672430.999
Residual
1942199.626
60
Total
49614630.624 a Predictors: (Constant), Months b Dependent Variable: TotalBankingAssets
1
Mean Square 147672430.999
F 4562.016
Sig. .000(a)
32369.994
61
Coefficients(a) Unstandardized Coefficients
Model
1
(Constant)
B 1632.488
Std. Error 46.257
Months 86.240 1.277 a Dependent Variable: TotalBankingAssets
Standardized Coefficients
t
Sig.
95% Confidence Interval for B
35.291
.000
Lower Bound 1539.960
Upper Bound 1725.017
67.543
.000
83.686
88.794
Beta .993
103
Figure 5.10 Scatter Plot of total banking Assets by Months
8000.00
TotalBankingAssets
6000.00
4000.00
2000.00
R Sq Linear = 0.987
0.00 0.00
10.00
20.00
30.00
40.00
Months
104
50.00
60.00
70.00
Appendix 2 Time series and simple linear regression model Time series is defined as data values that are collected, recorded, or observed over successive increments in time. When time series variable is recorded and observed, it is often difficult or impossible to visualize its various components. Components of time series include: The secular Trend ±the long-term component that represents the growth or decline in the series over extended period of time. The basic forces responsible for the trend of series are population growth, price inflation, technological change and productivity increases. The cyclical component- is the wave like fluctuation around the trend. The seasonal component ± is pattern of change in quarterly or monthly data that repeats itself from year to year. The irregular component- is a measure of the remaining variability of the time series after the other components have been removed caused by unanticipated and nonrecurring factors. The time series decomposition model can either be multiplicative or additive, that is Y=TxCxSxI or Y = T+C+S+I respectively In this study the component of interest is the secular trend. A typical series either reveals a straight ±line trend or can use a straight line as a close approximation to a slight curvilinear trend. The least squares procedure is used to find the straight line that best fits the observed time series data. The equation below describes this linear trend function. This is the same procedure used to minimize SSE =
¦
2
(Y-Yr) and find the estimated regression equation
in regression analysis. Yr=bo+b1X Where
Yt=Forecast trend value of Y ( or dependent variable ) for selected coded time period X b0= Constant or value of Y when X is coded as zero b1= Slope of the trend line ( rate of change ) X= Value of time selected (or independent variable in regression analysis) Scatter diagrams - for regression analysis are constructed with values of the dependent variable Y on the vertical axis and values of the independent variables on the horizontal axis .The scatter diagram enables us to observe the data graphically and to draw preliminary conclusions bout possible relationship between variables . Correlation coefficient ± r - This is a value between ±1 and +1 that indicates the strength of the linear relationship between two quantitative variables. 2
Coefficient of simple determination ± r ± measures the percentage of variability in the independent variable, Y, that can be explained by the predictor variable, X.
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Hypothesis Testing In Regression Analysis 2
Apart from r that shows the ability of X to explain the variability in Y in the form of an easily interpreted percentage, the t value is a key statistic used to test the null hypothesis that the slope of a regression equation in the population is zero. If a regression equation has a slope of zero, change in X does not affect Y. In other words X and Y have no correlation in the population. The symbol for the slope in the population regression equation is E 1. The null hypothesis and two-tailed alternative hypothesis for testing the slope are: H0 :
E 1=0
H1 :
E 1 z0
A two-tailed alternative is used when the goal of the analyst is to determine if the slope of the regression equation is zero or not. A One-tailed alternative is used when the analyst is testing to determine whether the slope is positive (H1 : E 1 ² 0 ) or negative ( H1 : E 1 ¢ 0 ) . As with the correlation coefficient test, it can be shown that if the null hypothesis (the population slope is equal to zero is true, then the appropriate sampling distribution for the test is the t distribution with (n-2) degrees of freedom. Two degrees of freedom are lost because two population parameters ( E 0 and E 1) are estimated using sample statistics (b0 and b1 ) .
