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Loan default and the efficacy of the screening mechanism: The case of the Development Bank in Guyana
Hunte, Cyril Kenrick, Ph.D.
The Ohio State University, 1993
UMI 300 N. ZeebRd. Ann Arbor, MI 48106
Loan Default and the Efficacy of the Screening Mechanism: The Case of the Development Bank in Guyana
Dissertation
Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University
by Cyril Kenrick Hunte, M.S., M A.
THE OHIO STATE UNIVERSITY 1993
Dissertation Committee: Approved by:
Claudio Gonzalez-Vega, Ph.D. Richard L. Meyer, Ph.D. Cameron S. Thraen, Ph.D. Professor Claudio Gonzalez-Vega Adviser Department of Agricultural Economics and Rural Sociology Copyright by
Cyril Kenrick Hunte
1993 Dedication
In memory of my grandparents:
Susan Smith and Berisford Hunte ACKNOWLEDGEMENT
It is with a deep sense of gratitude and appreciation that I acknowledge the untiring efforts of Professor Claudio Gonzalez-Vega. As my principal adviser,
Professor Gonzalez-Vega has been instrumental in helping me to understand the many issues in rural financial markets, using both theoretical models and practical experience, gained from his research assignments in many developing countries. His guidance over the last five years will long be remembered. My sincere thanks also to the other members of my committee, Professor Cameron S. Thraen and Professor Richard L.
Meyer. Their incisive comments and timely suggestions made this research better than it would have been.
I am deeply grateful for the financial support I received from three organizations which funded my graduate program. I acknowledge the financial support from the Rural
Finance Program, Department of Agricultural Economics and Rural Sociology, The Ohio
State University and the United States Agency for International Development (USAID).
I also wish to acknowledge the scholarship provided by the World Bank/Government of
Japan Scholarship Program, which allowed me to begin my graduate work in September,
1988. Without these generous sources, graduate school would have remained a desire.
Gratitude is hereby expressed to Dr. Nelson Aguilera, who assisted me in the early stages of my research. Thanks are also extended to my other colleagues and
iii friends in the Agricultural Economics program at the Ohio State University, especially
Mr. Neal Blue, Mr. Saief Elhagmusa, and Ms.Susana Sanchez. Soon it will be your turn.
My formal introduction to the problems in rural financial markets began when I
attended one of the seminars that discussed some of the material included in Undermining
Rural Development with Cheap Credit, edited by Adams, et al, 1984. This seminar was
held in Guyana in 1980. In that same year, too, I was employed at the Guyana
Cooperative Agricultural and Industrial Development Bank (GAIBANK), and
subsequently, I attended a Rural Credit Course organized by the Economic Development
Institute (E.D.I), The World Bank. Visits to development financial institutions in the
Caribbean also formed part of my early enlightenment. I want to say thanks to the
numerous borrowers whom I met over the years and a special thanks to the management
and staff of GAIBANK, including my many friends, who recently assisted me with the
collection of the data for this dissertation.
To my many relatives, a special word of appreciation for their constant
encouragement and support. I especially thank my parents, Janet and my late father,
Cyril Berisford Hunte, who provided a focus, stability, a strong work ethic, and a
discerning quality to separate the things that matter. To my wife, Jackie, I sincerely say
thanks for being there at all times, no matter the circumstances. Her willingness to be
involved, and her commitment to endure with me, made my journey that much easier.
Finally, to the Grand Architect of the Universe, without whom nothing is accomplished, and nothing is achieved, I say a special word of thanks and praise. VITA
December 13, 1951...... Bom: Georgetown, Guyana
1970-1975 ...... Guyana Public Service: Clerk, Ministry of Agriculture, Forestry, Public Service Ministry
1979 ...... B.Sc., Agricultural Economics Utah State University, Logan, Utah, U.S.A.
1 9 8 1 ...... M.Sc., Agricultural Economics Utah State University, Logan, Utah
1990 ...... M.A., Economics The Ohio State University, Columbus, Ohio
1980-1984 ...... Manager, Research and Development; Manager, Credit Department Guyana Cooperative Agricultural and Industrial Development Bank (GAIBANK)
1984-1988 ...... Lecturer, Faculty of Agriculture University of Guyana
1984-1985 ...... Deputy General Manager, GAIBANK.
1985 to present ...... General Manager, GAIBANK.
1991 to present ...... Graduate Research Associate Department of Agricultural Economics The Ohio State University
v FIELDS OF STUDY
Major Field: Agricultural Economics and Rural Sociology Financial Markets in Developing Countries.
Studies in:
Rural Financial Markets and Development Economics
Claudio Gonzalez-Vega Douglas Graham
Economic Theory and Banking.
Stephen McCafferty Robert Driskill Patricia Reagan Edward Kane
Applied Demand Analysis and Welfare Economics and Agricultural Policy.
Wen Chem Alan Randall Luther Tweeten
Quantitative Methods, Statistics and Econometrics.
Mario Miranda Cameron Thraen Nelson Mark TABLE OF CONTENTS
DEDICATION ...... ii
ACKNOWLEDGEMENTS...... iii
VITA ...... iv
LIST OF TABLES...... x
LIST OF FIGURES...... xiii
Chapter Page
I. INTRODUCTION...... 1
1.01 DEFAULT AT DEVELOPMENT BANKS ...... 1 1.02 STATEMENT OF THE PROBLEM...... 2 1.03 HYPOTHESES ...... 7 1.04 ORGANIZATION ...... 9
II. REVIEW OF THE LITERATURE ON CREDIT RATIONING AND LOAN DEFAULT ...... 10
2.01 CREDIT RATIONING...... 10 2.02 LOAN DEFAULT ...... 16 2.03 CONCLUDING REMARKS ...... 19
III. THE FINANCIAL SECTOR IN GUYANA...... 22
3.01 INTRODUCTION...... 22 3.02 MACROECONOMIC ENVIRONMENT...... 23 3.03 STRUCTURAL ADJUSTMENT PROGRAM...... 25 3.04 THE STRUCTURE OF FINANCIAL M ARKETS...... 26 3.05 PERFORMANCE OF THE FINANCIAL SEC TO R...... 33 3.06 POLICIES AND PERFORMANCE OF GAIBANK ...... 37 3.07 GAIBANK INTEREST RATE POLICY ...... 38 3.08 OBJECTIVES OF GAIBANK AND ELIGIBILITY ...... 40 3.09 APPLICATIONS PROCESSED DURING 1987-1991 ...... 41 3.10 DISBURSEMENT LEVELS, 1987-1991 ...... 43 3.11 DECENTRALIZATION OF THE GAIBANK CREDIT SYSTEM 45 3.12 LOAN PORTFOLIO MARKET SH A RES...... 48 3.13 STAFF TURNOVER, 1987-1991 ...... 49 3.14 POTENTIAL FOR DEPOSIT MOBILIZATION...... 50 3.15 GAIBANK PROFIT AND LOSS STATEMENT...... 51 3.16 LENDING CAPACITY AND THE IMPACT OF ARREARS AND DEFAULT...... 52 3.17 CONCLUDING REMARKS ...... 54
IV MODEL OF LOAN SCREENING AND DEFAULT...... 56 4.01 INTRODUCTION...... 56 4.02 MODEL ASSUMPTIONS...... 59 4.03 THE RATIONING R A T IO ...... 68 4.04 SPECIFYING THE EMPIRICAL MODEL ...... 71 4.05 EFFICACY OF THE SCREENING MECHANISM ...... 72 4.06 CHOICE OF THE INDEPENDENT VARIABLES IN THE SCREENING MECHANISM ...... 76 4.07 CREDITWORTHY AND BORROWERS IN DEFAULT ...... 81 4.08 CONCLUDING REMARKS ...... 82
V. SURVEY DESIGN, METHODOLOGY AND RESULTS ...... 84
5.01 INTRODUCTION ...... 84 5.02 SAMPLE DESIGN AND SURVEY INSTRUMENT...... 85 5.03 SURVEY RESULTS...... 87 5.04 DISTRIBUTION BY TYPE OF BORROWER ...... 91 5.05 DISTRIBUTION BY TYPE OF INVESTMENT...... 92 5.06 DISTRIBUTION BY REGION...... 93 5.07 DISTRIBUTION BY SOURCES OF FU N D S ...... 94 5.08 DISTRIBUTION BY TERM STRUCTURE ...... 95 5.09 DISTRIBUTION BY LOAN SIZE ...... 96 5.10 DISTRIBUTION OF LOANS IN THE SAMPLE, 1987-1991 . . . 97 5.11 LAND TENURE AND ITS IMPLICATIONS FOR LENDING . . 98 5.12 COLLATERAL SECURITY BY TYPES OF INSTRUMENT . . . 100 5.13 BORROWER BALANCE SHEET, 1987-1991 ...... 103 5.14 ANALYSIS OF THE FINANCIAL TECHNOLOGY ...... 104 5.16 LOAN DEFAULT AND REPAYMENT STATUS...... 109
viii 5.17 REPAYMENT BY TYPE OF INVESTMENT ...... I ll 5.18 REPAYMENT STATUS BY TYPE OF BORROWER ...... 113 5.19 REPAYMENT STATUS BY LOAN S IZ E...... 114 5.20 REPAYMENT STATUS BY SOURCE OF FU N D S...... 115 5.21 REPAYMENT STATUS BY YEAR...... 116 5.22 CONCLUDING REMARKS ...... 117
VI EMPIRICAL ANALYSIS...... 119
6.01 INTRODUCTION...... 119 6.02 IMPLICIT SCREENING AND RATIONING...... 120 6.03 ROLE OF THE SIGNIFICANT PARAMETERS IN SCREENING AND RATIONING...... 120 6.04 THE ROLE OF SIGNIFICANT PARAMETERS ...... 123 6.05 EFFECTIVENESS OF THE SCREENING AND RATIONING DEV ICE...... 125 6.06 RATIONALE FOR USING THE TOBIT ESTIMATOR...... 126 6.07 LOGIT ESTIMATOR ...... 131 6.08 EXPLANATORY VARIABLES ...... 133 6.09 RESULTS FROM ESTIMATIONS ...... 134 6.10 RESULTS FROM TESTS OF HYPOTHESES ...... 135 6.11 SCREENING AND ITS EFFICACY ...... 141 6.12 CONCLUDING REMARKS ...... 142
VII SUMMARY, CONCLUSIONS AND RECOMMENDATIONS ...... 144
7.01 SUMMARY ...... 144 7.02 THEORETICAL RESULTS ...... 146 7.03 EMPIRICAL EVIDENCE AND POLICY IMPLICATIONS .... 147 7.04 DIRECTION FOR FUTURE RESEARCH ...... 149
BIBLIOGRAPHY ...... 151
APPENDIX A ECONOMETRIC RESULTS...... 158
APPENDIX B SURVEY INSTRUMENT...... 162
APPENDIX C MAP OF GUYANA AND LOCATION OF GAIBANK OFFICES . . . 171
ix LIST OF TABLES
TABLE PAGE NO.
1 AVERAGE REAL GDP RATES OF GROWTH, 1961-1991...... 23
2 GUYANA: DOMESTIC CREDIT, MONETARY AGGREGATES, INFLATION, INTEREST AND EXCHANGE RATES, 1961-SEPTEMBER, 1991 ...... 25
3 RATE OF CHANGE OF CONSUMER PRICE INDEX (CPI) AND NOMINAL INTEREST RATES, 1989-1991...... 39
4 NUMBER OF APPLICATIONS RECEIVED BY GAIBANK DURING THE 1987-1991 PERIOD ...... 42
5 GAIBANK: VALUE OF DISBURSEMENT BY ECONOMIC ACTIVITY REAL TERMS (1985=100) G$M, 1987-1991 ...... 44
6 LENDING AUTHORITY AND THE NUMBER OF LOANS PROCESSED FOR VARIOUS CATEGORIES OF MANAGERS AND THE BOARD OF DIRECTORS IN THE SAMPLE, 1987-1991 ...... 47
7 LOAN PORTFOLIO MARKET SHARES BY GAIBANK AND COMMERCIAL BANKS, 1987-1990 ...... 48
8 LOAN PORTFOLIO INFORMATION AND STAFF LEVELS: 1987-1991 ...... 49
9 FINANCIAL INTERMEDIATION BY GAIBANK BORROWERS, G$M 1987-1991 ...... 51
10 GAIBANK INCOME AND EXPENDITURE STATEMENTS: (1987-1991) G$M ILLION ...... 52
11 AGING OF THE LOAN PORTFOLIO (Principal): G$M 1987-1990 ...... 53
x 12 DIAGNOSTIC MATRIX FOR EVALUATING THE SCREENING AND SCREENING AND RATIONING MECHANISM ...... 74
13 EXPECTED SIGNS IN THE MODEL...... 78
14 SUMMARY OF THE DATA IN THE SURVEY (5 PERCENT SAMPLE)...... 87
15 NUMBER OF APPLICATIONS RECEIVED, APPROVED AND REJECTED BY REGION IN THE SAMPLE, 1987-1991 ...... 89
16 NUMBER, AMOUNT AND AVERAGE LOAN SIZE BY TYPE OF BORROWER FOR THE SAMPLE OF LOANS DISBURSED, 1987-1991 ...... 91
17 NUMBER, AMOUNT AND AVERAGE LOAN SIZE BY TYPE OF INVESTMENT FOR THE SAMPLE OF LOANS DISBURSED 1987-1991 ...... 93
18 NUMBER, AMOUNT AND AVERAGE LOAN SIZE BY REGIONS FOR THE SAMPLE OF LOANS DISBURSED, 1987-1991 ...... 94
19 NUMBER AND AMOUNT AVERAGE LOAN SIZE BY SOURCE OF FUNDS...... 95
20 NUMBER, AMOUNT AND AVERAGE LOAN SIZE BY TERM STRUCTURE FOR SAMPLE DISBURSED, 1987-1991 ...... 96
21 NUMBER, AMOUNT AND AVERAGE LOAN SIZE BY LOAN SIZE CATEGORY, 1987-1991 ...... 97
22 NUMBER, AMOUNT AND AVERAGE LOAN SIZE BY YEARS, 1987-1991 ...... 98
23 DISTRIBUTION BY LAND CATEGORIES AND ACRES: 1987-1991 ...... 100
24 BALANCE SHEET FOR A BORROWER, USING THE AVERAGE VALUES REPORTED IN THE LOAN APPLICATION FORM G$1987-1991 ...... 103
xi 25 CREDIT EXPERIENCE, GRACE PERIOD, PROCESSING TIME, COLLATERAL TO LOAN DEMAND(SL), LOAN APPROVED TO LOAN DEMAND (RR), AND EQUITY (G$) BY REGION, SOURCE OF FUNDS, AND INVESTMENT TYPES (AVERAGE), 1987-1991 ...... 105
26 REPAYMENT STATUS OF THE NUMBER OF LOANS IN THE SAMPLE DISBURSED BY REGION, 1987-1991 ...... 110
27 REPAYMENT STATUS OF THE LOANS IN THE SAMPLE DISBURSED BY REGION, G$M, 1987-1991 ...... 110
28 REPAYMENT STATUS BY LOANS IN THE SAMPLE BY NUMBER AND AMOUNT DISBURSED BY TYPE OF INVESTMENT, 1987-1991 ...... 112
29 REPAYMENT STATUS BY LOANS IN THE SAMPLE BY NUMBER DISBURSED BY TYPE OF INVESTMENT, 1987-1991 ...... 112
30 REPAYMENT STATUS OF LOANS IN THE SAMPLE DISBURSED BY TYPE OF BORROWER, 1987-1991 ...... 114
31 REPAYMENT STATUS OF LOANS IN THE SAMPLE DISBURSED BY LOAN RANGE, 1987-1991 ...... 115
32 REPAYMENT STATUS OF LOANS IN THE SAMPLE DISBURSED BY SOURCE OF FUNDS, 1987-1991 ...... 116
33 REPAYMENT STATUS OF LOANS IN THE SAMPLE DISBURSED BY YEARS, 1987-1991 ...... 117
34 VARIABLES USED IN THE ESTIMATION...... 133
35 REGRESSION COEFFICIENTS AND ASYMPTOTIC t RATIOS 136
36 RESULTS FROM DIAGNOSTIC TEST ...... 143
xii LIST OF FIGURES
FIGURE PACT NO.
1 LENDING TECHNOLOGY...... 6
2 CREDIT RATIONING BY BORROWER...... 61
3 THE IMPACT OF MARGINAL COST ON RATIONING ...... 67
4 IMPACT OF COLLATERAL ON RATIONING ...... 68
5 THE IMPACT OF DEFAULT RISK ON RATIONING...... 68
6 SAMPLE DISTRIBUTION AND CREDIT RATIONING...... 90 CHAPTER I
INTRODUCTION
1.01 DEFAULT AT DEVELOPMENT BANKS
The development community, including lending agencies, policymakers, and researchers, has been increasingly concerned with the disappointing results of rural credit programs.1 The two main objectives of these programs, to improve rural income distribution and to increase agricultural output, have not been achieved. Moreover, the specialized development financial institutions (DFIs) created to manage these programs have been plagued by high arrears and loan default problems that have led to the demise of many of them through insolvency. These results have further slowed down the process of financial deepening in the rural areas of developing countries (McKinnon
1973; Shaw 1973).
Recommended policy reforms have included the deregulation of interest rates, the encouragement of deposit mobilization, the adoption of cost-reducing technologies, and institution building. These have all been aimed at generating a wide range of high-quality
lFor a review of these problems see Adams and Graham, 1981; Adams et al 1984; Braverman and Guasch 1986. financial services, long-term commitments among savers, lenders, and borrowers, and the fruition of viable financial institutions.2
While these reforms can foster more efficient rural financial markets (RFMs), this dissertation claims that these reforms would be incomplete, unless more explicit consideration is given to the widespread incidence of loan default.3
1.02 STATEMENT OF THE PROBLEM
It is important to analyze loan default problems as a separate issue in financial markets, because of their profound effects on the viability of financial institutions. In particular, default problems destroy lending capacity, as the flow of repayment declines, transforming lenders into welfare agencies, instead of viable financial institutions.
Moreover, default problems incorrectly penalize creditworthy borrowers, if the financial technology is not sophisticated enough to separate high-risk applicants from low-risk borrowers. As a result, interest rates increase for all borrowers, to cover losses from default. Loan delinquency may also deny new applicants access to credit, as the bank’s cash-flow management problems augment in direct proportion to increasing default problems.
Donald (1976) suggested that loan default may be the result either of the inability or the unwillingness of borrowers to repay their loans. Inability to repay may be due to
2For a review of these recommendations see Gonzalez-Vega, 1986.
3Several researchers have investigated loan default, including Baker and Dia (1987), Mortensen et al (1988), Turvey and Brown (1990). Recently, Aguilera (1990) provided a theoretical framework for its analysis. adverse exogenous conditions, such as unexpected floods and pests, or to structural problems, such as poor marketing, land tenure arrangements, transportation, and extension services (Von Pischke, 1976). Unwillingness to repay refers to political economy dimensions, where some borrowers are promised debt forgiveness by politicians for a vote cast in the right direction (Harris, 1983; Khalily and Meyer, 1992); or it may result from dishonest borrowers who do not repay, unless it is financially advantageous to them (Jaffee and Russell, 1976; Christen, 1984; Braverman and Guasch,
1986).
Recently, Aguilera and Gonzalez-Vega (1990) have shown that loan targeting can also cause default, as the ability of the lender to reject high-risk borrowers is constrained by the regulatory environment and non-enforceable loan contracts. Finally, loan default may result when the lender employs a flawed screening mechanism that fails to reject a sufficiently large number of borrowers who would not pay.
Lenders who are confronted by default problems in an environment of imperfect information and binding interest rate ceilings may seek to protect their capital resources by rationing credit through a reduction in the number of loans to new borrowers, favoring borrowers who have the most collateral, or making shorter-term loans, which imply lower price and yield risks (Bourne and Graham, 1984). They may also make some conditions in loan contracts more restrictive, such as shorter grace periods, or they may impose high transaction costs, especially on small borrowers, that may cause them to leave the market (Bester, 1985). 4 Bourne and Graham (1984) have suggested that reducing the high levels of loan default at DFIs can be achieved by lenders using more efficient data systems, to provide timely management information on lending costs, arrears rates and delinquency ratios for loans in the portfolio. This approach to analyzing the loan default problems in DFIs has become a central focus of recent research by the Agricultural Finance Program at The
Ohio State University.
In particular, beginning with a conceptual framework and methodology for evaluating the viability of development banks (Gonzalez-Vega, 1990), empirical work on default was completed at one agricultural development bank, the Banco Agricola de la
Republica Dominicana, by several researchers, including Graham, Poyo, and Aguilera
(1990) and Aguilera and Gonzalez-Vega (1990). More specifically, the dissertation by
Aguilera (1990) provided a theoretical model for analyzing equilibrium credit rationing and loan default in formal rural credit markets and also provided an empirical analysis of the effects of several regulatory variables on the performance of a DFI, using individualized information from the application files and accounting records of borrowers at the Banco Agricola de la Republica Dominicana.
Recognizing that loan default may also result from a flawed financial technology that is endogenous to DFIs, this dissertation pursues a similar line of research to that completed at the Banco Agricola de la Republica Dominicana, by undertaking two investigations:
(a) To examine the technology utilized by development banks to screen loan
applications and ration credit to borrowers, in the expectation that they can reduce loan default, while interest rates have a neutral role as a screening device.
Interest rates are considered neutral because in most rural credit projects they are
fixed by the regulatory environment and usually do not change to reflect
increasing risks or inflation. They are kept at artificially low levels with only a
limited role, if any, as a screening device.
(b) To evaluate the efficacy of the screening technology, as borrowers conform or
deviate from their contractual obligations.
These two investigations are important, because deviations from contractual obligations and flaws in the financial technology lead to default, transforming lenders into welfare agencies. Furthermore, under the assumption that expected borrower repayments form the basis for screening and rationing decisions, flaws in the financial technology that lead to default may emanate from the different interpretations of the Board of
Directors’ guidelines for lending by the management, or from actual borrower responses.
In this sense, default results from information and monitoring problems between the principal (Board of Directors) and the agents (management/employees), and between the principal (DFI) and agents (borrowers).
Figure 1 shows the applicant/borrower requesting loans from the lender. This request is processed through the financial technology, using the information provided by the applicant and guidelines of the Board of Directors. The lender (management and perhaps the Board of Directors) makes a decision to approve or to reject the loan request.
If the loan request is rejected, the applicant is informed and the lender keeps the 6 information. If the loan request is approved, the applicant is informed, a loan contract is prepared and disbursement of funds, supplied by donor lines of credit, is made.
After disbursement, the borrower decides whether to comply with the contract or not, and this response is observed in loan repayments. If the borrower decides to repay, the repayment of the loan can occur with or without arrears. If the loan is repaid without arrears, this outcome identifies a creditworthy borrower that is a valuable asset of the lender. If the loan is repaid, but with arrears, the lender experiences cash flow and liquidity management problems, with high opportunity costs. If the borrower decides to be non-creditworthy, the viability of the bank would be threatened, as loan default increases. This outcome raises an important question about the effectiveness of the financial technology, indicating that, perhaps, it might be flawed.
ACCESS TO CREDIT AND BLACK-BOX: FINANCIAL SERVICES: FINANCIAL APPLICANT/BORROWER TECHNOLOGY INFORMATION REJECTAPPROVAL
SAVING AND INVESTMENT SOURCE OF FUNDS LOAN CONTRACT
BORROWER RESPONSES
NON-CREDITWORTHY: CREDITWORTHY: WITHOUT DEFAULT REPAY ARREARS
WITH ARREARS VIABILITY OF DFI
FIGURE 1: LENDING TECHNOLOGY 7 1.03 HYPOTHESES
The general hypothesis of this dissertation is that the processing of borrower information through the screening mechanism influences loan contract terms, loan default, and the repayment behavior of borrowers. Thus, the purpose of this dissertation is to show that the parameters defining these relationships are non-zero. Furthermore, it is hypothesized that if the screening mechanism is sufficiently flawed, then loan default will keep rising, potentially causing the DFI to become insolvent.
Several specific hypotheses are also tested. As the number of previously approved loans for a borrower increases, the intensity of credit rationing will be less for that borrower, as compared to the credit rationing intensity for a new borrower.4 This result is based on the notion that the lender perceives the borrower with a long credit history to be a low-risk investment and is consistent with the hypothesis of Bourne and
Graham, 1984. At the same time, repayments are likely to be higher for a borrower with a long credit history, as compared to a new borrower. This is likely because borrowers with a good credit history would want to protect their credit reputation and their access to additional subsidized loans.