The value for the estimated standard error of b1 (Sb) is computed as follows:
The gradient Sb =
¦ x2
Se (¦ x) 2 n
Where Se =
2 ¦ y a¦ y b¦ xy n2
where Se is the standard error of the regression
For the gradient, the test statistic is t=
b1 E S b
The calculated value of t is compared with the Tabulated value of n-2 degrees of freedom at a chosen level of significance.
E 1=0 is generally used because if E is found not to be significantly different from 0 then Y=a and since the line of best fit passes through X and Y it will be horizontal at the value of Y.
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The hypotheses tests for significance of correlations, F test and t test provide the same results for simple linear regression analysis for one dependent variable and one independent variable. Thus only one of them is enough for such tests of significance. But with more than one independent variable only the F test can be used for tests of significance.
Cautions about the interpretation of significance tests Rejecting the null hypothesis H0 : E 1=0 and concluding that the relationship between x and y is significant does not enable us to conclude that cause and effect relationship is present between x and y .Concluding cause and effect relationship is only warranted if the analyst has some type of theoretical justification that the relationship is in fact causal. So the appropriateness of such cause and effect relationship conclusion is left to supporting theoretical justification and to good judgment on the part of the analyst. In addition, just because we are able to reject H0 : E 1=0 and demonstrate statistical significance does not enable us to conclude that the relationship between x and y is linear .We can state only that x and y are related and that a linear relationship explains a significant portion of the variability in y over the range of values for x observed in the sample. Given a significant relationship, we should feel confident in using the estimated regression equation for predictions corresponding to x values within the range of the x values observed in the sample. For values of x outside the range observed in the simple, we should be more cautious.
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APPENDIX 3 Rating Definitions and Process
source: King (2004)
Long term debt
Short term debt
Investment Grade
High Grade
AAA
A1+
Highest credit quality. The risk factors are negligible, being only slightly more than for risk-free government bonds.
AA+ AA AA-
Very high credit quality. Protection factors are very strong. Adverse changes in business, economic or A1 financial conditions would increase investment risk, although not significantly.
A+ A A-
High credit quality. Protection factors are good. A1However, risk factors are more variable and greater in periods of economic stress.
BBB+ BBB BBB-
Adequate protection factors and considered sufficient for prudent investment. However, there is considerable variability in risk during economic cycles.
Highest certainty of timely payment. Short-term liquidity, including internal operating factors and/or access to alternative sources of funds, is outstanding, and safety is just below that of risk-free treasury bills. Very high certainty of timely payment. Liquidity factors are excellent and supported by good fundamental protection factors. Risk factors are minor High certainty of timely payment. Liquidity factors are strong and supported by good fundamental protection factors. Risk factors are very small.
Good Grade A2
Non-investment Grade
Good certainty of timely payment. Liquidity factors and company fundamentals are sound. Although ongoing funding needs may enlarge total financing requirements, access to capital markets is good. Risk factors are small.
BB+ BB BB-
Below investment grade but capacity for timely Satisfactory Grade repayment exists. Present or prospective financial protection factors fluctuate according to industry A3 Satisfactory liquidity and other protection factors conditions or company fortunes. Overall quality qualify issues as to investment grade However, risk may move up or down frequently within this category factors are larger and subject to greater variation.
B+ B B-
Below investment grade and possessing risk that obligations will not be met when due. Financial protection factors will fluctuate widely according to economic cycles, industry conditions and/or company fortunes.
Non-investment Grade B
CCC Well below investment grade securities. Considerable uncertainty exists as to timely payment of principal or interest. Protection factors are narrow and risk can be substantial with unfavourable economic/industry conditions, and/or with unfavourable company developments. DD
Speculative investment characteristics. Liquidity is not sufficient to insure against disruption in debt service. Operating factors and market access may be subject to a high degree of variation
Default C
Issuer failed to meet scheduled principal or interest payments.