As the grace period on loans and disbursement in special programs increase, loan default will rise. This result reflects the increased risks of the extended term of maturity, and the preference of special programs for targeting loans to high-risk borrowers, thus reducing degrees of freedom in screening.
4 The rationing intensity is the ratio of the loan approved, divided by the loan amount demanded. As the value of collateral accepted as security increases, relative to the loan
amount, default will decrease. This result is consistent with the borrower attempting to
avoid large losses, as the lender shifts a substantial part of the risk to the borrower.
Collateral plays the role of a deductible in inducing borrower behavior compatible with
the lender’s interests.
Default is expected to be higher for loans disbursed in the early 1990s, as
compared to the late 1980s. In the late 1980s, no new funds were available, especially
for agriculture, and new lending was limited by the flow of repayments. Thus, rationing
was more intense, and lower default would be expected. In the 1990s, on the other
hand, when the Inter American Development Bank (IDB), European Investment Bank
(EIB), and Government counterpart funds were being disbursed, less rationing prevailed,
leading to higher default rates.
These and similar hypotheses are important, primarily because if they cannot be
rejected, then the opportunity exists for policy reforms that can lead to an improvement in the performance of lenders, by way of designing better loan contracts and improving the effectiveness of the screening mechanism. In order to test these hypotheses, a model that represents the financial technology of lenders interacting with borrowers is developed. Econometric estimation techniques are employed to determine how a parsimonious list of variables might define the credit screening and rationing behavior of the lender. Further, the use of econometric techniques identifies variables that explain when borrowers default or repay their loans. The efficiency of the screening mechanism may be improved on the basis of these results. 9 Data for the empirical analysis have been obtained from borrowers’ files and
accounting records, using a stratified random sample of 504 observations drawn from
eight branches of the Guyana Cooperative Agricultural and Industrial Development Bank
(GAIBANK) in the Cooperative Republic of Guyana.
1.04 ORGANIZATION
This dissertation consists of seven chapters. The aim of the literature review in
Chapter II is to identify how previous studies have approached the questions of loan
demand, supply, credit rationing, and loan default in rural financial markets. Chapter
III presents a brief survey of the financial system and of financial repression in Guyana, along with information on how GAIBANK fits into that system. The model in chapter
IV provides a theoretically plausible methodology for a description of lender and borrower behavior, as well as a diagnostic procedure designed to evaluate the efficacy of the financial technology. The survey design and methodology used to draw the stratified sample and descriptive statistics are examined in Chapter V. In Chapter VI, the results obtained from the empirical analysis are discussed, while chapter VII presents conclusions and recommendations. CHAPTER n
REVIEW OF THE LITERATURE ON CREDIT RATIONING AND LOAN DEFAULT
2.01 CREDIT RATIONING
Central to the issues in financial market development are concerns about efficient financial intermediation among savers, lenders, and borrowers. For individuals who attempt to maximize utility, there are the concerns of surplus and of deficit households.
On the one side, there are deficit households who seek the credit services of profit- maximizing financial intermediaries. On the other, there are surplus households who seek to invest their funds (savings), because they prefer to defer consumption or forego alternative low-retum investments, if it is profitable for them to do so.
Attractive interest rates and comprehensive financial services may cause surplus households to save in financial form, while the demand for savings by intermediaries may be equal to the supply of savings by surplus households at equilibrium interest rates.
On the other hand, because of risk, uncertainty, information problems, and financial regulations, an equilibrium solution that results in the demand for loans by deficit households being equal to the supply of loans may not be possible at going interest rates.
Instead, credit rationing may prevail.
10 11 Credit rationing is usually defined as an event in which borrowers do not receive all the credit they desire at the prevailing interest rate. Keeton (1979) identified two types of credit rationing:
Type I - Some or all of the applicants receive a loan smaller than they demanded
(loan-size rationing).
Type II - Some applicants secure a loan, while others are rejected, although they
are seemingly undistinguishable from the former (loan-quantity rationing).
More recently, in defining a third type of credit rationing, Bester (1985) claimed that potential borrowers, knowing in advance that the transaction costs imposed on them by the lender will be high, and that the probability of receiving a loan will be low, will choose not to apply for loans (self-select out).
Stiglitz and Weiss (1981) argued that lenders may prefer to ration credit on non-price terms rather than through interest rates, generating excess demands, because high interest rates may attract borrowers with a high probability of default (more risky projects), and might induce creditworthy borrowers to leave the market. To avoid this adverse selection problem, which could cause insolvency for lenders, the screening and credit rationing literature posits that lenders attempt to differentiate among applicants, based on borrower characteristics associated with low default risk.
In the 1950s, credit markets were viewed in the same manner as any other market. Any deviation from equilibrium was perceived as a temporary condition that would correct itself (Samuelson, 1952). However, persistent deviations could not be properly explained by the existing theory and this resulted in the work by Hodgman 12 (1960). He postulated that, given the borrower’s wealth, profit-maximizing lenders
would find it optimal to set loan size limits, since default risk is a function of loan size.
One implication is that no increase in interest rates could compensate the lender for the
increase in default risk for any loan above the optimal size. This model could not
explain why some borrowers, who were informationally identical, received loans and
others did not (Type II rationing). Second, it did not consider that large borrowers have
access to alternative sources of funds and may, therefore, switch to them when interest
rates increase (Blackwell and Santomero, 1982). Finally, it did not consider the
interaction of lender behavior and borrower demand.
Jaffee and Modigliani (1969, 1976) extended Hodgman’s work by introducing
the interaction between lender behavior and borrower demand. The major objection to
their work was the monopoly framework and the exogenous ceilings on interest rates
used for their analysis (Braverman and Gausch, 1986). Most of the early studies
ignored information problems in credit markets, until the literature on default and
rationing was pioneered by Jaffee and Russell (1976), Keeton (1979), Stiglitz and Weiss
(1981), and Bester (1985). Jaffee and Russell explored the behavior of honest and dishonest borrowers in a two-period Fisherian framework, with a single interest rate for the non-homogenous group of borrowers. Essentially, honest borrowers repay their loans, while dishonest borrowers do not, once the penalties for non-payment become lower than the benefits.
Keeton’s idea that credit rationing resulted from incentive problems was a new approach used to generate equilibrium-loan size and loan-quantity rationing. Unlike 13 Stiglitz and Weiss, who presented changes in the underlying risk of the borrowing population based on the variation in interest rates and a fixed loan size, Keeton examined the credit rationing problem in terms of borrowers who changed project risks when the terms of the contract changed.
Central to the Stiglitz-Weiss and Keeton models are the issues of adverse selection and moral hazard. Increases in the rate of interest charged to borrowers will bring high returns from borrowers who repay, but they will also attract high-risk borrowers who shift, in an adverse manner, the risk composition of the lender’s loan portfolio. Thus, rising interest rates may cause default to increase and net returns to lenders to decrease. Additionally, the moral hazard and adverse selection effects may render market-clearing interest rates non-optimal, leading to credit rationing.
Bester, unlike others, demonstrated that credit rationing might not be necessary in equilibrium, if banks can compete by offering different contracts with different interest rates and collateral requirements to a heterogeneous group of borrowers with different levels of loan default risks. Perfect selection will be observed when high-risk borrowers choose loan contracts with high interest rates and low collateral, while low-risk borrowers choose low interest rates and high collateral contracts. Unfortunately, access by small borrowers to marketable securities is almost non-existent in RFMs, as a fair amount of loans are disbursed without security to small farmers who do not have title to their lands. Thus, the self-selection equilibrium in the Bester model is limited
(Braverman and Guasch, 1986). 14 Another type of credit rationing model in which there is a ceiling on interest rates was developed by Gonzalez-Vega (1976). He has shown that as the ceiling on interest rates becomes more restrictive, the size of the loan granted to borrowers who are rationed declines, while the size of the loan granted to borrowers who are not rationed increases. This is the "iron law of interest rate restrictions," which results in a redistribution of the DFI loan portfolio in favor of large wealthy borrowers who are not rationed, while small borrowers are severely rationed.
Taken as a whole, the credit rationing literature is indeed extensive, diverse, and complex. Together with information problems between borrowers and lenders, this has not made the task of modeling credit rationing conditions any easier.5 One of the difficulties of the credit rationing approach has been the lack of empirical evidence to support many of the theoretical claims advanced. This has resulted from the poor tractability of these models (trade-off between complexity and manageability — DeVany
(1984); and from the limited availability of data. The credit rationing literature has also not been informative in relation to the issues of constructing a suitable framework for comparing credit subsidies with other policy instruments (Braverman and Guasch,
1986). Neither have transaction costs been integrated in a single credit model, although they have been independently analyzed by several researchers.
5Some of the information problems have been resolved through the interlinking of credit with other markets. Examples of these are: share-cropping and credit contracts (Bardhan, 1988; Binswanger et al, 1984; Bell and Srinivasan, 1985); and landlords and tenant contracts based on production loans tied to the use of commercial inputs (Singh, 1984; Braverman and Stiglitz, 1986); credit tying as a collateral substitute (Esguerra, 1993). These types of relationships were identified with the adverse selection problem, while the case of moral hazard (principal agent problems) has been examined by Braverman and Srinivasan (1981); Braverman and Stiglitz (1982); Mitra (1982). 15 The overriding problem of the credit rationing literature has been the inability of
researchers to model and empirically test the true revelation of an applicant’s default
probability and the maximum interest rate the borrower is willing to pay. This, if possible, would generate an incentive compatible loan-allocation mechanism that depends only on self-revealed characteristics of loan applicants. Until such a model can be derived, it is only natural that in the presence of information problems, lenders will continue to hedge against default risk by analyzing borrower characteristics (Stiglitz and
Weiss, 1981; DeVany, 1984).
Apart from the credit rationing approach, there are the special concerns of institution building and staff incentives to encourage work patterns that are consistent with financial viability (Braverman and Guasch, 1986; Stiglitz, 1975; Gonzalez-Vega and Chaves, 1992). There is also the important concern of transaction costs (Shahjahan,
1968; Nehman, 1973; Kern, 1980; Saito and Villanueva, 1981; Nyanin, 1982;
Cuevas, 1984; Cuevas and Graham, 1984; Gonzalez-Garita, 1986; Von Pischke, 1990).
High transaction costs is a special form of credit rationing, especially for small farmers.
Savings mobilization and financial deepening (Shaw, 1983; McKinnon, 1973), together with the integration of the informal and formal financial markets (Adams, 1989) and the measurement of subsidies (Ladman, 1984; Bourne 1983; Vogel 1984; Araujo,
Shirota and Meyer 1990) are only some of the key issues that have been examined in this literature. A reproduction of all the arguments that form the basis of this literature will not be undertaken here. For a comprehensive review of these problems in RFMs, the interested reader is referred to Baltensperger (1978, 1980); David and Meyer (1979); 16 Adams, Graham, and Von Pischke (1984); Santomero (1984); World Bank (1989);
Adams and Fitchett (1991); Agency for International Development (1992). The remaining sections of this chapter will highlight the recent research efforts on the loan default problem.
2.02 LOAN DEFAULT
Loan default is a serious problem for DFIs. Yet, not much work has been attempted in this area (Nelson and Letona, 1991). Baker and Dia (1987), who completed a survey of loan default studies, concluded that they were limited and did not adequately represent the system that generated default problems.
Gonzalez-Vega (1976) approached the subject from the standpoint of lenders that add a risk premium to the price of the loan to cover for default losses. This risk premium resulted from the fact that at the time of the loan request, the lender is not certain which borrower will default and which borrower will repay, as actual default losses only become known when scheduled repayments are not made.
Aguilera (1990) proposed a comprehensive model to analyze loan default. This model generated loan demands for borrowers as a function of the price of the loan, the certainty equivalent income from the investment project, the lender’s probability of collecting from defaulters, penalty costs, and the proportion of the loan that is collateralized. For the lender, the model incorporated an information technology using probabilities to predict borrower behavior. Aguilera assumed that the borrower maximized utility subject to constraints, while the lender maximized profits subject to the 17 various probabilities of default associated with different types of borrowers. While it may be argued that this model represents a complete theoretical approach to studying loan default, it is "unfortunately complex" (Aguilera, 1990, p. 31), with a low probability of it ever being tested empirically, because of the information requirements and the difficulties in explicitly specifying an information processing technology.
Furthermore, although appropriate penalties and risk premiums may be added to the price of loans in order to change loan default behavior, it should be recognized that penalties and risk premiums are not collectible when borrowers choose not to repay.
Because of information problems in financial markets, DeVaney (1984) asserts that since there is no mechanism that willa priori induce a true revelation of an applicant’s default probability and the maximum rate that he/she is willing to pay, banks must assess default risks based on the characteristics of borrowers that they observe directly and independently of what the applicant claims. Thus, researchers have been modeling lender and borrower behavior by utilizing non-price information. Generally, these variables may be classified as follows:
Wealth Variables: Value of land, machinery, equipment, livestock, collateral,
collateral substitutes and bank accounts.
Demographic Variables: Age, region, number of children, membership in
organizations, gender, education, type of land tenure, religion.
Economic Indicators: Type of borrower by economic activity, number of hired labor,
technology, savings, risk measured by mean and variance in prices, yields, and
income, input and output prices, farm debt and equity. 18 While theory has suggested the use of these non-price data, as interest rates alone
do not clear credit markets, it has not provided an appropriate framework to incorporate
these variables in proposed models. Thus, cross comparisons among studies have been
difficult. Moreover, since access to information in credit markets seems to be on the
increase, and less use is being made of aggregate data, it is imperative that the non-price
information be incorporated in empirical models in a theoretically plausible manner, so
that better understanding of the credit process and loan default can be achieved.
Recent empirical evidence from Canadian and United States studies, using wealth,
demographic and economic indicators, show lack of liquidity and relatively high financial
leverage as the main reasons why borrowers default on their loans (Turvey and Brown,
1990; Mortensen, Watt and Leistritz, 1988; Miller and La Due, 1989; and Turvey,
1991).6 In a developing country setting, a major conclusion reached is that loan default
is related to targeting in special programs (Aguilera and Gonzalez-Vega, 1990).
Boyes et al (1989) have argued that the traditional view of loan default was too
narrow in emphasizing default probability. They suggested that the goal of credit assessment should be to provide accurate estimates of each applicant’s probability of default and the pay-offs that will be realized in the event of default or repayment. From estimates of these parameters, loan officers can define a loan granting criterion that maximizes expected earnings. The distinguishing feature of this empirical model was that it nested the credit granting decision with the assessment of the default situation,
6 An interesting feature of these credit programs, especially in Canada, was that they were part of a larger government support program for some regions and activities, such as the dairy industry. 19 using binary dependent variables in a censored probit model. However, this model did not provide a theoretical basis for the analysis.
2.03 CONCLUDING REMARKS
This literature review has highlighted the difficulties experienced when modeling borrower and lender behavior and loan default problems in RFMs. Most of the empirical research on this topic has utilized aggregate data obtained from the published records of banks and credit programs. However, a better understanding of the credit process has been limited by a poorly developed theoretical framework and the use of many ad hoc models. One advantage of aggregate data is that it is easily obtained (Goldfeld, 1984), as it protects the privacy and confidentiality of individual savers and lenders, a critical requirement of financial markets. Aggregate data are also useful for standard financial and accounting work, but these data are definitely inadequate when analyzing lender and borrower behavior, using a microeconomic approach in credit markets with incomplete information. Hester and Pierce (1975) have argued that the dynamics of individual portfolios might not be sufficiently captured in aggregate analysis.
An interesting feature of the 1980s and early 1990s has been the increasing availability of disaggregated data through extensive field survey work carried out in many countries in Latin America, Asia, and Africa. This dissertation utilizes information from credit files and loan accounting records of borrowers at a DFI. It endorses the view
(Gonzalez-Vega and Chaves, 1991) that while financial statements presented on an accrued basis provide useful information, they are not sufficient to obtain an accurate 20 assessment of the performance of the DFI and an accurate measurement of delinquency.
For this reason, the approach in this dissertation is to utilize actual disbursement and repayment data, building up the financial information from individual accounting records and borrowers’ loan files.
The credit rationing literature and the information approach developed to analyze credit market performance have facilitated a better understanding of adverse selection and moral hazard problems. Moreover, the creation of new institutions, such as credit rating agencies in the developed countries, and the creation of new instruments, such as credit cards in developed countries and interlinked contracts between landlords and tenants in developing countries, have suggested mechanisms for over-coming these problems. However, empirical evidence has not been forthcoming in support of the credit rationing literature (Santomero, 1984), due to the lack of data and the limited tractability of the proposed models (DeVaney, 1984). Thus, the generation of policy recommendations has been circumscribed. Apart from this, the explosion in the number of explanatory variables identified in order to explain demand, supply, and loan default have underscored the need for an appropriate methodology which incorporates, in a theoretically plausible manner, all the new information these variables bring to RFM research.
The influence of the regulatory environment on RFMs has also compounded the problems. Indeed, the argument by Stiglitz and Weiss (1981), recently restated by
Belongia and Gilbert (1990), that lenders may prefer to ration credit to borrowers on 21 non-price terms, rather than raising interest rates, has extended the opportunity for
policymakers to demphasize the importance of interest rates in financial markets.
As lenders must first be able to predict and reduce default by separating
creditworthy borrowers from non-creditworthy borrowers, future research must
incorporate a theoretically plausible model that sequentially considers the screening and rationing behavior of lenders and the defaulting/repayment behavior of borrowers. A model which takes these relationships into consideration is developed in chapter IV. The next chapter reviews the financial sector in Guyana. CHAPTER HI
THE FINANCIAL SECTOR IN GUYANA
3.01 INTRODUCTION
Financial markets in Guyana are fragmented, due to existing regulation and specialized institutions that do not complete the financial intermediation process. Recent information on the performance of the sector shows that its relative importance with respect to the economy had been declining, due mainly to financial repression and an unstable macroeconomic environment. If the financial sector is expected to make a more positive contribution to the economy, then there is a need for more attractive financial instruments, modem legal and property rights systems, and skilled manpower. In addition, policy changes in the macroeconomic environment, improved administrative procedures, and an efficient information processing technology can also facilitate a positive transformation of this sector.
This chapter is divided into three sections. The first section presents a brief introduction to the macroeconomic environment and its impact on the financial sector.
The second section reviews the structure and performance of the financial sector, while the third section offers an analysis of the performance of the development bank,
GAIBANK.
22 23 3.02 MACROECONOMIC ENVIRONMENT
The real rate of growth of the economy was persistently negative during the 1981-
1990 period, and positive during 1991 (Table 1). The major reasons for the negative rate of growth in the 1980s were related to a series of economic policies. There were price controls in most input and output markets, as well as in the financial sector, where interest rates were fixed for extended periods of time. Coupled with deficit spending and an expansionary monetary policy by the central government, high rates of inflation and negative real rates of interest were pervasive in the economy.
TABLE 1
AVERAGE REAL GDP RATES OF GROWTH, 1961-1991.
YEAR 1961-1971 1971-1980 1981-1986 1987 1988 1989 1990 1991
Rate of 3.0 1.2 -3.3 0.7 -3.6 -6.9 (-)(a) 6.1(a) Growth
SOURCE: Inter-American Development Bank Annual Report 1990; (a) 1991 Budget Speech; (-) Negative, data not provided.
Government subsidies and transfers to unprofitable public sector enterprises, a growing debt burden and an overvalued exchange rate further complicated the macroeconomic environment, by penalizing official exporters and reducing the foreign exchange reserves at the central bank, while the parallel market expanded. Because of high marginal tax rates on personal income and profits, poorly maintained drainage and irrigation systems, especially in the agricultural sector, unreliable and expensive systems for electricity and water supply and telecommunications, and an increasing level of 24 migration of skilled manpower, these events, when combined, resulted in falling real incomes, and declining opportunities for private sector investment. In addition, while there have been high levels of unemployment, shortages of skilled manpower, entrepreneurial talent and modem technology, there has been a large, but under-utilized resource endowment, in agriculture, forestry, hydro-power, fisheries, and mining.
In an economy that is characterized by a high degree of import dependency
(imports/GDP above 90 percent), a relatively small domestic market (less than one million people), a large dependency on primary exports of sugar, rice, and bauxite for its main source of foreign exchange earnings, and a significant demand for imported oil, it is imperative that policymakers pursue stable macroeconomic policies that engender confidence in the financial system. Otherwise, there will be capital flight, as individuals shift resources out of the domestic financial system and into non-productive assets.
Table 2 shows a brief summary of the effects of the unstable macroeconomic environment and its impact on the economy. Some of the salient features during the
1980s were high public sector deficits, rising inflation (60 to 90 percent per year), due to expansionary monetary policies and deficit spending; crowding out of the private sector from the domestic credit market, as government absorbed a large proportion of total domestic credit; diminished foreign exchange reserves at the central bank, which fell to US$4.0 million in 1988, due to overvalued foreign exchange rates and negative real rates of interest, of -28.5 percent in the first half of 1991. 25
TABLE 2
GUYANA: DOMESTIC CREDIT, MONETARY AGGREGATES, INFLATION, INTEREST AND EXCHANGE RATES, 1961-SEPTEMBER, 1991.
1961-70 1971-80 1981-86 1987 1988 1989 1990 1991
Compoiitioa of Do me (tic 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 Credit: Public Sector 38.00 80.00 89.00 88.00 88.00 79.00 70.00 57.00 Privite Sector 62.00 20.00 11.00 12.00 12.00 21.00 30.00 43.00 Total Liquidity (M2) 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 Quail Money 55.60 57.60 65.30 65.10 61.30 63.60 65.30 66.80 Money (Ml) 44.40 42.40 34.70 34.90 38.70 36.40 34.70 33.20 Real Money Growth Rale 7.40 8.00 9.20 1.40 12.50 -16.00 -1.60 n.a. Inflation Rate 3.00 10.30 17.70 31.00 55.50 60.00 75.00 46.00 Deposit Interest Rate 3.20 5.40 11.80 11.10 10.50 31.50 27.50 25.10 Lending Interest Rate 7.40 8.70 14.70 15.00 15.00 37.60 32.40 35.80 Discount Rate 6.30 7.70 13.80 14.00 14.00 35.00 30.00 32.50 Treasury Bills Rate - 6.90 12.50 11.30 10.80 33.80 28.80 31.30 GAIBANK Rate - 7.00 13.00 13.00 13.50 22.50 22.50 32.50 Real Interest Rate 3.60 -7.70 -7.20 -12.20 -26.00 -14.00 -24.30 -28.50 Public Sector Deflcit (%GDP) 4.20 13.40 48.60 40.00 29.80 8.20 35.40 n.a. Exchange Rate GS/USSl. 1.83 2.40 3.62 10.00 10.00 33.00 45.00 120.50 Reserves: Central Bank USSM - 43.10 7.40 8.90 4.00 14.70 28.80 82.00
SOURCE: IMF YEAR BOOK, 1991; CDB Country Report, 1992; Brink of Guyana Statistical Bulletin, Sept., 1991.
3.03 STRUCTURAL ADJUSTMENT PROGRAM
In 1988 policymakers adopted, with the assistance of the International Monetary
Fund (IMF), the World Bank and developed country governments, a structural adjustment program geared to restore macroeconomic stability and to lay the foundation for growth and development of the economy. Specific features of the program were the liberalization of the foreign exchange market and the removal of price controls; the dissolution and divestment of public sector enterprises; the reduction in government expenditures to levels that were more consistent with revenue collected; the rescheduling the external debt and debt forgiveness; the introduction of a mechanism for auctioning treasury bills, where competition among bidders might minimize interest payments on the 26 debt; the reduction in marginal tax rates on personal income and corporate taxes; and restraining monetary, fiscal and income policies.7
These new policies began to take effect in the second half of 1991, as manifested by a larger share of domestic credit being directed to the private sector, as compared to the 1980s. Further, real rates of interest became positive from the second half of 1991 onwards. Foreign exchange reserves at the central bank increased significantly, improving from US$28.8 million in 1990 to US$82.0 million in 1991. Declining growth rates in the money supply and substantial reductions in the holdings of government treasury bills and debentures by the central bank have been some of the other positive changes in the financial sector.
Guided by these new macroeconomic policies, the economy began to reverse its downward trend, culminating in a positive real rate of growth of above 6 percent in
1991. The unstable macroeconomic environment of the 1980s, on the other hand, was also not conducive to financial deepening. These issues will be examined in the next section.
3.04 THE STRUCTURE OF FINANCIAL MARKETS8
Financial markets in Guyana are loosely organized around the Ministry of Finance and two legislative acts, the Banking Act and the Cooperative Financial Institution Act.
Apart from the Ministry of Finance, there is the Bank of Guyana (central bank), five
’The source for this information is the Guyana Budget Speech by the Minister of Finance, March 1992.
“Part of the analysis in this section has been obtained from an unpublished report of the financial sector by the World Bank. 27 commercial banks, one development bank (GAIBANK), 36 cambios or foreign exchange
houses, 15 insurance companies, 49 cooperative credit unions, four investment trust
companies, two mortgage finance companies, 23 pension schemes, as well as pawnshops,
moneylenders, and informal savings groups, using Mbox-hands” to be described below.
There are no capital markets in Guyana, largely because of information problems as well as the absence of financial regulations.
The Ministry of Finance is responsible for all fiscal and monetary policies, the
National Budget and Accounts and the National Treasury. Its major reporting responsibility is to the Parliament of Guyana. Bank of Guyana administers for the state, through a Board of Directors, all monetary, credit, foreign exchange, and interest rate policies. Established in 1965, it conducts open market operations through the sale and redemption of treasury bills, monitors reserve requirements for commercial banks, provides a prudential supervisory role and is a lender of last resort. Unlike the
Currency-Board which it replaced in 1965,9 the Bank of Guyana is not autonomous and has a reporting responsibility to the Minister of Finance.
Commercial banks provide local financial services through a network and correspondents overseas. Two of the five commercial banks are sub-offices of foreign- owned banks (Bank of Baroda, India; and Bank of Nova Scotia, Canada). They accounted for 9 percent of total bank assets in 1991. In the recent past, there were sub-offices for Barclays Bank, Chase Manhattan, and Royal Bank of Canada. They were
The Currency Board provided a system in which the British Colonial Government set monetary policy and authorized the issue of new currency only against the equivalent holdings of Sterling (English) securities. See Blackman (1988) for a review of the system in the English-speaking Caribbean. 28 recently sold to the Government of Guyana, and have since been partially divested with the issuing of shares to the public, under two new banks: National Bank of Industry and
Commerce (NBIC), formerly Royal Bank of Canada; and the Guyana Bank for Trade and Industry (GBTI), formerly Barclays Bank and Chase Manhattan/Republic Bank.
The fifth bank, the Guyana National Cooperative Bank (GNCB), has 97 percent of its shares owned by the government. GNCB has nine branch offices outside of the capital city, Georgetown, while the other commercial banks are mainly urban-based. In consequence, no commercial bank exists in many rural areas, especially in Regions 1,
7, 8, 9 and in some sections of Regions 2, 3, 4, 5 and 6.10 One reason is the high cost of operations in some areas.
In the past, the Guyana Post Office Saving Bank, an integral part of the postal service, provided savings facilities and a limited amount of financial services in most rural areas. However, these services were terminated when the bank was dissolved in
1974. This outcome severely reduced rural financial services, leaving only the receipt and disbursement system of the sub-treasury, a branch of the Ministry of Finance,
GNCB, and GAIBANK in some locations. Since then, no other semi-formal or formal financial institution has been formed to mobilize deposits in rural areas. Thus, savings in the form of livestock, crops, land and other assets are substantial in many rural communities. Shopkeepers, produce buyers and moneylenders still provide short-term
10For administrative and political reasons, Guyana is divided into ten regions. Appendix 2 has a regional map of Guyana and list of the GAIBANK offices. 29 lending services at relatively high rates of interest, recorded to be as high as 20 percent
per month in some locations.
Cambios, a manifestation of the regulatory dialectic (Kane 1984), are privately-
owned financial houses, specializing in the purchase and sale of foreign exchange for a
profit. They began operations in the late 1980s, as a result of the overvalued official
exchange rate, rigid controls on the movement of foreign currency (currency declaration
forms were in use at various ports of entry/exit), strict procedures and licences for
importing and exporting goods through the formal banking system, and the continuous
shortage or excess demand for foreign currency at commercial banks.
Technological change in international communications, as observed in relatively
low-cost fax-machine services, has allowed cambio businesses to link with partners,
especially in North America and Europe. They compete with commercial banks by
setting competitive exchange rates for convertible currencies on a daily basis and provide
timely customer service, especially for Guyanese residents in North America. In 1991,
the clause in the Bank of Guyana Act defining foreign exchange rates was modified to facilitate the determination of the exchange rate for the Guyana dollar by market forces, using the information in the cambio market. In 1991, there were 36 licensed cambios, trading convertible currencies to the value of over US$150.0 million (Budget Speech
1992).
Of the two mortgage finance institutions, the Guyana Mortgage Finance Bank
(GMFB), and the New Building Society, the former is solely owned by the government and has a market share of 4 percent, while the latter, which mobilizes savings from the 30 public, is highly capitalized, and controls 60 percent of the market (1991). Most of the
remaining market is covered by GNCB Trust and insurance companies.
Among four trust companies, the government-owned GNCB Trust controls 90
percent of the market, by managing a large share of the pension funds in Guyana. The
remaining three are privately-owned and only recently established. These companies
mobilize deposits from the public, but are not supervised by the central bank, neither are they considered commercial banks, because of a loop-hole in financial regulations. This is further proof of financial fragmentation.
The 49 credit unions, which fall under the umbrella organization Guyana
Cooperative Credit Union League Ltd., make up a very small share of the financial market. These unions mobilize deposits and make loans to their members, but like the trust companies, they do not engage the attention of the central bank and fall under the supervision of the Cooperative Department, which is a section in the Ministry of
Regional Development.
Informal finance is conducted by moneylenders and pawnshop owners and informal savings groups, which developed a short-term financial instrument called the
"box-hand", rotated among members. The "box-hand" has similar characteristics to the
"su-su" in Trinidad and Tobago and Ghana. It is used for emergencies, such as the payment in part or full of medical bills, school fees, funeral expenses, purchase gifts or to acquire other forms of wealth, including gold, livestock, or farm inputs. Screening and enforcement in the box-hand is organized around the following elements: Familiarity - members must be well known to each other, by close friendship and /or
family relationships.
Proximity - members must live close to each other, or see each other frequently.
Usually, members should socialize with each other, not necessarily all members
together, but in small gatherings, where the conversation would be about the
welfare of the other members in the group.
Homogeneity - members must have similar income flows, consistent with the rotation
process. This is important because no written records are kept, especially in small
groups (6 to 10 members). At the beginning of the "box-hand," each potential
member is told how long the rotation will last and when it will be their turn to
draw the "box-hand." If this is acceptable to all members, it is implemented;
otherwise, those who do not find it convenient will leave the group. All shares
must be received by the "keeper of the funds" not later than the date agreed upon
by all members. The last date for the receipt of shares is governed by the last pay
date for any member. This is verified by members in the group. For example,
group members who work in the public service know the date when salaries are
paid.
Amount of the "box-hand" - The "box-hand" is made of shares. For example, a share
may be $5.00, but members can save more than one share, provided that it is in
multiples of a share. The size of the "box-hand" is therefore equal to the
expected sum of the total shares contributed. No interest is paid for saving or
borrowing and the "keeper of the funds" works for free. Those who receive the 32 early " box-hand" receive an implicit interest income transfer from those who
receive their "box-hand" at the end of the rotation. Usually, the life of the " box-
hand" runs from a few weeks and up to one year. High transaction costs,
barriers to entry, and negative real rates of interest in the formal financial system
may be reasons why no concern is shown by group members for the loss in
interest income.
Choice of the " keeper" - The "keeper of the funds" must be some one in the group
who is well-liked and held in high esteem by all members. The home of the
"keeper" is usually the place of business and it may have a twenty-four hour
service.
Enforcement - Persons who default are embarrassed and it is not uncommon for the
"keeper" to make good the temporary short-fall, until the defaulter or his/her
family/relatives make good the debt. For a new "box-hand" the person who did
not pay is excluded and this information is circulated, confirming that the
individual is dishonest. This is equivalent to a bad credit rating.
An interesting observation of the "box-hand" is that it has many of the features identified by Adams (1989) in informal finance, such as its residential orientation, flexibility, convenience, low operating cost and effective enforcement that ensures high repayment rates. As a modem financial instrument, however, the "box-hand" has many limitations, since it does not have the important characteristics of anonymity with savers and lenders, neither does it provide long-term savings and borrowing opportunities. In addition, the membership level in each group is small, because of the information and 33 socialization requirements. These requirements would be difficult for large groups, with heterogenous members. Consequently, the information and socialization requirements reduce the opportunity for economies of size, something the modem financial institution does very well.
Long-term finance has been and still is required in the agricultural sector to establish, among other things, drainage and irrigation systems, which are very expensive to construct and maintain. The history of these systems in Guyana shows that several disasters occurred when dams broke and lands were flooded. These events caused large losses in property, agricultural investments, disease and death of livestock and people
(Rodney 1981; Moore 1987). The domination by large borrowers over small borrowers for access to government loans to complete drainage and irrigation works revolved around the fact that the cultivation of sugar-cane was considered industrious, while the production of food crops was considered to be a waste of resources.
3.05 PERFORMANCE OF THE FINANCIAL SECTOR
Although most financial institutions, especially commercial banks, have been declaring profits during the 1980s, it is claimed here that these profits have been overstated. Moreover, if commercial banks should mobilize savings from surplus households and, in turn, lend those funds to deficit households or businesses in the productive sectors, they have not been seriously performing this role, since, with the exception of GNCB, they have been investing most of their funds (no less than 80 percent) in treasury bills, instead of providing loans to the private sector. Treasury bills 34 are zero-risk, low transaction costs, non-productive assets that temporarily reduce excess liquidity, while generating high income for banks, insurance companies and pension funds. The supply of treasury bills leads to the crowding-out effects by government deficit spending, which absorbed more than 80 percent of total domestic credit in the
1980s.
Financial institutions, such as GAIBANK and GNCB, experienced high arrears and loan default problems, as they attempted to work with a minimal share of govern ment securities.11 This outcome of high loan default may have resulted from a flawed screening technology and inadequate collateral security, along with other problems.
In 1991, when policymakers equalized the foreign exchange rate with the uncontrolled rates in the cambios, some financial institutions, such as GBTI, made windfall profits by converting foreign exchange reserves to Guyana dollars and seeking the much higher interest rates in the local market, compared to foreign markets. In contrast, GAIBANK sustained large foreign exchange losses, since it absorbed the foreign exchange risk on foreign loans when the policy changed.
Further, an expansionary monetary policy in the 1980s led to high rates of inflation, negative real rates of interest, and a shrinking real asset base in financial markets, a clear sign of financial repression. The slow response of supervisory agencies to the changes in the financial sector led to many under-capitalized financial institutions.
Further, the establishment of trust companies that competed with commercial banks for
n GAIBANK is restricted by law from buying treasury bills; GNCB invested 40 percent of its portfolio in treasury bills. In 1992, the Government of Guyana provided binding to the GNCB in the amount of G$1.2 billion, to cover loan default losses. 35 deposits, but were outside the supervisory control of the central bank, was a result of loop-holes in financial regulations.
From these observations, it is clear that the profits generated by financial institutions under the unstable macroeconomic environment of the 1980s were likely to be overstated. Actually, the real annual rate of growth of total bank assets were reported as -18 percent in 1989, -8 percent in 1990, and -20 percent in 1991 (unpublished World
Bank Report, 1992). Thus the financial sector was shrinking in real terms, as inflation eroded the value of its assets. Furthermore, with only five commercial banks and a large captive market dominated by government credit demands (the largest user of credit in the system), competition in providing better services and to introduce new products and timely reports on performance to small individual clients is not a priority of the banking system. Current supervisory requirements are not effective, due to the lack of resources at the central bank and the lack of enabling legislation supportive of corrective actions.
Another feature of the financial sector had been the blossoming of many semi-formal financial firms, which were incorporated and subsequently dissolved. Five agricultural credit banks and 27 cooperative credit banks were established between
1915-1933, with their subsequent dissolution, beginning in 1937.12 The Guyana Credit
Corporation, which was incorporated in 1954 to replace 26 cooperative banks (Lewars
1977), was dissolved in 1975 and subsequently replaced by the Small Industries
Corporation, which was also dissolved in 1978. The Guyana Cooperative Credit
Society, under which 101 credit societies were registered, was incorporated in 1966. By
12The material in this section is compiled mainly from two unpublished reports by Adonis, 1988 and Bascom, 1971. 36 1985, only 25 were operational. Credit activities were also undertaken by the Ministry
of Agriculture, the Department of Local Government, and the Guyana Rice Marketing
Board. All these credit activities were subsequently terminated and the liabilities of
many of these institutions were transferred to GAIBANK.
The major reasons advanced to explain why many of these institutions failed in
their attempt to become financially viable were high arrears and loan default, loan
targeting and political considerations, crop failure due to poor maintenance of drainage
and irrigation systems, inadequate collateral security, resulting, in part, from block-leases
that were not subsequently sub-divided,13 inadequate appraisals and supervision
procedures, conflict of interest by management, poor accounting records, inadequate
management information systems to track loans, and the lack of professional
management and technical skills in banking.
Most of these institutions were managed by non-financial managers, such as
agriculturalists, cooperative officers and government civil servants, since skilled
financial managers were in short supply, as reported in the labor force survey of 1965.
Consequently, there were hardly any decision-makers of sufficiently high professional
ability to undertake the task of dynamic credit management in any of these institutions,
or even in any of the sub-offices of the foreign owned commercial banks.14 Thus, the
loan portfolios of commercial banks in Guyana, such as Barclays Bank of London, and
l3A block lease is a title document which recognizes land ownership by several persons. As a collective good, it solved a social and political problem; however, in financial markets, it is useless, unless there is unanimous agreement by all owners.
>4There were no locally-owned commercial banks before 1970 and the top management staff of these banks were expatriate employees. Training local staff for senior positions was not available. 37 Royal Bank of Canada, were restricted to the export crop, namely, sugar-cane cultivation and sugar-cane processing, the distributive trades, bauxite mining, and personal loans.
All other activities which the commercial banks perceived to be more risky, especially non-plantation peasant agriculture, were avoided (Bourne 1973).
These events provided the financial environment that preceded the establishment of the first locally-owned commercial bank, the Guyana National Cooperative Bank
(GNCB) in 1970, and the establishment of the only development bank (GAIBANK) in
1973 by the government of Guyana. Their mandate was to provide financial services in the non-urban areas and to take on those high-risk activities that were eschewed by existing commercial banks.
3.06 POLICIES AND PERFORMANCE OF GAIBANK
Similar to other development banks, the Guyana Cooperative Agricultural and
Industrial Development Bank (GAIBANK) could be described as a typical clone, whose modus operandi is to provide rural producers with supply-leading finance, especially loans, at subsidized rates of interest in advance of demand (Patrick, 1966).
GAIBANK was established by the Government of Guyana in 1973, to replace several insolvent development financial institutions that supplied loans to borrowers in agriculture, forestry, fishing, mining, and industry. Restricted by law from accepting deposits, it has been funded (approximately 90 percent) by international agencies such as the Inter-American Development Bank (IDB), the Canadian International Development
Agency (CIDA), the Caribbean Development Bank (CDB), and the European Investment 38 Bank (EIB). The Government of Guyana provides equity and counterpart funds for donor projects, when required by various donor contracts.
Policies for GAIBANK are set by the Ministry of Finance and implemented at
GAIBANK through directives given by the Board of Directors. The Board of Directors acts on behalf of the Government (the principal) and management performs the role as agent, dealing with clients and providing reports to the Board of Directors, lending agencies and government auditors.
3.07 GAIBANK INTEREST RATE POLICY
Interest rates are set by the Ministry of Finance and implemented through directives given by the Board of Directors to management. Over the 1980-1991 period, interest rates charged by GAIBANK have been fixed for extended periods of time, despite high levels of inflation during the 1980s and early 1990s. Different interest rates are charged for projects in agriculture and industry, with the lowest rates being applied to loans for agricultural projects, in keeping with the notion that the interest rate subsidy must be granted to small farmers. Both rates are fixed below the interest rates charged by commercial banks.
Substantial movements in interest rates only began in 1989, when the structural adjustment program was adopted, with the main intention of making interest rates positive in real terms. This objective was achieved in the second half of 1991, when the macroeconomic environment began to stabilize and inflation rates began to fall. Table
3 shows that during the 1980s interest rates in nominal terms were held constant for 39 eight consecutive years (1981-1988). In 1991 the interest rate charged to all borrowers,
except those who received loans repayable in foreign currency, was equalized for the first
time at 32.5 percent, but it was still below the interest rate charged by commercial
banks, by 3.5 percentage points.
TABLE 3
RATE OF CHANGE OF CONSUMER PRICE INDEX (CPI) AND NOMINAL INTEREST RATES, 1989-1991.
YEARS CFI COMMERCIAL BANK AGRICULTURE INDUSTRY DOMESTIC INDUSTRY LOANS LENDING RATE CURRENCY FOREIGN CURRENCY 1980 14.2 13.5 11 12 12 1981 22.0 13.5 12 14 14 1982 21.0 14.4 12 14 14 1983 14.9 15.0 12 14 14 1984 25.2 15.0 12 14 14 1985 15.1 15.0 12 14 14 1986 7.9 15.0 12 14 14 1987 31.0 15.0 12 14 14 1988 56.0 15.1 12 14 14 1989 60.0 37.6 20 24 25 1990 75.0 33.6 20 24 25 1991 90(a) 36.0 32.5 32.5 32.5/15(b)
SOURCE: Bank of Guyana Statistical Bulletin, 1991. Minutes of GAIBANK Board Meetings, 1980-1991 (a) Inflation was estimated at 90 percent in the first half of 1991, and 25 percent in the second half. (b) 15 percent was charged on loans repayable in US$.
Another important feature of interest rate policy has been that after 1991, loans provided for industrial projects from donor sources were supplied at an interest rate of
15 percent, if these loans were to be repaid in US dollars. Otherwise, these loans were to be repaid at the prevailing 32.5 percent, with the client absorbing the foreign exchange rate risk at the date of payment. This policy targeted mainly borrowers involved in rice milling, timber, fish, and gold mining who were provided with special foreign currency accounts through local commercial banks. Some borrowers took advantage of this 40 facility, but many others, who had a good reputation in the export market as reliable suppliers, rejected this offer because their correspondents provided supplier credits and access to international loans at interest rates that were below IS percent.
3.08 OBJECTIVES OF GAIBANK AND ELIGIBILITY CRITERIA: THE EXPLICIT TECHNOLOGY15
The objectives of GAIBANK, as stated in the Cooperative Financial Institutions
Act, are to provide credit and related advisory services for the development of agriculture and industry; to promote investment in development projects in agriculture and industry; to act as the agent of the Government in such matters as may be agreed, provided that the bank can do so appropriately and consistently within its functions; and to assist generally in the development of the cooperative movement, in so far as it relates to the development of agriculture and industry.
Any individual, formal or informal groups, cooperatives, private and public companies can borrow from GAIBANK, provided that the project is viable and it generates a reasonable return for the owner after debt service. Information pertaining to the characteristics of borrowers, such as age, region, wealth, and previous credit history at GAIBANK are recorded and utilized in the screening process. The repayment schedule, based on income flows of the project and installments, may require average payments of no more than 45 percent of net benefits, with GAIBANK supplying approximately 65 percent of the total investment cost. Loans are classified as short-term
lsThe source for this information are the published reports by GAIBANK. 41 loans (less than 2 years for repayment), medium- term loans (between 2 and 5 years), and long-term loans (over 5 years and up to 10 years).
Eligible economic activities for which loans can be considered are crops, livestock, inshore/offshore fishing, logging/sawmilling, food processing and milling, mining, quarrying, wood, metal, plastic, rubber, leather, ceramic and garment manufacturing. Following a set of procurement guidelines for international bidding and shipping, lines of credit are established through local commercial banks for the acquisition of machinery, equipment, technical/professional services, spare parts, raw materials and packaging materials. The procurement of local inputs and services follow a quotation system that is less rigid than that used for international tender.
Assuming no fungibility, items which do not qualify for funding are the purchase of property, passenger automobiles, payment of taxes, rent, dividend, shares, stocks, bonds, securities, or the purchase of land or buildings for residential uses or the refinancing of debt.
3.09 APPLICATIONS PROCESSED DURING 1987-1991
There has been a declining trend in the number of applications received and approved by GAIBANK over the 1987-1991 period (Table 4). Beginning in 1987 at
2,032 applications received, this number peaked at 2,521 in 1988 and declined thereafter to 1,769 applications received by the end of 1991. 42
TABLE 4
NUMBER OF APPLICATIONS RECEIVED, APPROVED, AND REJECTED BY GAIBANK DURING THE 1987-1991 PERIOD
YEAR/REGION 1 2 3 4 5 6 7 8 9 10 TOTAL APPLICATIONS RECEIVED 1987 24 796 142 178 267 302 23 2 261 37 2032 1988 47 877 401 240 326 363 65 1 162 39 2521 1989 29 1014 201 228 335 294 66 0 89 68 2324 1990 16 901 214 240 294 252 17 0 102 41 2075 1991 33 880 171 205 207 287 0 0 6 10 1769 APPLICATIONS APPROVED 1987 7 681 106 141 256 312 4 1 161 20 1689 1988 31 760 298 207 318 296 78 0 209 32 2229 1989 18 855 166 191 292 333 18 0 81 58 2012 1990 11 854 194 232 271 243 17 0 121 38 1981 1991 0 803 136 153 148 237 2 0 0 10 1489 APPLICATIONS REJECTED 1987 17 115 36 37 11 (10) 19 1 100 17 343 1988 16 117 103 33 8 67 (13) 1 (47) 7 292 1989 11 159 35 37 43 (39) 48 0 8 10 312 1990 5 47 20 8 21 9 0 0 (19) 3 94 1991 3 77 35 52 59 50 (2) 0 6 0 280
SOURCE: Derived from Survey Data (.) Due to approvals being greater than applications received, the difference in some years is negative because of the annual carry over of applications.
Most of the applications received by GAIBANK were from applicants in Regions
2, 5, and 6. These three regions accounted for 70 percent of the total number of 10,721 applications received. In these regions there have been high concentrations of small rice producers, linked into a set of privately-owned, local and foreign rice milling companies that have access to export markets in the Caribbean and Europe. There are over 100 private rice millers, 58 of whom have received loans from GAIBANK in the last two years under the IDB/Govemment of Guyana Industrial Rehabilitation Loan. Farmers in these regions also engage in other agricultural activities on relative small farms, as 43 compared to rice farmers. The average size of a rice project is approximately 5 acres,
while it is less than 1 acre for food crops.
It is interesting to note that the total number of applications received from Regions
1, 7, 8, and 10 is less significant (10 percent of the total) when compared to the other regions. Thus, access to credit has been limited in these regions, largely because of
transportation difficulties that hinder movement in these locations.
The overall approval and rejection rates were 87 and 13 percent, respectively, for the 1987-1991 period, suggesting that the screening of applicants from access to credit was not high. However, this level of screening, coupled with a binding interest rate ceiling might be allowing too many low-retum projects to enter the credit system. In fact, a recent GAIBANK report suggested that the screening mechanism was overly optimistic, allowing too many loan approvals for high-risk projects that increased loan default.
3.10 DISBURSEMENT LEVELS, 1987-1991
Over the 1987-1991 period, a total of G$l,081.5 million was disbursed to different projects in many different sectors (Table 5). While this level of disbursements appears to be high, a closer look at the data shows that most of these disbursements occurred in 1991, approximately 82 percent of the total. This result is largely due to a line of credit from the IDB, mainly for rice milling and sugar processing, as well as to another line of credit from EIB, for industry. Without these donor funds, the real level 44 of industrial disbursements would have declined dramatically, to levels below the average for the period of G$216.30 million per year.