Defaulted debt obligations. Issuer failed to meet scheduled principal and/or interest payments.
A synopsis of the rating process When rating agencies were first set up, the primary focus of credit analysis was on the default risk of the bond, or the probability that the investor would not receive interest payments and principal repayments as they fall due. Although this is a still important, credit analyst these days also consider the overall economic conditions as well as the chance that an issuer will have its rating changed during the life of the bond. There are differences in credit analysis
108
approach depending on the industry the company is part of. However, irrespective of the LQGXVWU\ EURDGO\ DQDO\VWV XVXDOO\ DGRSW D ³WRS-GRZQ´ DSSURDFK RU D ³ELJ SLFWXUH´ DSSURDFK and concentrate on macro ±issues first before looking at the issuer specific points in detail. 7KHSURFHVVWKHUHIRUHLQYROYHVUHYLHZLQJWKHLVVXHU¶VLQGXVWU\EHIRUHORRNLQJDWLWVILQDQFLDO and balance sheet strength, and finally the legal provisions concerning the bond issue. The rating process is structured to create an accurate, consistent framework for evaluating and ranking risk across various companies, industries and types of debt. The objective is to achieve a reasonable judgement on credit risk, not through a set formula, but rather through careful analysis of the critical strategic issues affecting individual organisations. The main emphasis is to understand the fundamental strategic factors associated with each individual organisation and the industry in which it operates, to evaluate the quality of management, and to identify critical risks to future cash generation. The emphasis is on determining how WKHVHVWUDWHJLFDVSHFWVZLOODIIHFWWKHSUHGLFWDELOLW\RIFDVKJHQHUDWLRQDQGWKHRUJDQLVDWLRQ¶V FDSDFLW\ WR UHVSRQG WR XQFHUWDLQW\ &RPSOHWLQJ D µGHVN WRS¶ DQDO\VLV DV RXWOLQHG LQ LQGXVWU\ analysis and company analysis below, starts the process. Industry analysis .In order to place the subsequent company analysis in context, credit DQDO\VLVSURFHVVIRUDVSHFLILFLVVXHZLOOUHYLHZWKHLVVXHU¶VLQGXVWU\)or example a company that has recorded growth rates of 10% each year may appear to be a quality performer, but not if its industry has been experiencing average growth rates of 30%. Generally industry analysis will review the following issues: x
Compilation of industry financial statistics R
Economic cycle .The business cycle of the industry and its correlation with the overall business cycle is a key indicator. That is, how closely does the industry follow WKH UDWH RI JURZWK RI LWV FRXQWU\¶V *13" 6RPH LQGXVWUies are more resistant to recessions than others. As well as the correlation with macro ±factors, credit analysts UHYLHZ WUDGLWLRQDO ILQDQFLDO LQGLFDWRUV LQ FRQWH[W IRU H[DPSOH WKH LVVXLQJ FRPSDQ\¶V Earnings per Share (EPS) against the growth rate of its industry.
R
Growth prospects 7KLV UHYLHZ LV RI WKH LVVXHU¶V LQGXVWU\ JHQHUDO SURVSHFWV $ company operating within what is considered a high-growth industry is generally deemed to have better credit quality expectations than one operating in a low growth environment. A slow-growth industry has implications for diversification, so that a company deemed to have plans for diversifying when operating in stagnant markets will be marked up.
R
Competition. Intensity of competition within a particular industry is related to that LQGXVWU\¶VVWUXFWXUHDQGKDVLPSOLFDWLRQVIRUSULFLQJIOH[LELOLW\7KHW\SHRIPDUNHWIRU example monopoly, oligopoly, perfect competition, and so on, also influence pricing policy and relative margins. Over capacity often leads to intense price wars leading in turn to financial deterioration in an attempt to gain market share. Competition is now regarded as a global phenomenon and so well rated companies are judged to be able to compete successfully on a global basis whilst concentrating on the highest ± growth regions. An assessment of the competitive position and market share of all the key players is essential.