TABLE 5
GAIBANK: VALUE OF DISBURSEMENTS BY ECONOMIC ACTIVITY REAL TERMS (1985=100) GSM, 1987-1991
ACTIVITY/YEAR 1987 % 1988 % 1989 % 1990 % 1991 %
Rice Inveatment 2.56 9.30 2.78 7.50 0.97 3.30 1.62 1.70 1.48 0.17 Rice Production Credit 6.44 23.40 8.73 23.30 11.10 37.32 9.96 10.20 17.63 1.98 Sugar cane 0.04 0.10 0.07 0.01 0.00 0.00 0.00 0.00 0.01 0.00 Food and Tree Cropa 0.75 3.00 2.67 7.14 0.65 2.00 0.42 0.42 0.27 0.03 Dairy 0.60 2.16 1.00 2.70 0.21 0.70 0.13 0.13 0.21 0.02 Beef 0.70 2.54 0.37 0.90 0.04 0.13 0.02 0.00 0.00 0.00 Poultry 0.75 2.71 1.63 4.30 0.57 2.00 0.37 0.38 1.48 0.17 Pig Inveatment 0.08 0.20 0.20 0.50 0.49 2.00 0.31 0.31 0.15 0.02 Pig Feed Credit 0.01 0.01 0.00 0.00 0.10 0.00 0.00 0.00 0.00 0.00 Other Livestock 0.02 0.01 0.39 1.00 0.03 0.10 0.01 0.00 0.00 0.00 Food Crop Revolving Fund 0.67 2.43 1.52 4.00 0.99 3.33 0.63 0.64 0.24 0.02 TOTAL AGRICULTURE 12.62 45.86 19.36 51.00 15.06 51.08 13.47 13.78 21.46 2.41
In/OfTahore Fishing 1.33 4.80 3.79 9.30 2.45 8.30 2.10 2.00 0.76 0.09 Deep Sea Fiahing 0.16 0.50 0.41 1.10 0.14 0.50 1.04 1.10 0.29 0.03 Logging/Sawmilling 0.27 0.90 2.31 6.10 0.50 2.00 2.44 3.10 4.83 0.50 Charcoal 0.04 0.10 0.00 0.00 0.10 0.00 0.00 0.00 0.00 0.00 Wooden Producta 0.56 2.00 0.57 1.50 0.08 0.03 0.55 0.60 4.39 0.49 Transportation 0.00 0.00 0.00 0.00 0.03 0.01 0.00 0.00 1.84 0.20 Fabricated Metala 0.02 0.01 0.00 0.00 0.00 0.00 1.12 1.20 0.06 0.00 Textile and Leather 0.10 0.30 0.07 1.90 0.01 0.00 0.07 0.07 0.00 0.00 Non Metallic Minerala 0.00 0.00 0.40 0.10 0.00 0.00 0.90 0.10 0.00 0.00 Chemical Producta 4.63 16.80 0.13 0.30 0.08 0.03 0.01 0.00 10.12 1.14 Printing and Publiahing 0.21 0.73 0.00 0.00 0.08 0.03 0.67 0.70 1.07 0.11 Food and Beveragea/Rice 5.91 21.50 3.80 9.30 9.93 33.39 31.57 32.40 843.71 95.00 Milling/Sugar Processing Mining and Quarrying 0.59 2.60 0.79 2.10 0.37 1.30 0.32 0.34 0.00 0.00 Trade and Hotels 0.00 0.00 0.42 1.10 0.17 0.60 0.51 0.53 0.00 0.00 Construction and Business 0.08 3.90 0.26 0.62 0.11 0.31 0.03 0.04 0.15 0.02 Services Other Manufacturing 0.00 0.00 5.75 15.23 0.71 2.42 43.04 44.04 0.10 0.01 Industry TOTAL INDUSTRY 14.90 54.14 18.34 49.00 14.68 50.92 84.28 86.22 867.32 97.59 GRAND TOTAL 27.52 100 37.70 100 29.74 100 97.75 100 888.78 100
SOURCE: GAIBANK Annual Reporta 45 Total disbursements for agriculture declined after 1988, falling from G$19.36 million to 13.5 million in 1990. Disbursements would have declined in 1991 had it not been for rice production loans. This outcome has been largely determined by high inflation rates over this period, indicating that stability in the macroeconomic environment is important for growth in the loan portfolio. Furthermore, with the number of loans declining, and the volume of disbursements rising, this is a clear indication of the beginning of portfolio concentration, as a few large borrowers in rice milling, beverages and other manufacturing activities captured a disproportionate share of subsidized credit.
3.11 DECENTRALIZATION OF TOE GAIBANK CREDIT SYSTEM
GAIBANK conducts its business using a decentralized credit system of several full- and part-time offices, located in the ten administrative regions in the country. These offices are:
Region 1: Kumaka.
Region 2: Anna Regina, Charity, and Ondemeeming.
Region 3: Head Office, Wakenaam, and Leguan every Monday and the first week of
the month for field service. Parika Marketing Center on Mondays through
Thursday. Timehri Docks- every Friday.
Region 4: Head Office, Cane Grove on Wednesdays, and Kuru Kururu Newtown
Marketing Center on Friday.
Region 5: Onverwagt. 46 Region 6: Tain, New Amsterdam G.C.I.S Office on Friday, Corriverton District Council
Office on Tuesday, Black Bush Poulder, Johanna Land Development Office on
Wednesday, and the Marketing Center at Mibikuri on Monday.
Region 7: Head Office and Bartica Regional Administration Office every third week of
the month. Upper Mazaruni during the last two weeks in every quarter.
Region 8: Head Office. Officers visit locations once every three months.
Region 9: Lethem.
Region 10: Linden and Ebini.
Depending on the complexities of the project submitted by an applicant, credit analysts employed by Gaibank conduct a detailed examination of the loan request.
Integrated studies of market demand and market share for the proposed project are initiated, along with an analysis of the proposed technology to be utilized, consisting of a review of management, labor and legal requirements, financial and economic analyses, as well as the calculation of the internal rate of return. All these actions form the basis for the explicit screening process which, in turn, leads to the preparation of a stylized project report recommending loan approval or rejection.
As part of the decentralized credit system in the mid 1980s, GAIBANK’s Board of Directors approved various lending limits to Regional Managers. The objective of this arrangement was to reduce the time taken to process applications through the system, reduce transaction costs for borrowers, and shift the credit-granting responsibility to
Regional Managers. 47 The data in this dissertation have shown a shift from the decentralized credit
system, as less loans were being approved by Regional Managers, on an annual basis,
while an increasing number of loans were sent to the Head Office for processing,
especially by the General Manager (Table 6). This trend represents a contraction of the
decentralized credit system and is contrary to existing procedures, because senior
management must become more involved in loan appraisal activities, they must monitor
delinquent borrowers, and control loan default. Indexing lending limits can resolve this
problem, as it appeared that high inflation rates caused the shift in the distribution of
loans from the decentralized system, instead of a change in credit policy. The loss of
experienced managers also contributed to this situation.
TABLE 6
LENDING AUTHORITY AND NUMBER OF LOANS PROCESSED FOR VARIOUS CATEGORIES OF MANAGERS AND THE BOARD OF DIRECTORS IN THE SAMPLE, 1987-1991.
ITEMS 1987 1988 1989 1990 1991 TOTAL
LENDING AUTHORITY
Number of loans approved by: - Board of Directors 0 2 2 1 6 11 - General Manager 7 36 53 54 19 169 Deputy General Manager 0 1 2 6 36 45 - Senior Managers 14 3 4 2 2 25 - Regional Managers 58 82 51 38 25 254
TOTAL 79 124 112 101 88 504
SOURCE: Derived from Survey Data 48 3.12 LOAN PORTFOLIO MARKET SHARES
Nationally, GAIBANK’s share of the total loan portfolio relative to commercial banks has been changing over the 1987-1991 period. Whereas in 1987, GAIBANK had a market share of loans for agriculture and for industry of 38 and 29 percent, respectively, by 1990 the share for agriculture had decreased to 28 percent, while the share for industry had increased to 42 percent (Table 7). This suggests that the bulk of subsidized loans at GAIBANK were directed to industry. This change, as noted before, is related to the donor fund previously mentioned. However, it should be noted that
GAIBANK did not receive any new loans for the agricultural sector for ten years and has been limiting its activities in agriculture to the repayments from existing loans. Thus, with increasing loan default problems, the agricultural portfolio will be adversely affected.
TABLE 7 LOAN PORTFOLIO MARKET SHARES BY GAIBANK AND COMMERCIAL BANKS, 1987-1990 AGENCIES 1987 1988 1989 1990 % %% % Ag. Ind. Ag. Ind. Ag. Ind. Ag. Ind. GAIBANK 38 29 33 23 26 36 28 42 Commercial Banks 62 71 67 77 74 64 72 58 Total Value GM$ 141 386 216 638 358 1284 464 2127 SOURCE: 1) GAIBANK Annual Report; 2) Statistical Bulletin, Bank of Guyana 49 3.13 STAFF TURNOVER, 1987-1991
There has been a continuous flow of staff in and out of the employment of
GAIBANK during the 1987-1991 period. Additionally, the total number of employees declined from 338 in 1987 to 271 by 1991. With an average staff turnover rate of 28 percent, this implies that the consolidation of a critical mass of trained staff is lacking.
In 1991, out of a total of 271 employees, 80 percent of them (217) had less than five years of service in an institution that is on the verge of entering its third decade of existence. Table 8 shows data on the loan portfolio and staff levels. Some of the reasons advanced to explain the turnover of staff have been unattractive salaries and benefits that are lower than the benefits paid by other financial institutions. Other reasons have been migration and opportunities for higher education, and poor incentives to reward good workers.
TABLE 8
LOAN PORTFOLIO INFORMATION AND STAFF LEVELS: 1987-1991
1987 1988 1989 1990 1991
Number of Applications Received 2,032 2,521 2,324 2,075 1,769 Number of Applications Approved 1,689 2,229 2,012 1,981 1,489 Number of Applications Rejected 343 292 312 94 280 Number of Active Loans in the 9,475 6,529 6,250 2,571 2,000* Portfolio Total Staff 338 347 344 303 271 Staff Leaving the Job 56 77 109 110 88
SOURCE: GAIBANK Reports 1987-1991; * Unaudited 50 3.14 POTENTIAL FOR DEPOSIT MOBILIZATION
Because of existing regulation, GAIBANK does not provide deposit services to its borrowers. At the same time, evidence compiled in the data for this dissertation indicated that not only do borrowers of GAIBANK save in financial form, but they also maintain savings accounts at commercial banks. In particular, savings at commercial banks by GAIBANK borrowers in the sample increased in nominal terms from G$0.8 million (real terms 0.6 million) in 1987 to G$8.9 million (real terms 2.2 million) in
1990. It appears that the commercial banks lent to these producers the equivalent of what they had mobilized in 1988 and 1989 (G$2.7 million), while in 1987, 1990 and
1991 total deposits of G$11.8 million were contrasted with loans for about G$2.05 million (Table 9). The commercial banks may have used the surplus funds from these producers to buy other assets, such as risk-free Treasury Bills, which accounted for 80 percent of the total commercial bank assets over the 1987-1991 period.16
In contrast, loans to GAIBANK borrowers in the sample increased from G$1.4 million in 1987 to G$23.4 million in 1991. With no deposit services, GAIBANK does not complete a cycle of financial intermediation. Should GAIBANK consider establishing savings and demand deposit services, it appears that the liquidity potential of clients is large, as borrower liquidity levels grew from G$2.0 million in 1987 to G$52.2 million in 1991. Indeed, these liquidity levels were higher than the loan disbursements by
GAIBANK and commercial banks combined. This implies that a strong potential for
16Unpublished World Bank Report 51 deposit mobilization programs exists, provided that appropriate regulations are adopted.
Table 9 shows details of the survey results on financial intermediation.
TABLE 9
FINANCIAL INTERMEDIATION BY GAIBANK BORROWERS, GSM 1987-1991.
GAIBANK COMMERCIAL BANKS 1987 1988 1989 1990 1991 1987 1988 1989 1990 1991
Borrower Saving Accounts -- -- - 0.8 1.3 1.4 2.1 98 Outstanding Debt by Borrowers at Appraisal 0.1 0.9 1.6 1.9 7.6 0 1.3 1.4 0.05 2.0 New Loans to Borrowers 1.3 3.2 0.5 6.2 15.8 --- - - TOTAL 1.4 4.1 2.1 8.1 23.4 0.8 0 0 2.05 6.9 CASH AND LIQUIDITY POTENTIAL OF BORROWERS 1987 1988 1989 1990 1991 Cash 0.2 0.8 1.5 1.6 6.7 Accounts Receivable 0.4 0.7 2.4 4.4 7.3 Inventory Crops/Livestock 1.3 3.5 6.5 6.3 20.9 Inventory Stock 0.1 2.3 3.0 3.6 17.3 TOTAL 2.0 7.3 13.4 15.9 52.2
SOURCE: Derived from Survey Data
3.15 GAIBANK PROFIT AND LOSS STATEMENT
Although gross profits (total interest and other income minus total expenses) were made over the 1987-1991 period, these profits have been overstated. One reason for this has been foreign exchange losses on donor loans, resulting from the devaluation of the
Guyana dollar. This has resulted from liabilities payable in foreign currency, while loans to borrowers are contracted in Guyana dollars at interest rates that do not cover this risk. 52 This situation resulted in net profits (gross profits minus foreign exchange losses) being
negative for the 1987-1990 period, but positive for 1991 due to a favorable exchange rate
(Table 10).
This situation suggests, once again, the importance of having realistic exchange
rates and lending policies that produce loan terms and conditions that do not jeopardize
the long-term viability of the bank.
TABLE 10
GAIBANK INCOME AND EXPENDITURE STATEMENTS: (1987-1991) GSMELLION
ITEM/YEAR 1987 1988 1989 1990 1991* Total Income 34.0 39.9 116.1 275.5 890.4 Total Expenditure 30.9 37.3 89.9 169.1 434.6 Gross Profit (Loss) 3.1 2.6 26.2 106.4 455.8 Less (0.9) (2.9) (48.9) (40.2) (216.7)
Reserve for Bad Debt Foreign Exchange Gain (Loss) (82.7 (14.7) (170.1) (202.1) 409.5 Other Adjustments -- -- 98.0 TOTAL (80.5) (15) (192.8) (98) 786.6
SOURCE: GAIBANK reports and records.; * Unaudited statements
3.16 LENDING CAPACITY AND THE IMPACT OF ARREARS AND DEFAULT
Inflation and negative real rates of interest have drastically reduced the lending capacity of GAIBANK over the 1987-1991 period (Table 11). Furthermore, lending capacity has also been declining due to repayments GAIBANK has been making on existing debt to various lending agencies. During the 1987-1991 period, GAIBANK 53 repaid principal and interest for G$157.0M on donor loans. While this debt repayment by GAIBANK appears to be an indication of its capacity to meet its obligations, it is a drain on its liquidity, since the foreign exchange rates over this period have been as much as 50 times the rates when the loans were initially disbursed. More importantly, lending capacity has been reduced because of arrears and loan default problems.
TABLE 11
AGING OF THE LOAN PORTFOLIO (Principal): GSM 1987-1990.
1987 1988 1989 1990 No. Instal Val. GSM No. Instal Val. GSM No. Instal Val. GSM No. Instal Val. GSM
1-3 Months 401 5.6 342 0.7 446 4.2 426 10.1 3-0 Months 162 1.0 100 0.6 334 1.1 290 21.0 6-12 Months 1,780 4.5 2,892 4.0 669 8.7 621 9.7 12 Months and over 27,105 20.4 30,604 25.4 35,102 39.4 1,029 42.1 Total Arrears 29,448 31.5 33,938 30.7 36,551 53.4 2,366 82.9
No. Instal Val. GSM No. Instal Val. GSM No. Instal Val. GSM No. Instal Val. GSM
Contaminated 82.4 59.1 153.9 262.0 Principal
Outstanding Balance: . Agriculture - 54.4 - 71.3 - 92.0 129.0 . Industry - 113.1 - 145.0 - 459.4 886.3 TOTAL - 167.5 - 216.3 - 351.4 1,015.3 No. of Loans 9,475 6,529 6,250 2,571
SOURCE: GAIBANK Reports, 1987-1990; ’ Estimated
NOTE: The total number of loans declined between 1989 and 1990 due to no new loan sources, increasing default problems and the concentration of the portfolio into large loans, especially in industry. Financial records show that some G$82.4 million were recorded as "contaminated
loan principal,"17 with G$20.4 million of installments in arrears for one year or more
in 1987. By 1990, the contaminated loan principal increased to G$262.0 million, with
G$42.1 million of installments in arrears for one year and over. Installments in arrears
for less than one year also increased from G $ ll.l million in 1987 to G$40.8 million by
December, 1990 (Table 11). Furthermore, a large proportion of the loans in arrears 12
months and over were written off the loan portfolio in 1989. This caused the number
of installments in arrears to decline significantly between 1989 and 1990, from 36,551
to 2,366.18 Thus, arrears and loan default are the most important problems GAIBANK
face. Failing to find a strategy for solving these problems will eventually cause
GAIBANK to be insolvent.
3.17 CONCLUDING REMARKS
This chapter has shown that a stable macroeconomic environment is necessary for
building a strong financial sector. The macroeconomic policies of the 1980s were not
conducive to such an environment and this resulted in a financial sector that had been
marginalized, due to deficit spending, high inflation rates, negative real rates of interest,
financial disintermediation, and decreasing foreign exchange reserves. Although the financial repression of the 1980s has been significantly reduce in the early 1990s, it is
17Contaminated loans have at least one installment in arrears and it is a first indication of default problems. Although the proportion of the loan portfolio in arrears has declined (1987-1991), the absolute levels are relatively larger.
l8These debt write-offs, however, were not "pardons" given to clients, as they were still liable for these debts through court action. 55 argued here that the viability of the financial sector will still be threatened, if the arrears and loan default problems are not addressed at the micro level within each financial institution. The next chapter addresses the loan default problem by presenting a model that attempts to explain lender behavior by analyzing the financial technology utilized by
GAIBANK to screen applications and ration credit to borrowers. CHAPTER IV
MODEL OF LOAN SCREENING AND DEFAULT
4.01 INTRODUCTION
Because of inherent information problems in financial markets, and the inability
of lenders to predict which borrowers will default, banks usually devise screening and
rationing mechanisms aimed at avoiding loan default.19 A comprehensive evaluation
of screening mechanisms should compare the actions and criteria for loan approval and
rejection by the lender with the repayment responses of borrowers. Unfortunately, most
lenders, including GAIBANK, do not have the resources to compare their actions with
those of borrowers. One reason for this is that new loan programs are usually given priority over any other action by the lender. Thus, the efficacy of the screening
mechanism is not sufficiently investigated.
The screening and rationing procedures at GAIBANK are based on a set of production objectives, a list of eligible economic activities, lending guidelines, borrower information, and a project appraisal system for loans. If GAIBANK cares about financial viability, then the decision to approve or reject a loan application will be based, at least
19Advances in loan processing technologies have allowed large banks in the United States to utilize credit scoring models to screen loan applications and to separate clients into different risk categories, so that they can charge different interest rates and design different lending terms. This technology is not available in many developing countries and may not be applicable, given the limited availability of certifiable financial information for small firms.
56 57 in part, on its perception that borrowers from a particular borrower-class will in the future repay or default. However, which borrowers will actually pay or default is unknown at the time of approval.20 If GAIBANK decides to lend, and if its objective is to remain financially viable, then it must determine how much to lend before granting the loan, as high loan default and the high cost of granting credit will transform the institution into a welfare agency, instead of a solvent financial institution.
The cost of granting credit consists of three components. These are the costs of the funds to be lent, the administration costs of the loans, and the losses due to loan default (Gonzalez-Vega 1976). The cost of the funds to be lent are payments the bank must make to depositors, the owners of equity, lenders, and donors. These payments, which are the rewards for deferring consumption, must reflect the opportunity cost of capital. In addition, there are handling costs for maintaining these sources of funds.
Administration costs consist of two parts. One part reflects the handling charges the bank incurs for recording and disbursing the funds to borrowers, and the second part reflects the risk-reducing costs of gathering information on the borrower in order to facilitate the operation of the screening mechanism and monitoring after the loans have been approved.
Handling costs (H), which tend to be independent of loan size, and the riskiness of the loan, are considered fixed. Risk-reducing costs are related to the risk of loan default. As the lender spends more resources in information gathering for each
20Usually, applicants and the lender have preliminary discussions/ interviews before a formal application is hied and some applications are screened out at this stage. Those that are not screened out at this stage could still be rejected later. The requested loan amount could also be modified by the lender before approval. 58 applicant, this action increases the chances the bank has for successfully screening out bad borrowers and reducing its losses from loan default. However, it should be noted that there is an optimal amount that the bank should spend on information gathering, since it is a costly undertaking.
Apart from the lender determining how much to approve, given that loan default may increase with loan size, this decision is further complicated by a fixed, nominal and binding interest rate ceiling which reduces the opportunity for GAIBANK to apply an appropriate risk premium to cover expected losses from high-risk investments. In order to examine these issues and their impact on the lender, a model of lender behavior will be presented in this chapter. This chapter will be divided into three parts. In the first part, a description of the guidelines utilized by GAIBANK to process loan applications through the credit system will be undertaken. This will be followed in the second part by the model proposed for the study.
The model will consist of two equations, representing the screening and rationing actions of the lender and the repayment and default responses of borrowers. These equations will then be examined under a diagnostic device from which an assessment will be made about the efficacy of the screening mechanism. It is anticipated that these results will enable policymakers to design better loan contracts which, in turn, would influence the willingness of borrowers to repay loans, while it improves the efficacy of the screening and rationing mechanism. Screening and rationing will be specified in one equation, while the repayment responses will be specified in the second equation. Given the complementarity relationship between the expected value of the promised repayment 59 and the expected losses from loan default (Gonzalez-Vega, 1976), the internal consistency
of the repayment responses will be compared to various default responses of borrowers.
The last section will present some concluding remarks and a summary of the expected
signs for the parameters in the model.
4.02 MODEL ASSUMPTIONS
The model presented in this chapter is based on an extension and application of
the credit rationing models by Hodgman (1960) and by Gonzalez-Vega (1976). The main
idea of the extension is to analyze the screening and rationing process, as the lender
makes decisions about approving credit, avoiding default and maximizing the value of
the portfolio, while the borrower makes decisions about loan default and repayment.
Several assumptions are made for this model. First, borrowers are assumed to be
heterogenous, with different levels of risk, expected returns and unobservable
probabilities of default. In these circumstances, borrowers should be charged different
interest rates, to reflect the differential cost of lending to them. High-risk projects, for
instance, should be charged a higher rate of interest or require higher collateral security,
in order to reflect the associated risk of default.
Second, because of borrower heterogeneity, it is assumed that the first task the lender undertakes is to screen applicants into different risk classes. This distinction among borrowers is made possible by the information supplied by the borrower and the utilization of the existing financial technology of the lender. For ease of exposition, it is assumed that the lender divides all the applications received into three classes, namely, 60 class I (low-risk), class II (medium-risk), and class III (high-risk).21 The main reason for separating borrowers in this manner is to allow appropriate risk premiums to be charged and to allow the lender to identify the appropriate marginal cost of lending to each class. Applications placed in the high-risk class may be from applicants with little or no collateral, or no previous credit history with the lender, or involved in activities known to experience large variability in incomes, resulting from price or yield risks.
Alternatively, applications placed in the low-risk class may be experienced applicant- borrowers with a long credit history with the lender, and may have substantial collateral security, or may be involved in profitable activities, where the variability in income, output and profits are small. Those in the medium-risk class fall in between the other two classes.
Third, it is assumed that a binding, nominal interest rate ceiling (rc) is set by the regulatory environment (Ministry of Finance), with the understanding that it must be equally applied on all loans supplied by the lender, GAIBANK. The impact of the binding ceiling is that it neutralizes the interest rate as a screening device (Hoff and
Stiglitz, 1990; Stiglitz and Weiss, 1981; Adams and Graham, 1981), with the result that non-price credit rationing is employed to allocate credit to the various borrower-classes.
In this situation, three possibilities are likely, as shown in Figure 2. Some borrowers may receive an amount that is equal to that demanded at interest rate rc (loan amount Lc in the low-risk class). Other borrowers in class II are rationed by the amount L4-Lj , while
21 In theory, every borrower may be placed in a separate and distinct class, as this could certainly facilitate better selection. However, this process would be very costly, given the information processing technologies available ( Gonzalez-Vega, 1976, p. 293). 61 class III borrowers are screened-out of the market, receiving no loans (rejected applicants). The main implication here is that classes of borrowers with the highest marginal cost of lending are more severely rationed.
r class I class II class III me 1 me 3 me 2
D2 D4
Figure 2: CREDIT RATIONING BY BORROWER CLASSES
Fourth, it is posited that default risk characterizes the screening and rationing actions of the lender, based on the following assumptions:22
(a) The lender is risk neutral, making one-period loans.
(b) The borrower’s project provides possible outcomes (y=income), bounded
by k < K, with a probability density function, fCy).23
2*These assumptions are adapted from the model by Hodgman (1960).
23 When collateral is provided, k shifts to the right to include the value of collateral as a source of repayment. 62 (c) The contractual total repayment equals (1 +r)L;, based on the loan size Ls
and the binding lending rate, rc. If default occurs, the bank receives the
available proceeds, y, in partial repayment.
(d) It is assumed that the expected repayment of principal E(Lj) per loan is
given by:
Li K m E{Li)=jyf(y)dy+jLif{y)dy i=l..n k Li
where the first term represents the bank’s expected repayment (y) when there is partial loan default (y BILJ = G(y) \Lki+L1 (F(y) ) £ = GiLj) -G(k) +L± [F(K) -F(Ld) ] <2> = G(Lj) + Lj [1 - F(L,)] where F(K) is the upper limit of the cumulative distribution, equal to 1 at that point, and [1- F(L;)] is the probability for each borrower fully repaying the loan principal. Gf is the expected value of repayment when partial default of principal occurs (it is the first term in equation 1) and G(k) is zero. (e) Inherent in the specification of equation (1) is the lender’s subjective probability, used in screening applications into various borrower classes and rationing credit according to the information obtained from borrowers’ 63 files. Thus, if the lender is concerned about risk-adjusted returns on each loan, then the expected revenue (E(R)) is given by: E(R) Li where r, is the risk-adjusted return on the portfolio. (f) It is assumed that the per unit variable cost of lending for the bank is: Vj(Li), which increases with loan size. Also, it is assumed that the fixed administration cost per loan is H. (g) It is assumed that loan demand (Dj) is negatively related to the interest rate charged and the lender knows the optimal loan offer for each applicant, given the loan amount applied for, the information obtained from the preliminary discussions between the lender and the borrower, information in equation (1), and the restriction that: Li ^D1 i . . .n ^ which reflects the rationing that is observed.24 (g) With a binding, exogenously-imposed interest rate ceiling (rc), it is assumed that the lender maximizes expected profits (E(tt)) from the loan portfolio of n applicant-borrowers: 24This restriction is adapted from the model by Gonzalez-Vega (1976) 64 (5) Max.Li:E(n)= [ (i+re) E(LX) -L j - L r nH subject to: L ^ D t i= i..n <6) E(L1)=G(Li) +Li [l-F(L1)] (7) L^t 0; D±> 0; D ^ L ^ , i^r^r^O i=l..n where r; is the unconstrained-profit maximizing interest rate for the bank. The Lagrangian function for this problem is: LG = E-i (<1+rc) WLi) +Lia-FU i)l-J^i Li)- (9) E - i vi
(h) Assuming that the expected-profit function E(x) is concave and twice
differentiable and the technology is convex, ensuring, thereby, that the Kuhn-
Tucker conditions are sufficient (Hestenes, 1981) for attaining the optimal loan
size, the first-order conditions are: 65
- |^ = &vj , « i ^1* 0 (10) = (1 +TC) [ l - F ^ ) ] - 1 - M C i - k ^ 0 = MERi - M C l-X ’i i 0 Li-T7^ = LiiMERr MCi -A.J = 0 OLj ( 1 1 ) Li [MER±- MCi -^1= 0 i = 1 ____n 6 LG bki X^-XtlLrDjs 0 U2> Lji 0; 0; 0 (13) where MC^MARGINAL COST =vi + -^ L i (14) OLj and 66 (15) MERi = Marginal Expected Revenue =(l+rc) [1 -F(L±)] -1 These results imply that when the Lagrangian multiplier is strictly positive (Xj>0), then marginal expected revenue is greater than marginal cost for the amount demanded and the borrower is not rationed, receiving a loan that is equal to the amount demanded:25 Xs>0MERX = MC^X^, L1=Di i= l. .n *16) Alternatively, when lambda is zero, then marginal expected revenue is equal to marginal cost and the borrower is quantity rationed, receiving a loan that is less than the amount demanded: A,i=0:^ MERi = MC± ; w i t h 1=1... n (17* Finally, it is important to recognize that when the size of the loan increases, or when there is an increase in the risk of default, marginal expected revenue declines: bMERi , . . (18) 6l 1«-(l*Xe)flLi)<0 ' ' The figures below show three different cases in the presence of a binding interest rate ceiling (rc) and its impact on rationing. Figure 3 presents the case of two borrowers for whom marginal costs of lending as perceived by the bank differ. At loan size L,, marginal expected revenue (MER) is greater than marginal cost (MC,) for borrower 1 and no rationing takes place, as lambda (X,) is positive. This is not the case for borrower 25 These results are consistent with those obtained by Gonzalez-Vega (1976). 67 2, as MC2 is higher than MER for that loan size. The bank will reduce the size of the loan supplied. At loan size L 2 , marginal expected revenue becomes equal to marginal cost, X2 is zero, and the borrower is quantity rationed by L! - Lj. Figure 4 shows the case of collateral as a source of repayment. As the value of collateral accepted as security increases k, to k2, the risk of default declines, causing an upward shift of the marginal expected revenue curve and quantity rationing to decline from Lj-Lj to zero. In Figure 5, the perception of higher risk is shown by the downward rotation of the MER curve. At MERj = MC, there is no rationing at the interest rate ceiling (rc). As the risk of default increases, the marginal expected revenue curve becomes steeper and when MER2 = MC, the borrower is rationed by L4 -Lj. In summary, ceteris paribus, rationing will be more intense the higher the marginal cost of lending, the lower the collateral ratio, and the higher the perceived risk. M C 2 MC1 MER FIGURE 3: THE IMPACT OF MARGINAL COST ON RATIONING 68 MC men mer2 FIGUIRE 4: IMPACT OF COLLATERAL ON RATIONING r MER1 mer2 FIGURE 5: THE IMPACT OF DEFAULT RISK ON RATIONING 4.03 THE RATIONING RATIO It can be inferred from the last section, equation (15), that when the probability of full repayment ( [l-F(Lj)]) rises, marginal expected revenue increases, and the lender is likely to ration the borrower less. Alternatively, when the probability of full repayment declines, the lender is likely to ration the borrower more intensely. Using the information from the first-order conditions (Equations 10, 11 and 12), a specification of the lender rationing credit to borrowers may be given as: 69 X,>0 : -*X,= MER,- MC, X,=0 : - MER,= MC, L L <19) X,>0 : -* RR, = — =1; X,=0 : — R R ,= —±< 1 1 Di Di This result implies that lambda is the marginal expected profit for the lender. As long as this marginal expected profit is positive, for the amount demanded, the borrower is not rationed, and the rationing ratio is equal to one. If marginal expected profit becomes zero, for a size of loan smaller than that demanded, rationing will take place. In this case, the rationing ratio is less than one. Furthermore, when the marginal cost curve shifts upwards or when the probability of default increases for a given size of loan, the rationing ratio declines, implying that the intensity of rationing increases. Similarly, when expected repayment increases, the rationing ratio increases, indicating that there is less rationing: 6RRi . bRRi . 6RRi (20) 6F(Lx) ' 6MCS ' 6 CL-FiLj)] These results suggest that the rationing ratio (RRj) is a function of marginal costs and the probability of full repayment: JZJZi=J (MC±, [1-FU.j)] ) (21) Thus, information on the determinants of marginal cost and on what affects expected repayment will influence the intensity of rationing, as reflected in the rationing ratio (RRi). For example, established borrowers will imply lower information costs compared to borrowers who are yet to establish a credit history with the lender. Thus, ceteris paribus, established borrowers will likely be rationed less, given a lower marginal cost 70 curve, while the marginal cost curve for new borrowers will be higher, resulting in more rationing. Similarly, a decentralized credit system, composed of regional branches and a strong interaction between the lender and borrowers, is likely to imply lower information costs than a centralzed credit system in which the exchange of information between the lender and borrwers is minimal. Since accurate information on expected revenue is costly to collect and analyze for each borrower, and since the ceiling has neutralized the interest rate as a screening device (Adams and Graham, 1981; Hoff and Stiglitz, 1990; Stiglitz and Weiss, 1991), it is assumed that the lender assesses a subjective probability of repayment, by using observable borrower characteristics that are independent of what the borrower claims.26 Thus, credit rationing is based on the use of non-price rationing criteria (Stiglitz and Weiss, 1981; Gonzalez-Vega, 1984). It is therefore assumed here that borrower characteristics, as recorded on loan applications, and components of loan contracts convey all the information the lender understands about the probability of full repayment ([1- F(Lj)]) by applicants. Rationing can, therefore, be formally expressed as: 26ln this regard, some lenders have classified default risk factors according to the five " C" of credit: character, capital, capacity, conditions and collateral(Koch,1988). Character refers to borrower’s honesty; capital refers to borrower’s wealth measured by financial soundness; capacity refers to the management expertise of the borrower to undertake the investment and repay the debt; condition refers to the economic environment, macroeconomic policies and sector specific incentives and profitability levels; and collateral is the lender secondary source of repayment in the case of default. While it is plausible that this information could be obtained in many developed countries, this type of information is almost non existent is RFMs. 71 RR±=h(borrower characteristics) ^22^ In the next section, the rationale for selecting the independent variables for the rationing ratio will be discussed, along with a precise specification of the model. 4.04 SPECIFYING THE EMPIRICAL MODEL A lender concerned with the viability of the DFI should set guidelines for screening and rationing credit, under the assumption that criteria that increase repayment should contribute to less rationing, while criteria that decrease repayment should contribute to more intense rationing. For example, the Board of Directors, the principal, may direct the management and staff, the agent, to use borrower information to: (a) reject some applications, because the risk of default is too high; (b) limit the size of the loan for approved applicants, in an attempt to reduce the amount of expected losses; (c) reveal the level of commitment by borrowers, by requiring equity contributions in the project, or requiring some minimum proportion of the loan to be covered by collateral; (d) select borrowers with a good repayment record and reduce its exposure to new applicants; (e) identify economic activities that are comparatively more profitable, or select locations/regions where the infrastructure for utilizing modem technology, marketing, and extension are in good working order; and 72 (f) negotiate loan contracts using (b) to (e). However, since the principal is not always aware of how the agent interprets the lending rules, and it is difficult to monitor the agent on a continuous basis, there is the possibility that the lender may be surprised at the repayment stage. Thus, a specification of this model may be given by: (23) RPR1=Xa+e2i where RRj is the rationing ratio; RPRj is the repayment ratio, defined as the loan principal actually paid, divided by the loan principal that is due and payable as specified in the contract; and X is a set of borrower characteristics chosen by the lender. The error terms are e, and % to be defined later. 4.05 EFFICACY OF THE SCREENING MECHANISM In order to evaluate the efficacy of the screening and rationing mechanism, the level of parameter significance and the sign in the estimation of the rationing ratio should be compared with the level of significance and sign in the estimation of the repayment equation. Significant parameters in the screening and repayment equations will reveal the correct identification of rationing criteria. Significance in screening, but not in repayment reflects a useless screening device. Significance in repayment, but not in screening reveals that the lender is ignoring useful information. A significant positive sign in the screening equation that is matched with a significant positive sign in the repayment 73 equation for the same variable will reveal that the screening process was successful in identifying creditworthy borrowers. A significant positive sign in the screening equation matched with a significant negative sign for the same variable in the repayment equation will reveal that the screen ing mechanism is flawed, attracting default prone-borrowers. A significant negative sign in the screening equation and a significant positive sign in the repayment equation will reveal that the mechanism is incorrectly rationing credit too strictly to creditworthy borrowers. Finally, significant negative signs in both equations will reveal that the lender is successful in identifying default-prone borrowers, and is rationing them more strictly. Creditworthy borrowers are defined as borrowers who repay their loans in full without arrears and satisfy their contractual obligations. Non-creditworthy borrowers are those borrowers who do not repay their loans in full and do not satisfy their loan obligations. The perception by the lender of who are creditworthy borrowers is revealed by all those who are categorized as class I borrowers. In this context, the estimated parameters should be significantly positive in both the rationing and repayment equations. These borrowers are identified in sector I of Table 12. Borrowers in sector III have been perceived by the lender to be higher credit risks, as the estimated parameter is negative, but the repayment performance has a positive parameter. Since the lender offered a smaller loan than requested, it can be inferred that the screening and rationing technology was inaccurate, offering smaller loans than these creditworthy borrowers were capable of repaying. Sectors II and IV are non-creditworthy borrowers. Borrowers in sector IV have been correctly identified as high-risk borrowers. 74 TABLE 12 DIAGNOSTIC MATRIX FOR EVALUATING THE SCREENING AND RATIONING MECHANISM LENDER’S CREDIT LENDER’S CREDIT HYPOTHESES ACTION; accept H. ACTION: reject H. (STATES) ex ante ex post ex ante ex Dost no rationing repayment rationing repayment H0: Creditworthy + ft + 0* -P. + a rp SECTOR I SECTOR III NO ERROR TYPE I ERROR (a) H,: Non- +0. -a* -P. -ct* Creditworthy SECTOR II SECTOR IV TYPE II ERROR (0) NO ERROR 0, = Screening mechanism(RR): if negative, more rationing; if positive, less rationing. orrp = repayment rationing(RPR): if negative,less repayment expected; if positive, more repayment expected. Borrowers in sector II are not creditworthy, as well, but the screening mechanism is flawed, producing a wrong diagnosis. Instead of being screened out of the market, these borrowers are incorrectly favored as creditworthy by the screening process. The proportion of significant parameters falling in each of these four sectors will indicate whether or not the screening and rationing mechanism is flawed. This result will have some important implications for the financial viability of the DFI. In particular, because the ex ante actions of the lender always include the likelihood of income and principal losses, the most critical credit rationing error is made when the lender approves 75 a loan for a non-creditworthy borrower.27 For example, assuming that the lender’s loan approval decision is based on the notion that the null hypothesis (HJ suggests that the borrower will repay the loan; and the hypothesis of rejection is the alternative hypothesis (H,) that the borrower will not repay, then a type II error is made whenever a loan is approved for a non-creditworthy borrower who does not repay. In this case, the estimated parameter in the rationing mechanism is positive but negative in the repayment equation. In contrast, a type I error occurs when the lender strongly rations credit to a creditworthy borrower, resulting in a loss of income (opportunity cost) for the lender. In this case, the estimated parameter in the rationing mechanism is negative and positive in the repayment equation (Table 12). If a borrower is creditworthy, then the lender must have a low probability of rejecting this loan request. Let this probability be a = P (Rejection | creditworthy) = P(-/3,|+an>); and /? = P(Approval | Non Creditworthy) = P(+/3,j-arp). These two probabilities determine how effective the lender is in processing the information the applicant provides and how efficient the lender is in minimizing the size of a and 0. Thus, the decision to approve or reject a loan request presupposes that the lender has some device which allows rational choices to be made. This device, hereafter called the diagnostic device, is an instrument that can be used to: (1) Examine the technology utilized by development banks to screen applications and ration credit to borrowers (RRj), in the expectation that they can increase the probability of full repayment [ 1 - F(Li)] or reduce loan default risk (F(L;)). 27This section is based on an adaptation of the methodology advanced by Learner (1984) in explaining hypothesis testing and type I and type II errors. 76 (2) Evaluate the efficacy of the screening technology and its impact on the financial institution, where the possibility exists that a disproportionate share of type I and type II errors are inadvertently committed by a lender that is constrained by information problems and regulatory restrictions, such as binding interest rate ceilings and targeted lending programs. (3) Identify specific variables that can be used to improve the selection of creditworthy borrowers. 4.06 CHOICE OF THE INDEPENDENT VARIABLES IN THE SCREENING MECHANISM Potential proxy variables to explain credit rationing, loan default and repayment, as specified in equation (22), may be selected from six categories, namely, indicators of economic activity, borrower characteristics, the regulatory environment, the lender’s procedures and loan contracts, the political economy, and the productive environment. The productive environment captures the impact of unexpected events that reduce the ability of borrowers to repay, such as pests, disease, floods, drought, and hurricanes (Donald 1976). The political economy refers to implicit promises made by politicians to borrowers that debt forgiveness would be extended in exchange for a vote cast in their favor. Due to data limitations, these two categories of potential default will not be included in this dissertation. The interested reader is referred to Donald (1976), Harris (1984), Khalily and Meyer (1992). Borrower characteristics may be sub-divided into two groups, wealth and demographic variables. Wealth variables are the values of land, livestock, machinery 77 and equipment, collateral and collateral substitutes, such as personal guarantees by a third party. Demographic variables are age, region, number of children, membership in organizations, gender, religion, land tenure. Indicators of economic activity are income, savings, equity contribution in the project, technology, type of activity, alternative or substitute interest rates, input and output prices, risk, and uncertainty. The regulatory environment refers to targeted lending programs, subsidized interest rates and high transaction costs, while the lender’s procedures refer to loan- processing time, managers’ lending authority, loan contract terms, and management rescheduling and refinancing decisions. The regulatory environment also provides credit- rating information, obtained through credit enquiries from other banks or financial institutions. Variables actually considered to explain rationing and repayment are borrower characteristics, such as credit experience (ce), gender, joint applications, net-worth (total assets - total liabilities), and equity divided by the size of the investment (E/I). Credit experience is represented by the number of loans the applicant had with GAIBANK prior to the loan examined in this dissertation. As the number of previous loans increases, the intensity of rationing should decline, since the lender has a stock of information about repayment behavior on which to base the loan decision. At the same time, borrowers with previous loans have a reputation to maintain if they are to continue receiving subsidized credit. Therefore, the sign on the estimated parameter should be positive in both equations (Table 13). TABLE 13 EXPECTED SIGNS IN THE MODEL VARIABLES RR RPR Credit Experience (+) (+ ) Year 1988 (-) (+ ) Year 1989 (-) (+) Year 1990 (+ ) (-) Year 1991 (+) (-) Region 2 (+) (+ ) Region 3 (-) (-) Region 4 (-) (-) Region 5 (-) (-) Region 6 (+) (+) Region 7 (-) (-) Region 9 (-) (-) Region 10 (-) (-) E/I (+) (+ ) Rice (+) (+) Sugar cane (+ ) (+ ) Foodcrop (-) (-) Livestock (-) (-) Fishing (+ ) (+ ) Industry (-) (-) Male (+ ) (+ ) Female (-) (-) Joint (+) (+) Networth (+ ) (+) IDB (-) (-) Gaibank (+ ) (+ ) Delay (-) (-) Grace period (+ ) (+ ) Collateral/demand (+) (+ ) (+ ) Implies less rationing in the RR equation. (-) Implies more intensive rationing in the RR equation. (+ ) Implies more repayment in the RPR equation. (-) Implies less repayment in the RPR equation. 79 Borrowers who provide increasing levels of equity relative to the size of the investment (E/I) should be less rationed and the estimated parameter should have a positive sign in both equations. This is in keeping with the notion that the borrower may be demonstrating more commitment to the investment and that borrower behavior is constrained by a deductible. The lender should be more willing to lend to joint applicants, because the cost of lending should be lower and collateral security should be more easily available for securing the loan. In some cases, it is observed at GAIBANK that the senior partner in a joint application provides the security and may have some credit experience, while the junior partner provides the manpower and skilled labor. This is similar to peer monitoring, in which the bank transfers some of the risk to the owner of the security (Stiglitz, 1990). Thus, it is expected that the estimated parameter in both equations should be positive. Net-worth is expected to have a positive sign in the rationing ratio equation. This result is expected, because wealthy borrowers are expected to be better rent seekers than less wealthy borrowers. Demographic information may also be included to explain rationing and repayments, representing the eight regions considered, namely, Regions 2 to 6, 7, 9, and 10. The expected sign on the parameters for Regions 2 and 6 should be positive in both equations, since the infrastructure, such as drainage and irrigation systems, marketing and milling, in these two regions is better maintained than in the other regions. Also these regions have access to technology used by producers of export crops. This implies 80 that there should be less rationing in Regions 2 and 6, and higher repayment than in the other regions. Seven economic activities, namely rice, sugar cane, livestock, food crops, fishing, other agriculture, and industry may be included to explain rationing and repayments. The signs on the parameters for sugar cane, fishing, and rice are expected to be positive in both equations, implying less intense credit rationing and higher repayments, principally because these activities are profitable and earn foreign exchange from exports, with sugar exports accounting for the largest net earner of foreign exchange in Guyana. Explanatory variables may also consider the source of funds for GAIBANK and the special program resourses of IDB and EIB funds to the industrial sector. Because of the elaborate screening procedures, and the fact that applicants may inflate their requests to ensure that they receive their desired levels of foreign exchange and subsidized loans, together with perception that the risk of default is high for targeted funds, it is expected that intense credit rationing will prevail and the sign on both parameters will be negative. The proportion of security to the loan amount (si) and the grace period (gp), as components of the loan contract are also included as explanatory variables. The estimated parameter should be positive for security, implying less credit rationing, as the risk of lending is perceived by the lender to be lower. Longer grace periods may contribute, on the other hand, to increased risk. Rationing by the length of time taken to process the application (delays) and the year when the application is processed are additional explanatory variables. Because of higher potential risk and a shortage of 81 information, the lender is expected to intensely ration credit to those borrowers whose applications were subject to a long processing time (dys). Thus, the sign expected for processing time is negative in both equations, as the lender rations credit by waiting. The sign expected for years 1990 and 1991 should be positive in the rationing equation, because of the greater availability of donor funds in the early nineties as compared to the late eighties. The use of less strict criteria to disburse funds should lead to negative signs in the repayment equation. In contrast 1987, 1988 and 1989 are expected to have negative signs for the rationing equation and positive signs for repayment, because of the shortage of donor funds. The screening criterion becomes observable through the sign and level of significance on each parameter, making, thereby, the implicit rationing behavior of GAIBANK evident. Of interest here is whether this mechanism is allocating more credit to borrowers who default than to borrowers who repay. If this is indeed the case, then the mechanism is flawed and it should be modified. The use of the diagnostic matrix will facilitate this examination. 4.07 CREDITWORTHY AND BORROWERS IN DEFAULT The most profitable assets in the loan portfolio of DFIs are borrowers who repay their loans without arrears. These borrowers are identified as creditworthy borrowers. Unfortunately, not much attention has been focused on them. These borrowers show, by their repayment records, that they value the service provided by the DFI. Thus, the lender should attempt to improve its customer relationships with this class of borrowers. 82 In order to examine this borrower class, a separation must be made between those borrowers who have repaid their loans without arrears as well as those that are current, from all other borrowers. Define the following: (1) non-creditworthy borrowers (ncwb) - these are all loans that had no repayments (full default), loans in arrears, loans that have been refinanced and rescheduled; (2) creditworthy borrowers (cwb) - these are all loans that have been repaid without arrears and all loans that are current in the portfolio. Thus, various versions of the repayment equation can be identified. For example, the estimation of the creditworthy equation (cwb), contrasted with all other borrowers, should produce the same signs on the parameters as in the RPRj equation. Specifically, it is expected that the estimated parameters representing credit experience, collateral/loan demand, regions 2, and 6, sugarcane, rice and fishing should be positive, indicating that as these variables increase, the likelihood that the borrower repays on time increases. On the other hand, when variables representing loans for IDB and EDB funds, time delays, grace period, year 1990 and 1991 increase, repayments should decrease and the sign on these parameters should be negative. 