R
Supply sources. The availability of suppliers in an industry has influences for a FRPSDQ\¶V ILQDQFLDO ZHOO-being. Monopoly sources of supply are considered restrictive and thus have negative implications. A vertically integrated company that is able to supply its own raw materials is less susceptible to economic conditions that may affect suppliers or leave it hostage to price rises.
R
Research and Development. In certain industries like telecommunications, media and information technology, a heavy investment in R&D is essential simply in order to maintain market share.
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R
Level of regulation .The degree of regulation in an industry, its direction and effect on the profitability of a company are relevant in credit analysis .A highly regulated industry such as power generation, production of medicines, telecommunications can have a restrictive influence on company profits. A policy of deregulation is normally considered a positive development in an industry.
R
Labour relations. An industry with a highly unionised labour force or generally tense labour relations is viewed unfavourably compared to one with stable labour relations. The patterns of historical strikes and production days lost to industrial action. The status of labour relations is influential in a highly labour intensive industry than a highly mechanised or automated one.
R
Political climate. Failure to foresee certain political developments can have far ± reaching effects for investors, as recently occurred in Indonesia when that country experienced a change of government; foreign investors lost funds as several local banks went bankrupt.
R
An overview of the industry covering aspects such as the nature of value creation, barriers to entry, regulatory trends, vulnerability to economic cycles and input costs.
R
An assessment of the key strategic features driving the industry.
Company analysis (Financial analysis). The second step is to complete the analysis on the specific company. The traditional approach to Credit Analysis concentrated heavily on financial analysis .The more modern approach involves a review of the industry the company is operating in first, discussed above, before considering financial considerations. The initial requirements focuses on the following: x Ratio Analysis. Audited financial statements for the past five years and management accounts for the year to date are used. Generally ratio analysis is compared to the levels prevalent in the industry, as well as historical values, in an effort to place the analysis in context and compare the company with those in its peer group. The ratios that can be considered include: R
Pre-tax interest cover, the a level of cover for interest charges in current pretax income
R
Fixed interest charge level
R
Leverage, which is commonly defined as the ratio of long-term debt as e percentage of total capitalisation. Level of leverage compared to industry average
R
Nature of debt, whether fixed or floating, short or long term
R
Cashflow, which is the ratio of cashflow as a percentage of total debt. Cashflow is usually defined as net income from continuing operations, plus depreciation and taxes, while debt is taken to be long-term debt.
R
Net assets as a percentage of total debt. The liquidity of assets-meaning the ease with which they can be turned into cash-is taken into account when assessing the net assert ratio.
The rating agencies maintain benchmarks that are used to assign ratings, and these are monitored and if necessary modified to allow for changes in the economic climate. For H[DPSOH 6WDQGDUG 3RRU¶V JXLGHOLQHV IRU SUH-tax interest cover, leverage and cashflow in1997 are shown in the table below. A pre-tax cover of 9.00 for example, is consistent with a double A-rating.
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Credit rating Pretax interest cover Leverage cashflow AAA 17.99 13.2 97.5 AA 9.74 19.7 68.5 A 5.35 33.2 43.8 BBB 2.91 44.8 29.9 Table 3.3: S&P ratio benchmarks,1997, Source :S&P R
Age and condition of plant
R
Working capital
R
Return on Equity. The range of ratios in this category of performance measure include: Return on net assets, Return on sales, Return on Equity, Pre-tax interest cover, EBIT interest cover, Long term debt as % of capitalisation
R
A synopsis of key policy guidelines in respect of credit, liquidity, interest rate and counterparty risk.