4.08 CONCLUDING REMARKS This chapter provided a theoretically plausible model of how the lender attempts to screen and ration credit to applicants in an environment of imperfect information and loan default costs. The screening and rationing mechanism has been identified as a function of two equations, namely, the screening and rationing equation, and the 83 repayment ratio equation. The necessary first-order conditions for a firm that maximizes expected risk-adjusted profits leads to the identification of a rationing ratio that is dependent on the expected repayment of principal. Proxy variables are utilized to gain an insight into the lender’s rationing technology and this, in turn, is examined against the repayment ratio and other behavior response equations of borrowers. The rationing ratio and the repayment ratio form a diagnostic device designed to examine the technology utilized by development banks to screen loan applications and ration credit to borrowers in the expectation that they can reduce loan default; evaluate the efficacy of the screening and rationing technology and its impact on the financial institution; and identify specific variables that can be used to enhance repayment and decrease default. A summary of the expected signs for the estimated parameters in the model is shown in Table 13. A positive sign in the rationing ratio suggests less rationing, as the variable increases, while a negative sign suggests the opposite. A positive sign in the repayment equation implies that repayment increases. A negative sign signals declining repayment. The next chapter presents the data used in this dissertation, while chapter VI deals with the empirical estimation and presents measures for the four sectors in Table 12 above. CHAPTER V SURVEY DESIGN, METHODOLOGY AND RESULTS 5.01 INTRODUCTION The data for this dissertation were drawn from borrowers’ files and accounting records, so that GAIBANK and borrower behavior could be investigated on a case by case basis. In order to randomly select the stratified sample, the loan application register for the 1987-1991 period was used as the initial source of information on applicants, since it represented the original access point for loans by borrowers. Tracking these loan applications through the system and over time revealed the status of clients categorized as approved or rejected applicants by the screening technology. Also, tracking approved applicants revealed whether they were borrowers who repaid in full their loan obligations and satisfied their loan contracts (creditworthy), or whether they were borrowers who were in default. This chapter will be divided into three sections. The sample design procedures used to draw the sample, and the survey instrument will be presented in the next section. The second section will contain a detailed description of the data, covering the number of loans disbursed by region, economic activity, type of borrower, source of funds, and loan size. This will be followed by information showing the processing time and experience of the borrower with GAIBANK and how this information impacts on the 84 85 financial technology. Finally, information will be shown on repayments, emphasizing the various categories of loan default as observed in the sample. The last section will present some conclusions drawn from the sample data. 5.02 SAMPLE DESIGN AND SURVEY INSTRUMENT Over the 1987-1991 period, GAIBANK received a total of 10,721 applications for loans in many different agricultural and small manufacturing, food and primary processing activities. Because of a financial constraint and the high cost involved in compiling loan information from a mannually maintained credit system, only a five percent sample was drawn, eqivalent to 536 loan applications. The sample was drawn using a stratified random sample design that is known to produce smaller errors than a simple random sample (Kish, 1965). The stratification is based on all ten regions in which GAIBANK provides credit services. Using the register of applications and dividing the total number of applications received by the sample size, this process generated the first application number in the sample. For example, if 5 was the random number selected, then the subsequent numbers in the sample were 25, 45, 65,...n. This process was repeated until the entire sample was drawn in accordance with the proportion of applications received, approved and rejected by region during the 1987- 1991 period. The measuring document prepared for this study was a survey instrument divided into three sections. The first section continued questions on borrower characteristics, such as the type of economic activity, land tenure, region, financial status and the 86 borrower credit experience with GAIBANK. The second section identified additional information GAIBANK used in processing the loan request through the screening technology. This information included the loan amount applied for, sources of funds from which the project could be financed, collateral security required, disbursement frequency, and the explicit transaction costs the borrower paid to the lender to process the application. The third section contained questions pertaining to the actual disbursements made by GAIBANK, the actual repayments made by borrowers, and the classification of the data by GAIBANK into indicators of arrears, default, not due, and fully repaid loans. Appendix I includes a copy of the survey instrument. In order to evaluate the effectiveness of the survey instrument, a test was conducted using a small sample of 32 applications, randomly selected from the register of applications received for the ten regions during the 1987-1991 period. After this information had been analyzed by the researcher, some questions were modified and the instrument was reorganized to match the flow of information in the files and the sequencing on how the information could have been extracted and compiled by several GAIBANK staff. The researcher next trained GAIBANK staff in the approach to extract the data from borrowers’ files and accounting records. This exercise to compile the data commenced in April, 1992 with GAIBANK credit officers extracting information for Section 1 of the survey instrument first. The survey instrument was next delivered to the Accounts Department, where financial data were recorded by a team of accounting staff. Thereafter, the survey instrument was passed to the Legal Department, to record 87 the type of collateral security accepted by GAIBANK for the loan. The survey instrument was checked for completeness and after it was prepared for preliminary coding of the data on computer, using Lotus. Final processing was completed in Columbus, Ohio, using the Quattro program. 5.03 SURVEY RESULTS Table 14 shows a distribution of the proposed and actual sample drawn and shortfall sustained. TABLE 14 SUMMARY OF THE DATA IN THE SURVEY (5 PERCENT SAMPLE). No. of Required Applications No. of Actual Applications No. of Shortfall Drawn YEAR Approved Rejected Total Approved Rejected Total Approve Rejected Total d 1987 85 17 102 62 17 79 23 0 23 1988 111 15 126 109 15 124 2 0 2 1989 99 17 116 95 17 112 4 0 4 1990 99 5 104 96 5 101 3 0 3 1991 74 14 88 74 88 14 0 0 0 TOTAL 468 68 536 436 68 504 32 0 32 SOURCE: Survey Data Of the 536 observations required by the sampling methodology, only 504 usable observations (94 percent) were obtained. Complete borrower information for the remaining 32 observations could not be obtained when the survey was conducted. This shortfall affected approved loans, with 70 percent of them (23 observations) from Region 88 2. Replacements could not be obtained, due mainly to logistical problems and resource limitations of the researcher.28 The sample of 504 applications is distributed over Regions 2 to 6, 7, 9, and 10 (Table 15). Observations from Regions 1 and 8 were not drawn because of the sample size and the small number of loans in these regions. Information examined at the bank suggested that Regions 1 and 8 had loan characteristics that were similar to those in other regions 2, 4, 9, and 10. Thus, the quality of the information was not severely compromised.29 Table 15 shows that the lender employs a screening mechanism to separate applications into two distinct classes of approval and rejection, in keeping with the notion that the expected probability of default for rejected applications are higher than for approved applications. Of the 504 applications received, 68 (13 percent) were rejected (screened out) and 436 were approved (87 percent), with only 24 approved applications being quantity rationed. Loan quantity rationing is defined by the ratio of the loan amount approved (Lj) divided by the loan request received (Dj). This result may indicate the weakness of the screening and rationing mechanism by GAIBANK, as too many unprofitable projects are being financed. Region 2 was dominant in terms of the number of applications received, approved, and rejected. The highest proportion of applications were rejected in Region 7 (38 percent) and Region 3 (23 percent). MIt should be observed that the current filing system is manually maintained and is no longer effective in tracking the records of borrowers and providing timely information. Moreover, with many borrowers having the same name and perhaps located in the same region and engaging in the same economic activity, as in the case of rice production, borrower files are easily misplaced or correspondence misfiled. A computerized records system will resolve many of these problems. 29 The economic activities in most of these regions are relatively the same, differing only by scale in some instances. TABLE 15 NUMBER OF APPLICATIONS RECEIVED, APPROVED, AND REJECTED BY REGION IN THE SAMPLE, 1987-1991. YEAR 1987 1988 1989 1990 1991 TOTAL 1987-1991 PERCENT 1987-1991 REGIONS REC APP REJ REC APP REJ REC APP REJ REC APP REJ REC APP REJ REC APP REJ REC APP REJ 2 22 IS 7 46 40 6 48 40 8 46 44 2 44 40 4 206 179 27 41 41 40 3 7 5 2 20 15 5 10 8 2 11 9 2 9 7 2 57 44 13 11 10 19 4 9 7 2 12 10 2 11 10 1 12 12 0 10 8 2 54 47 7 11 11 10 5 12 11 1 17 16 1 18 14 4 14 14 0 10 7 3 71 62 9 14 14 13 6 14 14 0 18 17 1 15 15 0 13 12 1 14 12 2 74 70 4 15 16 6 7 1 0 1 3 3 0 3 1 2 1 1 0 0 0 0 8 5 3 1 1 5 9 12 9 3 6 6 0 4 4 0 2 2 0 1 0 1 25 21 4 5 5 6 10 2 1 1 2 2 0 3 3 0 2 2 0 0 0 0 9 8 1 2 2 1 TOTAL 79 62 17 124 109 15 112 95 17 101 96 5 88 74 14 504 436 68 100 100 100 SOURCE: Survey Data; REC: Received; APP: Approved; REJ: Rejected 90 Shown in Figure 6 is a brief summary of the data, reflecting the screening process and the responses by borrowers once the loans become active in the loan portfolio. Also, Figure 6 illustrates the distribution of active loans that have been fully repaid with and without arrears, refinancing and rescheduling, loans in default, and current loans. For the latter, the distribution of active loans considers a category for no repayments, and for all other loans. A detailed analysis of the active loans is provided below. 504 applications Screening Rationed 24 (5.5%) Non- No. Approved No. Rejected Rationed 436 (87%) 68 (13%) 412 (94.5%) Not Due Loan Portfolio 407 (93%) No Repayments When Due Full Default 23 (6%) Refinanced Current Rescheduled 78 (18%) No Arrears Arrears 18 (4%) 226 (53%) 85 (20%) FIGURE 6: SAMPLE DISTRIBUTION AND CREDIT RATIONING 91 5.04 DISTRIBUTION BY TYPE OF BORROWER Borrowers in GAIBANK credit programs consist of five categories, namely individuals applying for credit as single applicants (male and female), joint applicants, groups of individuals, and private or public companies (Table 16). The majority of individual borrowers were male, accounting for 310 approved applications (72 percent) and 80 percent of the disbursement valued at G$ 12.4 million, with an average loan size of G$40,000.30 TABLE 16 NUMBER, AMOUNT AND AVERAGE LOAN SIZE BY TYPE OF BORROWER FOR THE SAMPLE OF LOANS DISBURSED, 1987-1991. Volume of No. of Loans Average Type of Borrower Disbursement Size N % GSM % Male 310 72 12.4 79.5 40,000 Female 13 3 0.1 0.5 6,154 Joint 103 24 2.9 18.5 28,155 Group/Company 4 1 0.2 1.4 55,000 TOTAL 430 100 15.6 100.0 36,279 SOURCE: Sample data. There were 103 joint applications, mainly relatives and not necessarily married couples, but father/son combinations who were engaged in fishing and rice production. They received 18 percent of the total disbursement valued at G$2.9 million, in contrast to 13 female applicants and 4 groups/companies who received less than 2 percent of the total disbursement. Some reasons for joint applications are to improve the quality of the 30Exchange rate, 1991: US$1 =G$100.00 92 collateral offered, or to establish new applicants as borrowers. These are indications of the third-party guarantee or collateral substitutes usually found in rural financial markets. An important difference between female borrowers and other borrowers is that female borrowers receive loans of average size six times smaller than male borrowers and eight times smaller than group/company loans. Group/company loans were the largest disbursed by the lender. 5.05 DISTRIBUTION BY TYPE OF INVESTMENT The types of activities financed by GAIBANK may be classified into export and domestic activities. The export activities are rice, sugar cane, and fishing. The domestic activities are food crops (cassava, plantains, yams, eddoes), other agriculture (pineapples, tomatoes, melons, vegetables, peanuts), livestock (poultry, beef, dairy, and pork), and industry (metal, garment, woodworking, and rice milling). Most of the loans disbursed by GAIBANK were for rice production (317 loans) valued at G$7.5 million, accounting for 48 percent of the disbursements, while industry accounted for only 7 loans but 38 percent of the disbursements, valued at G$5.86 million. This concentration reflects the skewed distribution usually observed in rural credit programs (Ladman, 1984; Gonzalez-Vega, 1984), where a few large borrowers receive a large share of the loan amounts. In this sample, industrial borrowers received loans of average size 23 times larger than the average loan size received by all other borrowers. Disbursements for fishing was the second largest activity, but it was still below the average amount disbursed in the sample (Table 17). 93 TABLE 17 NUMBER, AMOUNT AND AVERAGE LOAN SIZE BY TYPE OF INVESTMENT FOR THE SAMPLE OF LOANS DISBURSED 1987-1991. Volume of No. of Loans G$ Type of Investment Disbursement Average N % GSM % Size Rice 317 74 7.50 48.0 23,659 Sugar cane 2 1 0.05 0.3 25,000 Food Crops 14 3 0.15 1.0 10,714 Livestock 33 7 0.70 4.5 21,212 Fishing 35 8 1.00 6.4 28,571 Other Agriculture 22 5 0.30 1.9 13,636 Industry 7 2 5.86 37.9 837,143 TOTAL 430 100 15.60 100.0 36,279 SOURCE: Sample data. 5.06 DISTRIBUTION BY REGION Table 18 shows the number and amount of loans disbursed by region. Region 2 had the largest number of loans in the loan portfolio (41 percent), followed by Region 6 and 3. In contrast, Region 4, with only 46 loans (11 percent), had the largest average size of loan. This distribution is explained by the high concentration of industrial loans in Region 4, given the availability of support infrastructure for manufacturing, as against the limited availability of infrastructure in the rural agricultural areas such as Regions 2, 5 and 6. 94 TABLE 18 NUMBER, AMOUNT AND AVERAGE LOAN SIZE BY REGIONS FOR THE SAMPLE OF LOANS DISBURSED, 1987-1991. Volume of No. of Loans G$ Regions Disbursement Average N % GSM % Size 2 178 41 3.70 24.0 20,787 3 44 10 0.94 6.0 21,364 4 46 11 6.34 41.0 137,826 5 59 14 2.11 14.0 35,763 6 69 16 1.74 11.0 25,217 7 5 1 0.58 3.0 116,000 9 21 5 0.13 0.8 6,190 10 8 2 0.06 0.2 7,500 TOTAL 430 100 15.60 100.0 36,279 SOURCE: Survey Data 5.07 DISTRIBUTION BY SOURCES OF FUNDS GAIBANK received loans from the InterAmerican Development Bank (IDB) and the European Investment Bank (EIB), and equity from the Government of Guyana. In the sample, 98 percent of the number of loans were disbursed from GAIBANK equity from the Government of Guyana for agricultural activities, accounting for 63 percent of the total disbursements of G$9.8 million. However, the average loan size was only $23,168 from GAIBANK sources, while loans from the InterAmerican Development Bank had an average size of G$1.4 million and loans from the European Investment Bank averaged G$33,333. The international funds are mainly disbursed for industrial activities, while GAIBANK funds are mainly disbursed for agriculture (Table 19). 95 TABLE 19 NUMBER, AMOUNT, AND AVERAGE LOAN SIZE BY SOURCE OF FUNDS. Volume of No. of Loans Source of Funds Disbursement Average Size N % GSM % GAIBANK 423 98 9.8 63 23,168 IDB 4 1 5.7 36 1,425,000 EIB 3 1 0.1 1 33,333 TOTAL 430 too 15.6 100 36,279 SOURCE: Survey Data 5.08 DISTRIBUTION BY TERM STRUCTURE GAIBANK categorizes loans into three term structures, namely short-, medium-, and long-term loans. Short-term loans are supplied to borrowers for periods up to 18 months or less and tied to the production and marketing cycle. For example, short-term rice loans have maturity dates of 180-200 days. Medium-term loans are loans with maturities between 18 to 60 months, mainly used for the purchase of intermediate inputs and small equipment. Long-term loans are provided for major investments in infrastructure, machinery, and technical services. The majority of the loans in the sample (77 percent) were short-term loans valued at G$12.1 million (78 percent). This supports the hypothesis of Bourne and Graham, 1984, that short-term loans form a major portion of the loan portfolio of DFIs. Long term loans are the largest loans provided by the lender, averaging G$ 100,000 (Table 20). 96 TABLE 20 NUMBER, AMOUNT AND AVERAGE LOAN SIZE BY TERM STRUCTURE FOR SAMPLE DISBURSED, 1987-1991. Volume of No. of Loans Disbursement Average Size N % G$ % Short-Term 332 77 12.1 77.8 36,446 Medium 91 21 2.8 17.9 30,769 Long-Term 7 2 0.7 4.3 100,000 TOTAL 430 100 15.6 100.0 36,279 SOURCE: Survey Data 5.09 DISTRIBUTION BY LOAN SIZE Loans approved during the 1987-1991 period in the sample were classified into three categories. The smallest category of loans, below G$30,000, accounted for 80 percent of the number of loans, reflecting the typical loan size in the portfolio but only 20 percent of the disbursement, valued at G$3.2 million. In contrast, the largest loans in the sample, of over G$100,000, accounted for 6 percent of the total number of loans (24 loans) and 59 percent of the disbursements. The medium-size category, between G$30,000 and G$100,000, contained 14 percent of the number of loans, valued at G$3.2 million. These results reflect, once again, the skewed distribution previously described (Table 21). 97 TABLE 21 NUMBER, AMOUNT AND AVERAGE LOAN SIZE BY LOAN SIZE CATEGORY, 1987-1991. Volume of No. of Loans Average LOAN SIZE Disbursement Size N % G$M % Large: G$> 100,000 24 6 9.2 59 383,000 Medium: G$30,000-100,000 59 14 3.2 21 54,237 Small: G$< 30,000 347 80 3.2 20 9,222 TOTAL 430 100 15.6 100 36.279 SOURCE: Survey Data 5.10 DISTRIBUTION OF LOANS IN THE SAMPLE, 1987-1991 Between 1987-1988, the number of loans increased rapidly, moving from 62 to 109 loans. Thereafter, the number of approved loans declined, reaching a level of 69 loans in 1991. At the same time, however, the average size of loans increased from G$12,903 in 1987 to G$143,478 in 1991, when 63 percent of the total disbursements of G$9.9 million were made. The average loan size in 1991 was 3.9 times larger than the average loan size for the period, signifying that the lender was concentrating on supplying large loans and at the same time reducing the number of loans approved for small borrowers (Table 22). 98 TABLE 22 NUMBER, AMOUNT AND AVERAGE LOAN SIZE BY YEARS, 1987-1991. Volume of No. of Loans Average YEAR Disbursement Size N % G$ % 1987 62 14 0.8 5 12,903 1988 109 25 1.5 10 13,761 1989 94 22 1.9 12 20,213 1990 96 23 1.5 10 15,625 1991 69 16 9.9 63 143,478 TOTAL 430 100 15.6 100 36,279 SOURCE: Survey Date 5.11 LAND TENURE AND ITS IMPLICATIONS FOR LENDING There are five categories for classifying lands used by farmers in investments financed by GAIBANK. These are: Transported Lands: These are lands that are privately owned by individuals either singularly, jointly or severally as is the case of block leases. Transported land establishes clear property rights and ownership is recorded in the Deeds Registry, a section of the national court system. Private Lease Lands: These are lands that have been leased by a lessor to a lessee, the farmer. In this arrangement, the lessor agrees and records in the Deeds Registry his permission to grant the use (not ownership) of such lands from an existing transport owned by the lessor. 99 State Lease Lands: These are public lands that have been granted to private individuals by the President for 99 years. These lands, however, are not recorded in the Deeds Registry, but at the Ministry of Agriculture. Registered Land: This type of ownership is established through the Lands and Surveys Department, Ministry of Agriculture, and is supported by the Land Registry Act, Chapter 5:02; but this type of land is not recorded at the Deeds Registry. These lands could be mortgaged to secure a loan but this arrangement is not as strong as is the case of transported lands. Rented Land: This is the most common arrangement between land owners and renters. A tacit or written approval is given to the farmer renting the land from the own er. The renter has no control over changing or improving the state of the land. In the survey conducted for this study, it was revealed that over 41 percent of the land (6,251 acres) on which credit was approved were state lands (these are lands owned by the government), with another 30 percent (4,596 acres) being categorized as rented lands owned by private individuals (Table 23). Rented lands can preclude any long-term investment by tenant farmers in drainage and irrigation facilities required to improve yields and to adopt new technologies. Therefore, unless the owner (government or private owner) shows some willingness to make these improvements, no real land development works can take place on these lands. In contrast, transported lands and private lease lands are more easily managed and could be sold because of the ownership arrangements. However, these two categories make up less than 30 percent (3,963 acres) of the total lands in the survey, and are smaller 100 than the amount of rented lands (4,596 acres). This perhaps could be one explanation why many more short-term loans for production credit are provided by GAIBANK as against long-term loans for land development works. Regularizing these ownership arrangements by issuing more transported land titles, and consolidating and making a single government agency responsible for conducting these and other related asset ownership and title transfer concerns, may improve effective long-term loan demand and enhance the prospects for better collateral security in financial markets. The current arrangement is definitely not efficient for agricultural development, as formal financial institutions are at a disadvantage when collateral security is required to conclude loan contracts. TABLE 23 DISTRIBUTION BY LAND CATEGORIES AND ACRES: 1987-1991 1987 1988 1989 1990 1991 TOTAL % Transported Land 204 627 464 660 270 2,225 15 Private Leased Land 74 813 454 132 265 1,738 11 State Leased Land 1,362 1,598 1,281 906 1,104 6,251 41 Rented Land 496 1,108 801 1,285 906 4,596 30 Registered Land 42 49 120 127 142 480 3 TOTAL 2,178 4,195 3,120 3,110 2,687 15,290 100 SOURCE: Survey Data. 5.12 COLLATERAL SECURITY BY TYPES OF INSTRUMENT Six categories of assets have been accepted as collateral security on loans. These are: assignment of sale proceeds; third-party guarantee; debenture; deposit of title documents with the lender; mortgage; and a charge on assets. 101 With the exception of the charge on assets, all the other security instruments are biased in favor of the borrower, having high transaction costs for the bank in the event of loan default. For example, the assignment of sale, which is an agreement between GAIBANK, the borrower and the buyer of the product produced by the borrower, is not legally binding on the buyer or the borrower. Thus, it is not uncommon to find that the assignment of sale, designed to enhance collections, is not efficient in delivering repayments. Borrowers and buyers collude and together withhold repayments from the lender. The third-party guarantee system, which makes someone else other than the borrower responsible for the debt, does not work efficiently, because of the difficulty in monitoring the status of the assets of the guarantor, especially moveable assets. A similar difficulty exists when a debenture (a charge over the company’s assets as evidence of a debt) is established. Without the consent of the bank, the borrower can also conduct other financial transactions using the same assets, depending on whether a fixed or floating charge has been organized. The deposit of title documents with the lender in order to restrain the owner from disposing of the asset is another approach used to deal with the problem of securing loans. This type of asset taken as collateral security is also ineffective in compelling repayment. For instance, in the event of loan default, the bank still has to approach the court system and obtain judgement against the borrower, before seeking another court order for the sale of the asset. This could be a long exercise, as there are no specialized courts dealing with financial matters. Similar difficulties also exist with mortgages, where 102 in the event of loan default the process through the court is long, with very high transaction costs for the lender. Also, there is no guarantee that the judgement will be efficacious in compelling repayment by the borrower. A charge on assets, in contrast, has lower transaction costs, higher enforcement powers and compels repayment expeditiously. A charge on assets is a financial claim on a borrower’s house, vehicle, land or other assets by the lender. This charge is only effective if it is registered at the Deeds Registry. In the event of loan default, the bank can proceed with immediate seizure of the assets and arrange a sale for the asset to retire the debt owed. There are disadvantages to the charge on assets, however. First, assets that are defined in a charge are still in the possession of the borrower, who could depreciate its value by poor maintenance or poor management practices. In another case, a vehicle over which a charge has been registered could be made non-marketable after an accident. Here the value of the vehicle depreciates dramatically and perhaps an insurance claim might be insufficient to cover the debt. A charge on some assets could also be made obsolete and valueless by a change in technology. A house over which a charge has been effected could be destroyed by fire and, once again, insurance coverage might be inadequate. Similarly, livestock over which a charge is made could be stolen or destroyed by disease. Quite obviously, moral hazard problems exist in all of these situations. 5.13 BORROWER BALANCE SHEET, 1987-1991 A balance sheet was constructed for the average borrower in the sample in order to understand how assets and liabilities are related for those borrowers applying for loans at GAIBANK and also to determine the direction of the net flow of funds in rural areas (Table 24). TABLE 24 BALANCE SHEET FOR A BORROWER, USING THE AVERAGE VALUES REPORTED IN THE LOAN APPLICATION FORM G$ 1987-1991. ASSETS 1987 % 1988 % 1989 % 1990 % 1991 % 1. Cash 2.6 2 6.4 3 13.3 2 15.8 2 76.1 4 2. Savings Account 10.1 6 10.4 4 12.5 2 20.7 3 101.1 6 3. Crops/Livestock 16.4 11 28.5 12 58.0 10 62.6 10 237.5 12 4. Inventory 1.3 1 18.5 8 26.7 5 35.6 6 196.5 11 S. Accounts Receivable 5.2 3 5.6 2 21.7 4 44.5 7 84.3 5 6. Machinery and Equipment 55.6 36 65.3 27 183.9 33 209.9 34 613.6 34 7. Land and Buildings 63.2 41 108.0 44 245.5 44 243.5 38 515.9 28 8. Total Assets 154.4 100 242.7 100 561.6 100 632.6 100 1825.0 100 LIABILITIES AND NET WORTH 9. GAIBANK Debt 2.5 2 7.4 3 14.4 3 19.8 3 86.3 5 10. Other Bank Debt 0 0 10.4 4 12.5 2 14.4 2 22.7 1 11. Other Liabilities 22.5 14 38.7 16 21.4 4 54.6 9 90.9 5 12. Net Worth 129.4 84 186.2 77 513.3 91 543.8 86 625.0 89 TOTAL LIABILITIES AND NETWORTH 154.4 100 242.7 100 561.6 100 632.6 100 1825.0 100 SOURCE: Survey Data. The borrower on the average had a diversified asset base with cash, saving accounts and accounts receivable being approximately 11 percent of total assets, during the 1987-1991 period. Inventory and crops/livestock averaged 17 percent, while machinery, equipment, land and buildings averaged 72 percent of total assets over the 104 period. GAIBANK debt averaged 3 percent of total liabilities and net worth, while other bank debt averaged 1.8 percent over the 1987-1991 period. Another feature shown in the balance sheet is that debt owed to GAIBANK is larger than the debt owed to commercial banks. But more significant is the observation that commercial banks appear to be moving savings out of the agricultural sector and into non-agricultural activities. This is revealed by the declining percentage of other bank debt (line 10) owed to commercial banks as compared to the increasing savings accounts in commercial banks (line 2) from these same borrowers. Finally, the debt owed to GAIBANK by borrowers is relatively small, ranging between 2 to 5 percent of the total liabilities and net worth. This result suggest that perhaps the GAIBANK debt is not a senior claim in the liability structure of borrowers; leading, thereby, to the high arrears and default problem at GAIBANK. 5.14 ANALYSIS OF THE FINANCIAL TECHNOLOGY The following is a summary of the important variables related to the financial technology used to process loans, contract terms, and the results from the exercise (Table 25). These variables are: Grace period (GP) - This is the length of time in days the bank writes into loan contracts, reflecting the period between the last date of disbursement to the date that the loan becomes payable. This variable varies from region to region, by source of funds, and by the type of investment. 105 TABLE 25 CREDIT EXPERIENCE, GRACE PERIOD, PROCESSING TIME, COLLATERAL TO LOAN DEMAND(SL), LOAN APPROVED TO LOAN DEMAND (RR), AND EQUITY (G$) BY REGION, SOURCE OF FUNDS, AND INVESTMENT TYPES (AVERAGE), 1987-1991. Average Region GP DYS S/L CE RG3 RR RPR 2 182 45 26 4.2 7,576 1.00 0.99 3 220 74 34 2.0 10,749 0.96 0.64 4 153 72 23 2.2 21,581 1.00 0.21 5 238 86 15 1.5 7,693 0.97 0.57 6 199 61 15 3.6 16,310 1.00 0.69 7 287 121 7 0.4 30,686 0.86 0.10 9 212 103 3 0.8 1,473 1.00 0.31 10 354 92 14 0.0 2,750 0.66 0.17 Source of Funds GAIBANK 199 63 22 3.1 9,204 0.99 0.86 IDB 166 107 16 0.3 165,772 0.90 0.35 EIB 215 36 4 2.0 14,452 1.00 0.72 Investment Type Rice 201 50 26 4.0 10,112 1.00 0.93 Sugar cane 369 168 15 1.0 7,978 1.00 0.20 Food Crop 294 107 6 1.0 2,468 0.86 0.40 Livestock 229 89 15 1.0 2,413 1.00 0.56 Fishing 92 105 13 1.0 14,120 1.00 0.63 Other Agriculture 232 106 5 1.0 2,881 0.80 0.14 Industry 187 78 11 1.0 100,920 0.95 0.03 No. of Loans 430 430 430 430.00 430 430.00 430.00 Mean 63.42 0.98 10,697 199.13 3.05 21.81 0.84 Standard Deviation 58.48 0.07 29,338 92.48 3.36 40.17 0.32 Coefficient of Variation 0.92 0.07 2.740 0.46 1.10 1.84 0.38 Minimum 0.00 0.40 0.000 0.00 0.00 0.00 0.00 Maximum 397.00 1.00 0.4E6 610.00 25.00 525.00 1.00 GP: Grace period; CE: Credit Experience; DYS: Processing time in days; RG3: Equity; S/L: Collateral/Loan Demand; RPR: Principal Actually Paid/principal due and payable at 12/31/91: RR: Rationing Ratio. 106 Processing time (dys) - This is the length of time in days that the bank takes to process a loan application from the date the application is taken to the date of first disbursement. Long processing times reflect a shortage of information that slows down the screening process. Credit experience (ce) - This variable represents the credit history of a particular borrower who had previous loans from GAIBANK. As the credit history increases, measured by the number of previous loans, more information on the credit capacity of the borrower is revealed to the lender during the screening process. Equity (RG3) - This variable identifies the equity contribution that the borrower invests in the project along side the loan considered in the project. Rationing Ratio (RR) - This is the quantity rationing ratio previously described. Repayment Ratio (RPR) - This is repayment ratio of principal actually paid divided by the amount of principal due and payable at the end of 1991. Collateral/Loan Demand (S/L) - This represents the value of security divided by loan demand, and is a measure of the coverage the lender takes when the loan is approved. Table 25 shows the relationship between key loan features and the regions, source of funds, and type of investment. In the sample, borrowers in Region 2 on the average showed highest number of previous loans received (4.2) and the shortest processing time (45 days) for screening applications. Similarly, rice loans were associated with borrowers with the longest credit experience (4 previous loans), the shortest processing time (50 107 days), and an amount of coverage of collateral to loan demand (S/L) of 26 times. The major reason for such a high security ratio is the total value of some offered assets as collateral that cannot be divided into small units, commensurate with the size of the loan. By sources of funds, GAIBANK loans reflected borrowers with the longest credit experience (3.1 loans) and the second shortest processing time. Donor loans usually require special procedures. Borrowers in Region 7 showed the longest processing time (121 days) and the second shortest credit experience. Borrowers receiving funding from the InterAmerican Development Bank line of credit had the shortest credit experience and the longest processing time (107 days). By investment type, credit experience was low for all activities, excluding rice, while sugar cane loans experienced the highest number of days for processing (168 days). The main implication from these results is that rice loans from borrowers with a long credit history (borrowers in Region 2 and 6) were favored by the credit system, which processed these applications in the shortest time. Further, these borrowers were not quantity rationed and they, in turn, reciprocated by repaying their loans better than other borrowers in other regions. A second implication is that most new borrowers should be quantity rationed, since a short credit experience implies a higher probability of low repayment rates. Third, the time required to process applications suggests that credit screening is slow, averaging over 100 days to process applications from Regions 7 and 9, and over 150 days for sugar cane. Finally, extended grace periods and processing times to suggest that these borrowers should also be quantity rationed, as repayments tend to be low in these cases. 108 Comparatively high coefficients of variation were recorded for equity, credit experience and the ratio of collateral to loan demand. These results imply that the loan portfolio consists of a heterogenous group of borrowers and, therefore, the lender should have a financial technology that is flexible enough to utilize this information efficiently. The rationing ratio, on the other hand, ranges between 0.4 and 1. 5.15 REPAYMENT PERFORMANCE OF LOANS GRANTED, 1987-1991 This section presents a detailed analysis of the repayment performance of approved loans in the sample by GAIBANK during 1987-1991. Six categories have been identified for classifying repayments, namely: Full Default: These are 23 loans for which no repayments were received when their installments became due and payable. This is hard core default that may cause insolvency. Current Loans: These are 78 loans not yet fully due, but for which the principal due and payable have been covered without arrears at December 31, 1991. Repaid with No Arrears: These are 226 loans that were repaid in full without arrears or being rescheduled or refinanced. These loans represent the best loans in the portfolio and the prime assets of the lender. Repaid with Arrears: These are 85 loans that were repaid in full but had installments that were paid later than the due date. Arrears cause liquidity and cash flow management problems that may have high opportunity costs, especially for 109 subsidized credit projects. Arrears also indicate the beginning of serious default problems. Refinance and Reschedule: These are 18 loans for which the repayment period and the loan amount may have had to be extended because of several problems. Some of these are delays in the supply of inputs, cost increases and unforeseen events, such as losses due to floods or pests. Refinancing and rescheduling is a management decision taken after additional information is available and should only be used when sufficient information can be gathered to verify more accurately, the likely outcome of repayments. Otherwise, it may be a way for management to hide arrears and default problems, as the number of loans placed in this category increases. Not Due: Refers to loans that have been disbursed but were not due for repayment by the end of 1991. Thus, nothing can be said about their repayment responses. 5.16 LOAN DEFAULT AND REPAYMENT STATUS Tables 26 and 27 show two distributions of the repayment status by number and value of the disbursements allocated by region. Of the 430 disbursed loans, only 5 percent of the number (23) were in full default, but these loans accounted for 38 percent of the total value of disbursements (G$15.6 million). This is a substanmtial proportion, that threatens the bank. More importantly, in Region 4, with only 3 loans in this default category, over 80 percent of the total value was in default (G$5.2 million). Region 9 showed the second highest proportion of the portfolio in default. 110 TABLE 26 REPAYMENT STATUS OF THE NUMBER OF LOANS IN THE SAMPLE DISBURSED BY REGION, 1987-1991 Disburse Repaid with Refinanced Full Current Repaid ment Arrears and Default Without Region Rescheduled Arrears No. No. % No. % No. % No. % No. % 2 178 26 15 3 2 0 0 2 1 147 82 3 44 14 32 1 2 3 7 14 32 12 27 4 46 15 33 2 4 3 7 10 21 16 35 5 59 11 19 5 8 4 7 22 37 17 29 6 69 12 17 7 10 8 12 10 14 32 46 7 5 1 20 0 0 0 0 4 80 0 0 9 21 2 9 0 0 5 24 12 57 2 10 10 8 4 50 0 0 0 0 4 50 0 0 TOTAL 430 85 20 18 4 23 5 78 18 226 53 SOURCE: Survey Data TABLE 27 LOAM REPAYMENTS IN THE SAMPLE DISBURSED BY REGION, GSM, 1987-1991 Volume Repaid Refinanced Full Default Current Repaid Disbursed with and Without Region Arrears Rescheduled Arrears % % % % % 2 3.70 0.62 17 0.04 1 0.00 0 0.24 6 2.800 76 3 0.94 0.30 32 0.06 6 0.40 4 0.06 6 0.480 51 4 6.34 0.49 8 0.08 1 5.20 82 0.30 5 0.270 4 5 2.11 0.31 15 0.23 11 0.63 30 0.85 40 0.090 4 6 1.74 0.27 16 0.19 11 0.15 9 0.46 26 0.670 38 7 0.58 0.46 79 0.00 0 0.00 0 0.12 21 0.000 0 9 0.13 0.06 46 0.00 0 0.05 38 0.02 15 0.003 1 10 0.06 0.04 67 0.00 0 0.00 0 0.02 33 0.000 0 TOTAL 15.6 2.56 16 0.60 4 6.07 38 2.07 14 4.313 28 SOURCE: Survey Data I l l At the same time, 226 loans had been repaid in full without arrears (S3 percent of the total number), but represented only 28 percent (G$4.3 million) of the total value disbursed. These results suggest that borrowers who receive small loans are more creditworthy than borrowers who receive large loans. Table 26 and 27 also show that Region 2, which had no loans in full default, showed the highest proportion of the number of loans (82 percent) that were repaid without arrears, as well as the largest value repaid without arrears (76 percent). Region 10 had a poor repayment record, where 50 percent of the number of loans were repaid with arrears, and no loans were repaid without arrears, although no default was observed either. Full default was particularly important in Region 9, where arrears were comparatively less important. 5.17 REPAYMENT BY TYPE OF INVESTMENT Of the total number of loans disbursed, 23 loans (5 percent) were in full default, accounting for 39 percent of the total portfolio (Tables 28 and 29). This situation was largely due to the poor repayment performance of industrial loans among which only 2 loans but representing 89 percent of the amount disbursed (G$5.2 million) were in full default. Although no more than 10 percent of the disbursement value for rice and other agriculture was in full default, a relatively high proportion of the repayments of these loans was made with arrears. In particular, 50 percent of the disbursement to other agriculture and 28 percent for rice were repaid with arrears. Also, 33 percent of the amount disbursed to livestock were refinanced and rescheduled. 112 TABLE 28 REPAYMENT STATUS BY LOANS IN THE SAMPLE BY NUMBER AND AMOUNT DISBURSED BY TYPE OF INVESTMENT, 1987-1991 Repaid Repaid with Refinanced and Full Default Current Without Type of Volume Arrears Rescheduled Investment Disbursed Arrears %%%% % Rice 7.50 2.08 28 0.22 3 0.69 9 1.15 15 3.36 45 Sugar cane 0.05 0.05 100 0.00 0 0.00 0 0.00 0 0.00 0 Food Crops 0.15 0.03 20 0.02 13 0.06 40 0.03 20 0.01 6 Livestock 0.70 0.11 16 0.23 33 0.08 11 0.04 6 0.24 34 Fishing 1.00 0.14 14 0.13 13 0.00 0 0.40 40 0.33 33 Other Agriculture 0.30 0.15 50 0.00 0 0.03 10 0.07 23 0.05 17 Industry 5.86 0.00 0 0.00 0 5.21 89 0.38 6 0.27 5 TOTAL 15.62 2.56 16 0.60 4 6.07 39 2.07 13 4.30 28 SOURCE: Survey Data TABLE 29 REPAYMENT STATUS BY LOANS IN THE SAMPLE BY NUMBER DISBURSED BY TYPE OF INVESTMENT, 1987-1991 Repaid with Refinanced and Full Default Current Without Type of Number Arrears Rescheduled Arrears Disbursed % % % % % Rice 317 57 17 12 4 13 4 29 9 206 65 Sugar cane 2 2 100 0 0 0 0 0 0 0 0 Food Crops 14 2 14 1 7 1 7 8 57 2 14 Livestock 33 4 12 2 6 1 3 15 45 11 33 Fishing 35 18 51 3 9 0 0 10 29 4 11 Other Agriculture 22 2 9 0 0 6 27 13 59 1 5 Industry 7 0 0 0 0 2 29 3 42 2 29 TOTAL 430 85 20 18 4 23 5 78 18 226 53 SOURCE: Survey Data 113 Some of the late repayments in rice were directly related to milling and marketing difficulties of the Government rice milling and marketing authority, which experienced technical and cash flow problems during the late 1980s and early 1990s. Similar difficulties were experienced by the sugar cane borrowers who sold their production to the Government-owned sugar cane processing company that had cash flow management problems. 5.18 REPAYMENT STATUS BY TYPE OF BORROWER Fifteen percent of the number of loans, accounting for 60 percent of the value of loans disbursed to female borrowers, were in full default (Table 30). In contrast, 5 percent of the number of loans, representing 47 percent of the amount disbursed to male borrowers, were in full default. Additionally, female borrowers were more likely to make late payments than male borrowers. Some 36 percent of the value disbursed to female borrowers, accounting for 23 percent of the number of loans, were repaid with arrears. Male borrowers on the other hand, had a slightly better arrears repayment record than females ( 22 percent of the number of loans disbursed, representing 14 percent of the value). 114 TABLE 30 REPAYMENT STATUS OF LOANS IN THE SAMPLE DISBURSED BY TYPE OF BORROWER, 1987-1991 Refinanced Repaid Repaid with Full and Current Without Type of Volume Arrears Default Borrower Disbursed Rescheduled Arrears %%% % % Male 12.460 1.757 14 0.491 4 5.82 47 1.141 9.00 3.251 26.0 Female 0.083 0.030 36 0.000 0 0.05 60 0.002 2.50 0.001 1.5 Joint 2.900 0.750 26 0.100 3 0.20 7 0.916 32.00 0.930 32.0 Group/Company 0.160 0.020 12 0.000 0 0.00 0 0.009 6.00 0.131 82.0 TOTAL 15.600 2.557 16 0.591 4 6.07 39 2.068 13.00 4.313 28.0 No. Disbursed Male 310 67 22 13 4 17 5 53 17 160 52 Female 13 3 23 0 0 2 15 2 15 6 46 Joint 103 14 14 5 5 4 4 22 21 58 56 Group/Company 4 1 25 0 0 0 0 1 25 2 50 TOTAL 430 85 20 18 4 23 5 78 18 226 53 SOURCE: Survey Data 5.19 REPAYMENT STATUS BY LOAN SIZE Of the total amount disbursed to projects of over G$100,000, 8 percent of the number of loans, representing 59 percent of the value, were in full default (Table 31). In contrast, loans below G$30,000 represented only 5 percent of the value in full default and almost an equal percentage (4 percent) of the numbr of loans in this category. More importantly, loans less than G$30,000 have a better repayment performance by both number and value for all categories, including loans repaid without arrears. These results confirm that large loans contribute more to the default problem than small loans. The most important component of a new strategy for the bank would be to pay particular attention to these large loans. 115 TABLE 31 REPAYMENT STATUS OF LOANS IN THE SAMPLE DISBURSED BY LOAN RANGE, 1987-1991 Repaid Refinanced Repaid Full with and Current Without Type of Volume Default Investment Disbursed Arrears Rescheduled Arrears % %% % % Large 9.2 1.15 13 0.221 2 5.389 59 0.83 9 1.6 17 G$> 100,000 Medium G$30,000- 3.2 0.73 23 0.213 7 0.527 16 0.63 20 1.1 34 100,000 Small G$< 30,000 3.2 0.68 21 0.157 5 0.154 5 0.61 19 1.6 50 TOTAL 15.6 2.56 16 0.591 4 6.070 39 2.07 13 4.3 28 No. Disbursed Large 24 3 13 2 8 2 8 8 33 9 38 G$> 100,000 Medium G$30,000- 59 10 17 4 7 8 14 18 31 19 32 100,000 Small G$< 30,000 347 72 21 12 3 13 4 52 15 198 57 TOTAL 430 85 20 18 4 23 5 78 18 226 53 SOURCE: Survey Data 5.20 REPAYMENT STATUS BY SOURCE OF FUNDS Repayments by source of funds indicated that 10 percent of the value of disbursements, accounting for 5 percent of the number of loans disbursed by GAIBANK were in full default (Table 32). Disbursements from IDB sources, on the other hand, had 90 percent of its portfolio in full default, with one large loan accounting for this poor repayment performance. All rescheduling and refinancing were processed only for GAIBANK loans, because rescheduling and refinancing was not permissible in IDB- funded projects. 116 TABLE 32 REPAYMENT STATUS OF LOANS IN THE SAMPLE DISBURSED BY SOURCE OF FUNDS, 1987-1991 Repaid Refinanced Repaid Full with and Current Without Source of Volume Default Funds Disbursed Arrears Rescheduled Arrears %% % % % GAIBANK 9.8 2.47 25 0.6 6 0.95 10 1.49 15 4.29 44 IDB 5.7 0.00 0 0.0 0 5.12 90 0.58 10 0.00 0 EIB 0.1 0.09 90 0.0 0 0.00 0 0.00 0 0.01 10 TOTAL 15.6 2.56 16 0.6 4 6.07 39 2.07 13 4.30 28 No. Disbursed GAIBANK 423 84 20 18 4 22 5 75 18 224 53 IDB 4 0 0 0 0 1 25 3 75 0 0 EIB 3 1 33 0 0 0 0 0 0 2 67 TOTAL 430 85 20 418 4 23 5 78 18 226 53 5.21 REPAYMENT STATUS BY YEAR Table 33 shows the repayment status of loans for the period 1987-1991. The important result from this table is that loans in full default, loans repaid with arrears, and loans refinanced and rescheduled are increasing over time by the number of loans, and by the value of disbursements in these categories. This implies that arrears and loan default problems are serious in the loan portfolio of GAIBANK. 117 TABLE 33 REPAYMENT STATUS OF LOANS IN THE SAMPLE DISBURSED BY YEARS, 1987-1991 Refinanced Repaid Repaid with Volume and Full Default Current Without YEAR Arrears Disbursed Rescheduled Arrears % %% % % 1987 0.8 0.409 50 0.000 0 0.013 2 0.158 20 0.220 28 1988 1.5 0.555 37 0.111 7 0.032 2 0.443 30 0.359 24 1989 1.9 0.267 14 0.067 4 0.445 23 0.564 30 0.557 29 1990 1.5 0.289 19 0.070 5 0.188 13 0.430 29 0.523 34 1991 9.9 1.037 10 0.343 3 5.393 54 0.473 5 2.654 27 TOTAL 15.6 2.557 16 0.591 4 6.071 39 2.068 13 4.313 28 No. Disbursed 1987 62 11 18 0 0 5 8 14 23 32 51 1988 109 19 17 3 3 3 3 23 20 62 57 1989 94 22 23 4 4 4 4 16 17 48 52 1990 96 20 21 4 4 4 4 15 16 53 55 1991 69 13 19 7 10 7 10 11 16 31 45 TOTAL 430 85 20 18 4 23 5 78 18 226 53 5.22 CONCLUDING REMARKS The survey data show that even though some borrowers repay their loans, especially Region 2 borrowers who received rice loans, still others do not repay, especially borrowers who receive large loans from international donor funds. This result confirms the skewed distribution of loans in rural financial markets. It also confirms that serious repayment problems exists in GAIBANK and rescheduling or refinancing will not resolve the arrears and loan default problems that had been increasing over the 1987- 1991 period. A closer look at the financial technology, however, would seem to suggest that high repayment levels may be positively related to the number of previous loans 118 obtained from the lender. Region 2 and to some extent Region 6 characterizes this observation, as these two regions had the most experienced borrowers in the loan portfolio, with the highest repayment rates. Thus, it is posited that the extent to which the experience of Region 2 can be replicated in the other regions might lead to a reduction in the arrears and default problems in GAIBANK. The next chapter analyses these issues, using an econometric model that incorporates the information presented in this chapter with the model developed in Chapter IV. CHAPTER VI EMPIRICAL ANALYSIS 6.01 INTRODUCTION In order to evaluate the efficacy of the screening mechanism, the empirical model, consisting of the rationing ratio and the repayment equation, defined in Chapter IV, will be estimated as two separate equations, using the Tobit estimator and the data in Chapter V. Also, a variation of the repayment model will be specified and estimated using the logit estimator, in an effort to examine what features are associated with creditworthy borrowers. Specifically, this chapter will be divided into 4 sections. Outlined in Section 1 is the approach used to examine the implicit screening and rationing behavior of the lender. Emphasis is placed on the sign, level of significance, and magnitude of the estimated parameters, and their impact on the assessment of the screening and rationing process. This is followed by a test of some of the hypotheses associated with credit rationing. Section 2 examines the repayment performance of borrowers by analyzing the sign, level of significance, and magnitude of the estimated parameters. This analysis seeks information about which variables explain repayment behavior. Several hypotheses are tested. Section 3 analyzes the efficacy of the screening mechanism by comparing and contrasting the results obtained in the rationing ratio with those for the repayment ratio. The sign and level of significance are important in this 119 120 analysis. Section 3 contains a discussion of the estimators used, as well as a brief discussion of the variables. A presentation of the results is made in Section 4, followed by some concluding remarks. 6.02 IMPLICIT SCREENING AND RATIONING In Chapter IV, it was posited that the lender constructs an implicit screening and rationing mechanism to make lending decisions. Specifically, the outcome of this mechanism was identified as the rationing ratio, defined as: (24) RRi = X$+eu where X is a vector of borrower characteristics, components of loan contracts, and processing procedures used by the lender. Thus, verifying that the screening and rationing mechanism exists, requires that the estimated parameters in the vector 0 be statistically significantly different from zero. If the estimated parameters are not statistically significant, then the independent variables included in the model would not explain how the credit rationing decisions of the bank are being made. If, however, the estimated parameters are statistically significant, then the independent variables in the model would explain to some extent the screening and rationing decisions of the bank. 6.03 ROLE OF THE SIGNIFICANT PARAMETERS IN SCREENING AND RATIONING Provided that the estimated parameters are statistically significant, their signs will determine their role in the rationing process. A positive sign would suggest that the 121 intensity of rationing declines (is lower) and the borrower receives a loan that is close or equal to the amount demanded. Alternatively, a negative sign suggests that the intensity of rationing increases (is higher). This implies that the applicant may be rejected, or the borrower received a loan that is less than the amount demanded. Statistically insignificant parameters do not explain rationing by the lender, and they are irrelevant to the screening and rationing mechanism. Statistically significant parameters will provide answers to the following hypotheses: (1) The processing of borrower information through the screening and rationing mechanism influences the size of loans approved (including rejection) for applicant borrowers. Thus, the purpose is to show that the parameters explaining the rationing ratio are non-zero. (2) As the number of previously approved loans for a borrower increases, the intensity of rationing will be less, as compared to the credit-rationing intensity for a new borrower. This result is based on the notion that the lender perceives the borrower with a long credit history to be a low-risk investment (Bourne and Graham, 1984). (3) As the value of collateral accepted as security relative to the amount of the loan demanded increases, the intensity of rationing will decline. This result is consistent with the lender attempting to avoid large losses, shifting a substantial part of the risk to the borrower. Further, collateral plays the role of a deductible, in inducing borrower behavior compatible with the lender’s interests. Rationing will be less in 1990-1991 as compared to rationing in 1987-1989. In the late 1980s, no new funds were available, especially for agriculture, and new lending was limited to the level of repayments. Thus, rationing would have been less intense than in the early 1990s, when the IDB, EIB and Government counterpart funds were being disbursed. Rationing will be less as the borrowers provide an increasing share of the total investment, (E/I), where the value of the investment is defined as the sum of the loan, plus the borrower’s contribution into the project. The IDB loan program requires that at least 20 percent of the project costs be financed by the borrower; otherwise, the application would be rejected. In general, increasing equity relative to the size of the investment should result in less intensive rationing. Borrowers engaged in profitable export activities such as fishing, rice and sugar cane production are expected to be less rationed as compared to other agriculture geared for the domestic market. Because of the access to better maintained infrastructure, export marketing and milling services, borrowers in Regions 2, 4 and 6 should be less rationed than borrowers in other regions where these facilities are not so well maintained. The lender is expected to ration credit more intensely to those borrowers whose applications are subject to long processing times, largely because of the lack of information required to facilitate the decision-making process. This variable represents rationing by waiting (Gonzalez-Vega, 1975). 