R
Various statistical breakdowns of the income and asset base.
x Non-financial factors. These include quality of management including diversity of age, broad breadth of experience and expertise, clear succession plans and non-dependence on key man. Future prospects, plans and budgets. These factors also include exposure to overseas operations. Due diligence and dissemination. Upon completion of the desktop analysis, the rating agency will spend some time with the organisational senior executives. This is in order to clarify any queries which have arisen during the desk top analysis, as well as to discuss other pertinent issues such as the organisation structure, risk management philosophies and procedures, the control environment, financial forecasts and expansions plans, accounting polices and strategic direction. Upon completion of the rating report it will first be forwarded to management for perusal and comment. Once management have reverted, the rating report (duly amended where necessary) will be submitted to the rating panel. Upon accordance of the rating the organisation will be advised accordingly and the report returned to management. Once management authorises to publicly release the rating the rating agency will issue a draft SUHVVUHOHDVHIRUWKHFRPSDQ\¶VSULRUDSSURYDO7KHUDWLQJUHSRUW GXO\HGLWHGWRUHPRYHDQ\ confidential information which is not for public dissemination) will be disseminated to clients. Confidentiality statement. Rating Agencies are acutely aware of the importance of maintaining the strictest levels of confidentiality. In accordance with this, they will not publish or otherwise disclose to third parties any information given to it by the organisation on a confidential basis. Raters also undertake to restrict access to such confidential information exclusively to employees of raters, all of whom have also signed appropriate confidentiality undertakings with the employer, the rater. This undertaking applies to all data that the organisation designates as confidential and which is not available to the raters from other sources. After each rating meeting, the organisation will be sent a draft copy of the rating report. If this draft contains any information that the organisation considers confidential and does not wish to be published, the rater amends the text accordingly.
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action plan, with options, for a robust and sustainable CRA framework (including short-term and medium to long-term milestones). The Terms of Reference for the consultant are: (i) Hold a workshop for all stakeholders to explain key issues surrounding CRAs and their operations and highlight international best practices on CRA establishment and operation based on experience of other countries; (ii) Determine the status of any initiatives taken in the creation of a CRA; (iii) Determine the status of initiatives already taken relative to regulation/ legislation that would provide the framework for a CRA; (iv) Determine if there are any restrictions to prevent the sharing of credit information between banks and between banks and non-banks; (v) Establish key outstanding issues that need to be addressed to enable the commencement of operations of the proposed CRA; (vi) Hold meetings with the BoZ, the Bankers' Association and CRBAfrica to identify pertinent issues; (vii) Review draft service level agreement, Banking and Financial Services Act and other relevant legislation and regulations, draft licensing agreement (if available) and any other key legal documentation; (viii) Meet non-bank potential users of the CRA. General Information on the FIRST Initiative The Financial Sector Reform and Strengthening (FIRST) Initiative is a significant US$53 million multi-donor program, supporting capacity building and policy development projects in the financial sector. FIRST has fully committed its resources for middle-income countries, and therefore, is only accepting project proposals for low-income countries. FIRST provides technical assistance grants for short and medium-term projects in the areas of financial sector regulation, supervision and development. FIRST supports activities and interventions mainly in the public sector, principally by providing technical assistance grants to policy makers and regulatory bodies. It also supports private sector activities when organised through institutions, such as stock exchanges, selfregulatory organizations and industry associations. In most cases, FIRST cannot provide support to non-regulatory institutions, such as individual banks, insurance companies or brokerage houses. FIRST's primary activity is the provision of advisors (independent consultants, consulting firms or employees of official agencies). FIRST also funds in-country and out-of-country training, secondment of experts, peer group workshops and other forms of peer support including the supply of relevant third country and World Bank and IMF materials. Usually, non-technical assistance activity must be linked to an identifiable reform program, whethersupported by FIRST, another donor, or a recipient country. FIRST supports the dissemination of information on best practices and useful tools related to financial sector reform and development in low-income and middle-income countries. An Information Exchange delivers information on both current and completed financial sector development assistance projects. FIRST's live service, AskFIRST, responds to programrelated queries and delivers customized information on financial sector reform and development activities.
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Appendix 5
GLOSSARY OF TERMS
Credit. A contractual agreement in which a borrower receives something of value now and agrees to repay the lender at some later date. Credit. Money loaned by any lender with an agreement to pay back the principal and interest at specified dates over a period of time. Money an individual borrows from any lender (e.g. Bank) Credit. In the trading terms between companies, credit is the value of goods which one company will supply to another company or person before payment is necessary (e.g. in Hire purchase, utility companies, credit card companies etc). Credit Reference Bureau $ FUHGLW %XUHDX LV D GDWD EDQNUHSRVLWRU\ RI LQGLYLGXDOV¶ DQG FRPSDQLHV¶ GHPRJUDSKLF UHFRUGV JRRG DQG EDG SD\ment history of various types of credit obligations, both open and paid. This information is made available at a fee to companies offering credit. Credit bureaus do not decide on who gets credit, neither do they grant credit. But they research the credit history of borrowers so that creditors can make decisions about granting of loans. Credit Rating Agencies 7KHVHDUHUHFRJQL]HGUDWLQJDJHQFLHVVXFKDV0RRG\¶V6WDQGDUG 3RRU¶V RU )LWFK WKDW JLYH DQ RIILFLDO RSLQLRQ RQ WKH FUHGLWZRUWKLQHVV LH WKH DEility of an entity to make payments of interest and principal on debt obligations on time based on financial status and credit history. Credit Rating. The degree of credit worthiness assigned to a person based on credit history and financial status. A rating used by banks, insurance companies, mortgage companies and other financial institutions making loans which they use to judge an individual or company's credit worthiness.A credit rating is an opinion issued by recognized rating agencies such as Mood\¶V6WDQGDUGDQG3RRU¶VRU)LWFKRIWKHFUHGLWZRUWKLQHVVLHWKHDELOLW\RIDQHQWLW\WR make payments of interest and principal on debt obligations on time. Credit Risk ± represents the risk of loss as a result of counterparty to a transaction failing to honour their obligations in terms of the transaction. Credit risk thus relates to the possibility of a loss in profit, cash flow or value of an obligation - loan, bond or promise to pay - from the failure of a borrower or obligor to pay what is owed in full and on time. The degree of risk LQYROYHGLVUHIOHFWHGLQWKHERUURZHU¶VFUHGLWUDWLQJ
114
Credit Default. Any payment of principal or interest on a loan or any credit facility that is in arrears. Credit Card. A plastic payment card that allows the owner to obtain goods and services without the requirement to pay cash and on credit terms. Transactions during a month are totalled
and presented to the card holder for settlement on a monthly basis. Alternatively a
percentage of the outstanding amount can be paid and the balance extended to the next month and so on. In addition to obtaining goods, credit cards can be used to obtain cash (used as an Automated Teller Machine Card -ATM). Credit Information Sharing (credit reporting or credit checks). These are checks made when a person tries to borrow money or purchase goods on hire purchase, and are used to determine the risk of lending one money. They will examine your credit history and check for payment defaults and what you owe to other financial organisations. A credit agency is often used. Credit History. A record of how a person has borrowed and repaid debt. If you have a history of bad debts, county court judgements or bankruptcy to your name, you may not be eligible for credit .To help ensure you are a good credit risk, a lender may require references from your existing lender, bank or landlord. In addition to this, many lenders will make use of the services of one of the credit agencies. These offer a credit inquiry or a full credit application, which show details of any existing credit arrangements or county court judgements against you. Credit Quality. A measure of the likelihood of default. Rating agencies assign letter designations such as AAA, AA, and so forth. Credit Period.
The time frame for which the lender agrees to provide you with credit.
Credit Averse. When a borrower has a poor credit history, has previously been declared bankrupt or has outstanding County Court Judgements, they are often described as credit averse. Persons with averse credit ratings often have to pay higher interest rates on a loan. Gross Domestic Product (GDP). The total market value of all final goods and services produced in a country in a given year, equal to total consumer, investment and government spending, plus the value of exports, minus the value of imports.
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