123 (9) The recent concern that women in developing countries are more likely to be discriminated against in credit markets is one hypothesis tested in this dissertation. Specifically, it is hypothesized that female borrowers are more intensively rationed than male borrowers. 6.04 THE ROLE OF SIGNIFICANT PARAMETERS IN EXPLAINING REPAYMENTS The repayment ratio is defined as: R P R i = Xa+e2i *25) It is explained by the same set of factors as the rationing ratio and it reflects the quality of the loan contract offered by the lender. Estimated parameters that are not statistically significant would not explain repayment behavior and might be irrelevant in attempting to predict and control repayment. Statistically significant parameters with positive and negative signs will, respectively, identify variables associated with the repayment behavior of borrowers. Specific hypotheses to be tested in this case are: (10) Repayments are likely to be higher for a borrower with a long credit history, as compared to a new borrower. This is likely because borrowers with a good credit history would want to protect their reputation and their access to additional subsidized loans. 124 (11) As the value of collateral accepted as security relative to loan demand increases, loan repayments will increase. This result is consistent with the borrower attempting to avoid the losses resulting from foreclosure. (12) Lower repayments are expected for loan disbursed during the period 1990-1991, as compared to those disbursed in 1987. In the early 1990s, when the IDB, EIB, and Government counterpart funds were being disbursed for industrial projects, this large infusion of funds and the desire for quick disbursement encouraged lax screening that would lead to lower repayments, as management was concerned with disbursement levels.31 (13) As the grace period on loans increases, it is likely that repayments will decrease, largely because the borrower has acquired other senior claims that are more important than the GAIBANK debt. In the balance sheet for borrowers, it was shown that GAIBANK debt was relatively small (2 to S percent of liabilities and networth) in comparison to other debt, which presumably might have been contracted at market rates of interest. Thus, these non-GAIBANK debts might have a first claim on the cash flows from borrowers. (14) It is hypothesized that higher repayments are expected from export based activities, namely: fishing, rice, and sugar cane, than from other activities. 31 Penalties are usually added to the cost of the loan from IDB to GAIBANK for the late disbursement of funds at the expiration of certain dates, or the undisbursed funds are withdrawn if the entire sum is not drawn down by a certain date. 125 (15) Because IDB sources do not allow refinancing and rescheduling, it is anticipated that disbursements from GAIBANK sources will generate lower repayments as compared to IDB sources. (16) Borrowers 2, 4, and 6 should be better repayers than borrowers in the other regions because of their support infrastructure and access to export markets. 6.05 EFFECTIVENESS OF THE SCREENING AND RATIONING DEVICE Comparing the results of the estimated parameters in the rationing ratio with the repayment ratio equation will reveal the effectiveness of the screening and rationing mechanism. There are six possibilities. First, if the parameter in the rationing ratio is positive and significant and if the same variable has a positive and significant parameter in the repayment equation, then this variable identifies one characteristic of creditworthy borrowers. Second, a significant and negative sign for the estimated parameters in both equations, signals that default-prone borrowers are being correctly identified. They should probably be excluded from the market. A third possibility occurs when a parameter in the rationing ratio equation is not statistically significant, but it is statistically significant in the repayment equation. This implies that the lender is ignoring useful information which explains higher repayments, in the decision-making process. Thus, the screening and rationing mechanism is inefficient. A fourth possibility occurs when a parameter is statistically significant in the rationing ratio, but it is statistically insignificant in the repayment equation. This result 126 indicates that the lender is rationing credit by an irrelevant variable that does not explain higher repayments. Thus, the screening and rationing mechanism is flawed. Another possibility is for the parameter in the rationing ratio equation to be negative and statistically significant, while the same variable is positive and statistically significant in the repayment equation. This outcome indicates that the lender is making a Type I error, because he is rationing credit to a borrower who has the capability to repay a larger loan. Thus, the lender is loosing income by this outcome. Finally, a positive and statistically significant parameter in the rationing ratio equation that is matched with a negative and statistically significant parameter in the repayment equation indicates that the lender is making a Type n error, because it is incorrectly providing credit to a non-creditworthy borrower who makes less than expected repayments. Thus, the lender is jeopardizing the viability of the bank, if too many of these variables are used in the screening and rationing mechanism. The proportion of parameters in each one of these six categories will indicate whether or not the screening and rationing mechanism is flawed. 6.06 RATIONALE FOR USING THE TOBIT ESTIMATOR In addition to assigning applicants to borrower classes, screening is observed when some applicants receive loans (L;> 0) and others are rejected (Lj = 0). For those who receive loans, there is, in addition, the intensity of rationing, since an approved loan amount that is greater than zero may still be less than the amount demanded. Thus, the rationing ratio as the dependent variable and defined as (RR=Li/Ld) is limited to be positive, but continuous between zero and one. Since the lender has the actual explanatory variables for both approved and rejected borrowers in the vector X, neglecting the use of any of this information by choosing the wrong estimator would yield biased and inconsistent estimates, as the sample is censored. Following Judge et al (1988), if we ignore the rejected applications for which RR=0, the ordinary least squares (OLS) applied only on the approved loans (RR>0) would yield biased and inconsistent estimates for /?. For instance, the expected value of RRj conditional on RRj>0 is: (26) Since it is assumed that e-, are independent and normally distributed random variables, then: (27) 'i where and 6t are the density and distribution functions evaluated at X/3 la. Thus, OLS applied when RRj>0 implies that the estimated parameters will be biased and inconsistent because OLS yields: (28) The term o$ J0 is omitted in the OLS regression. Similar difficulties would be observed if OLS is applied to all approved and rejected applications, since the unconditional expectation of RR; is: 128 (29) E iRR i) = ©.((.XpHoflj Since the survey data on loans contain applications with zero loan amounts (68 observations) and zero repayments (23 observations), together with variations in the actual rationing ratio chosen by the lender, or in the repayment ratio, the censored model by Tobin (1958) is the most efficient in this case. It is formulated as: RRS = *P+eu i f RRS>0 (30) = 0, otherwise and RPRi = Xa+e21 i f RPR>0 *31> = 0, otherwise where beta and alpha are vectors of parameters to be estimated, using the truncated normal and censored distribution to generate efficient estimates for ft and a2, based on n observations, with Uj and vt independently and normally-distributed errors (Maddala, 1990). The total sample of n observations for the rationing ratio can be written as T = Ti+T0, where T0 is the number of observations for which RRj = 0 (rejected applications) and T, is the number of observations for which RR' > 0 (approved loans). The parameters in this equation (ft a) can be consistently estimated by maximum likelihood (ML) procedures. Define and ft to be, respectively, the density function and the 129 distribution function of the standard normal distribution evaluated at a X;/ L=LnL=£0toInU-Gj) -(r1/2)ln2n-(r 1/2)lno2- ^ 1 (Ji-X/P)2/2o2 (32) where X1 is a set of explanatory variables. Assuming that the error term is normally distributed with zero mean and constant variance( N(0, a2)), the estimated parameters in this model yield predictions for RRj, while the maximum likelihood method to estimate the parameters (0, a2) yield the best unbiased estimates (Amemiya, 1984). Set the first derivative of /3 in the likelihood function to zero (S(/30) = 0), and obtain the second derivative of the likelihood function with respect to j8, (I(/3». Because the first derivatives produce an equation that is nonlinear in 0, the Newton Raphson iterative procedure defined as (0, = /30 + POJo)]'1 S(/3 q)), can be utilized for convergence to the maximum of the likelihood function. The total number of approved loans (T,) provides additional information, through their respective loan contracts and their actual repayment records. Rejected loan 32The density function is defined (Maddala, 1990): z $ ! = [= —-— e"t2/2 dt; where z=0xi/o ( 1 ) i \/T2ir) e -(0xi )J/2 In order to estimate the repayment equation (RPRJ, the Tobit procedures are the same as those utilized in the rationing equation and, therefore, these would not be rewritten. In the repayment model there are 430 observations, containing 23 loans with no repayments and 407 loans with repayments. The difference of 6 observations represents those borrowers whose repayments were not yet due at the end of 1991. The estimation is completed using the SHAZAM TOBIT software package and the same independent variables are utilized in both equations, so that borrower responses can be directly compared with lender behavior. An important advantage of the Tobit estimator is that it yields three different estimates for the dependent variable (Judge et al 1984). There is an estimate for the predicted value for the intensity of rationing, one for both loan approval and rejection, and another for rationing that only considers approved loans. These measures are identified by Maddala (1990): BE(RRl) _n (33) <3«> 131 (35) dEUjRji RRj >0) t1 ~=Q(z) _( Q(z) )2) . w here z = Ax/a dXj P^U Z Similar advantages hold for the repayment equation. 6.07 LOGIT ESTIMATOR The logit estimator is utilized to estimate one version of the RPR equation, using a qualitative dependent variable that identifies creditworthy borrowers. The reason for using the logit estimator is that in the repayment equation, the variable RR; does not separate creditworthy borrowers from all other borrower classes in a clearly defined manner. For example, borrowers who repay their loans without arrears, and borrowers who are current and up-to-date with their repayments are the most valuable assets in the loan portfolio of the DFI. These borrowers are better repayers than borrowers who repay their loans in full, but with arrears. The RR will be equal to one in both cases. They are also better than borrowers who had refinanced or rescheduled loans, for whom the RR variable may not be adequate. In the data set, there are 304 borrowers who may be classified as creditworthy (226 borrowers who repaid their loans without arrears; plus 78 borrowers who are current). There are 126 borrowers who may be classified as non-creditworthy (85 borrowers who repaid their loans with arrears, 18 borrowers who had refinanced or 132 rescheduled loans, and 23 borrowers who are in default). The logit model is defined by the regression relationship: = ■x®i+e3i (36) Yj = 1, if Y, > 0 Y; = 0, otherwise From equation (28), it is observed that: P ro b . (Yi=l)= P xob. (e^-Xb) (37> = 1 - F( -X5) where F is the cumulative distribution for e. The likelihood function is: L=nyi. 0.F<-XbM y r l [1 -F( -X 6)] (38) Assuming that the cumulative distribution of e-, is the logistic, this yields the logit model: F[X( PJ= i r <39> l+exp‘XiP Because the first derivative of this estimator is nonlinear in its parameters, the iterative Newton-Raphson method is used to maximize the log-likelihood function (Judge et al, 1988). For the estimation of the equation for creditworthy borrowers, there are 304 observations at 1, contrasted with the remaining 126 observations for non-creditworthy borrowers. The estimation is completed using the SHAZAM LOGIT software package. 133 6.08 EXPLANATORY VARIABLES The explanatory variables, defined in Chapter IV, include quantitative and qualitative variables (Table 34). TABLE 34 VARIABLES USED IN THE ESTIMATION Dependent Variables: RR = (Loan Supplied/ Loan Demand) = L|/D, RPR = (Actual amount paid/ Amount due and payable) GL = Creditworthy borrowers Dummy = 1 if GL; Dummy = 0 if otherwise. Independent Quantitative Variables: Credit experience (CE): Number of previous loans received from Gaibank. Equity/Investment (E/I): Contribution by borrower in the project with respect to investment size. Delays (Dys): Number of days taken by lender to process the loan. Grace period: Number of days from date of last disbursement to date of first instalment. Collateral/Demand: Collateral/Loan Demand. Net-worth: Total assets minus total liabilities. Independent Qualitative Variables: Year 1987: Dummy = if 1987 Dummy = 0 otherwise year 1988: Dummy = if 1988 Dummy = 0 otherwise year 1989: Dummy = if 1989 Dummy = 0 otherwise year 1990: Dummy = if 1990 Dummy = 0 otherwise year 1991: Dummy = if 1991 Dummy = 0 otherwise Region 2: Dummy = if Region 2; Dummy = 0 otherwise Region 3: Dummy = if Region 3; Dummy = 0 otherwise Region 4: Dummy = if Region 4; Dummy = 0 otherwise Region 5: Dummy = if Region 5; Dummy = 0 otherwise Region 6: Dummy = if Region 6; Dummy = 0 otherwise Region 7: Dummy = if Region 7; Dummy = 0 otherwise Region 9: Dummy = if Region 9; Dummy = 0 otherwise Region 10: Dummy = if Region 10; Dummy = 0 otherwise Rice: Dummy = if rice; Dummy = 0 otherwise Sugar cane: Dummy = if sugar-cane; Dummy = 0 otherwise 134 Table 34 (continued) Foodcrops: Dummy = if food crops; Dummy = 0 otherwise Livestock: Dummy = if Livestock; Dummy = 0 otherwise Fishing: Dummy = if fishing; Dummy = 0 otherwise Other agriculture Dummy = if other agriculture; Dummy = 0 otherwise Industry: Dummy = if Industry; Dummy = 0 otherwise Male: Dummy = if male; Dummy = 0 otherwise Female: Dummy = if female; Dummy — 0 otherwise Joint: Dummy = if Joint; Dummy = 0 otherwise Group: Dummy = if group; Dummy = 0 otherwise IDB: Dummy = if IDB; Dummy = 0 otherwise Gaibank Dummy = if Gaibank; Dummy = 0 otherwise EIB: Dummy = if EIB; Dummy = 0 otherwise The intercept includes year 1987, group/company, Region 10, EIB funds, and other agriculture. These variables are placed in the intercept term because of the relative importance of the other variables. For example, the size of the loan, or the volume disbursed and repaid in each region or year are relatively larger than that observed for the variables in the intercept. 6.09 RESULTS FROM ESTIMATIONS For the equations that are estimated by Tobit (RRj and RPR;), the asymptotic t- ratios are based on the approach suggested in the paper by Tobin (1958) in which he scaled the model by ay in order to yield normalized coefficients. In the RRj equation, 16 of the 29 estimated parameters are significant, with 13 of them being significant at the five-percent level or better. In the RPRj equation, 13 of the estimated parameters are significant, with 12 of them being significant at the five percent level or better. In the 135 logit equation for creditworthy borrowers, 7 of the 29 estimated parameters are statistically significant at the ten percent level or better (Table 35). The model was able to associate this information better with a stricter definition of repayment (RPR) than with the broader concept of delinquency implicit in the GL variable. Although all forms of delinquency are of concern for the bank, the hard core default captured by the RPR variable is the most damaging. 6.10 RESULTS FROM TESTS OF HYPOTHESES Because over 50 percent of the estimated parameters (16 out of 29) are significant in the RR equation, it is concluded that the bank uses an implicit screening and rationing mechanism to allocate credit based on this information (Table 35). This result confirms hypothesis 1, suggesting that the processing of borrower information through the screening mechanism influences lender behavior. If the implicit financial technology is flawed, arrears and loan default will be observed. Confirmation of hypothesis 2, that credit experience is important, is obtained from the rationing ratio equation, as the estimated parameter is positive and statistically significant. This result implies that prior knowledge of a borrower’s past credit history reduces the intensity of credit rationing. Confirmation of hypothesis 3, that an increasing value of collateral relative to the loan amount is used by the lender to ration credit is verified as the estimated parameter is positive and statistically significant. Thus, the willingness of the borrower to offer a substantial amount of collateral relative to the loan amount approved, influences the lender to reduce the intensity of rationing. TABLE: 35 REGRESSION COEFFICIENTS AND ASYMPTOTIC I RATIOS Explanatory Variables RR RFRGL 1. Credit Experience CE 0.02** 0.03** -0.01 (1.67) (1.99) (-0-17) 2. Year 1988 A3 0.16 -0.10 -0.42 (1.02) (-0.59) (-1.01) 3. Year 1989 A4 -0.11 -0.38** -.97*** (-0.73) (-2.21) (-2.32) 4. Year 1990 A5 0.34** -0.67*** -0.99*** (2.06) (-3.80) (-2.33). 5. Year 1991 A6 0.28* -1.01*** -0.78* (1.52) (4.68) (-1.50) 6. Region 2 A8 1.34*** 0.87** 2.56*** (3.03) (1.83) (2.40) 7. Region 3 A9 1.17*** 0.31 1.41* (2.69) (0.68) (1.35) 8. Region 4 A10 1.43*** 0.20 0.30 (3.20) (0.42) (0.29) 9. Region 5 A ll 1.23*** -0.17 0.29 (2.87) (-0.39) (3.30) 10. Region 6 A12 1.28*** 0.53 1.59* (2.85) (1.10) (1.48) 11. Region 7 A13 0.86* -0.14 2.68 (1.48) (-0.21) (1.27) 12. Region 9 A15 1.75*** 0.09 0.05 (4.11) (0.21) (0.06) 13. Equity/Investment E/I 1.9*** -0.087 0.37 (8.28) (-0.33) (0.60) 14. Rice A17 0.09 1.40*** -1.17* (0.27) (3.46) (-1.29) 15. Sugar cane A18 -0.91 1.36* -29.62 (-1.27) (1.65) (-0.6E-4) 16. Food crops A19 -0.34 0.48 -0.01 (-1.00) (1.23) (-0.8E-2) 17. Livestock A20 0.26 1.10*** 0.64 (0.78) (2.67) (0.69) 18. Fishing A21 1.37*** 0.974** -0.33 (3.74) (2.28) (-0.36) 19. Industry A25 0.82 -0.74 -28.33 (0.84) (-0.73) (-0.7E-4) 137 Table 35 (Continued) Explanatory Variables RR RPR GL 20. Male A26 0.55 -0.85 1.19 (1.17) (-0.17) (1-04) 21. Female A27 0.73* -0.19 1.27 (1.36) (-0.35) (0.97) 22. Joint A28 0.41 -0.18 1.17 (0.88) (-0.38) (1.00) 23. Net worth NW -0.43 -0.8E-8 0.6E-7 (-0.21) (-0.16) (0.5E-4 24. IDB D1 -0.29 -4.41 27.7 (-0.14) (-0.24) (-0.6E-4) 25. GAIBANK D4 -0.10 -0.17 -26.38 (-6.87) (-1.94) (-0.14) 26. Delays (Days) Dys -0.002*** -0.1E-2** -0.9E-3 (14.97) (-6.00) (0.69) 27. Grace Period GP 0.008*** -0.4E-2*** 0.1E-2 (2.81) (1.68) (1.09) 28. Collateral/Loan Demand SL 0.003*** 22.70** 0.4E-2 (-0.43) (-0.16) (0.46) 29. Constant (C) -0.56 3.21*** 25.7 (-0.57) (3.11) (0.6E-4) * = 10% ; ** = 5% or better, but less than 1 %; *** = 1 % or better; The Constant includes: year 1987, Region 10; Other agriculture; Group/company; and EIB Funds. Confirmation of hypothesis 4, that rationing will be less for applications during the 1990-1991 period as compared to the 1987-1989 period, is verified as the parameters for years 1990-1991 are positive and statistically significant. In contrast, the parameters for 1988-1989 are not statistically significant. Confirmation of hypothesis 5, that rationing will be less as the share of equity invested in the project increases, is verified by a positive and statistically significant parameter. 138 Confirmation of hypothesis 6, that export activities such as fishing, rice and sugar cane production will be less rationed is not verified for all of these activities, except for fishing, which has a positive and statistically significant parameter. All the other parameters are not statistically significant. This result indicates that, with the exception of fishing, the lender does not ration credit according to economic activities. This may reflect a comparatively lax rationing attitude on the part of the bank. Confirmation of hypothesis 7, that the lender rations credit less in Regions 2, 4 and 6 is verified. However, it should be noted that in all the regions, compared to Region 10, credit is less rationed, because all the estimated parameters are positive and statistically significant. From the magnitude of the estimate parameters, it can be inferred that in Region 9 credit is rationed the least, followed by Regions 4, 2, and 6. Limited information and other characteristics, including opportunities to become involved in export activities, may explain the more strict rationing in Region 10. Confirmation of hypothesis 8, that the lender rations credit by waiting is verified, as the parameter is negative and statistically significant. When it takes long to grant the loan, the lender’s concern shows up as more strict rationing. Contrary to expectations, female borrowers are not rationed hard relative to male borrowers, because the estimated parameter for female borrowers is positive and statistically significant, while the parameter for male borrowers is not statistically significant. In fact, by type of borrower, the only significant parameter is the parameter for female borrowers, suggesting a special preference for female borrowers in the rationing mechanism. 139 Confirmation of hypothesis 10, that borrowers with a long credit history tend to be better repayers is verified as the parameter is positive and statistically significant. This result coincides with the positive and significant parameter in the rationing equation. The accumulation of information on borrowers is helping the bank improve its screening effectiveness. Confirmation of hypothesis 11, that borrowers who provide increasing levels of collateral relative to loan demand are good repayers, is verified by a positive and statistically significant parameter. Confirmation of hypothesis 12, that lower repayments are expected from loans disbursed in 1990-1991 compared to those granted in 1987-1989 is verified by negative statistically significant parameters for 1990 and 1991, as compared to 1987, while the parameter for 1988 was not significant. At the same time, the parameter for 1989, though statistically significant and negative (-0.38), was different in magnitude from the parameters for 1990 (-0.67) and for 1991 (-1.01). By this ranking it can be inferred that arrears and default have been increasing over time. The bank has to be particularly careful during periods of too rapid portfolio expansion. Confirmation of hypothesis 13, that extended grace periods cause repayments to fall is verified by a negative and statistically significant parameter in the repayment equation. When such periods are required by the nature of the project, the bank must be careful in loan evaluation. Confirmation that producers of crops repay consistently better has been verified for rice, fishing, and sugar cane as the parameters were positive and statistically 140 significant. Ranking repayments by the magnitude of the estimated parameters, the best repayments have been received from rice (1.40), sugar cane (1.36), livestock (1.1), and fishing (0.97). The parameters for industry and food crops were not statistically significant. Confirmation that repayments of loans from GAIBANK funds will be lower than those IDB sources (hypothesis IS) is not confirmed, and the parameter is not statistically significant. There were only four IDB loans in the sample. Confirmation that higher repayments are expected from Regions 2, 4 and 6 (hypothesis 16) is not verified for Regions 4 and 6, but it is verified for Region 2. Most of these parameters, were not significant, suggesting that there are little differences across regions, except for Region 2. The equation for creditworthy borrowers (GL) generated significant parameters with negative signs for years 1989, 1990, 1991, and rice. These results imply that repayments are likely to be made with arrears for loans disbursed in these years or for rice. The parameters for the variables for Regions 2, 3, and 6 are statistically significant with positive signs. These results indicate that more punctual repayments are expected from creditworthy borrowers in these regions than elsewhere. The other parameters are not statistically significant. 141 6.11 SCREENING AND ITS EFFICACY The efficacy of the screening mechanism is revealed by comparing the signs and level of significance on the 29 parameters for the rationing ratio, with the signs and level of significance in the repayment equation. The empirical evidence reveals that positive parameters with significant signs for both equations occurred for credit experience, Region 2, borrowers involved in fishing projects, and the variable collateral divided by loan demand. In all of these cases, GAIBANK is screening and rationing on the basis of criteria that correctly reflect probabilities of default. These variables must continue to be emphasized by the bank as reliable indicators of borrower performance. Parameters which are significant and positive in the repayment equation, but are not significant in the rationing ratio are related to rice, sugar cane, and livestock. This is useful information that the bank is not necessarily considering at present. Parameters in the rationing ratio, that are positive and are not significant in the repayment ratio and, therefore, do not explain repayment include Regions 3 to 7, and 9, the ratio of equity to investment, and loans to female borrowers. The ratio of equity to investment may be an artificial figure constructed for the loan application that may not truly reflect commitment to the project. The parameters for food crops, industry, male and joint borrowers, GAIBANK and IDB sources of funds, net-worth and year 1988, do not explain rationing or repayments. Errors in measurement may invalidate the usefulness of net worth. Since 142 most loans in the sample (98 percent) were from GAIBANK funds, not enough information was available to properly test for the influence of the source of funds. Type II errors occurred 10 percent of the time, represented by grace period, years 1990 and 1991. Gaibank should be more careful when granting loans with extended grace periods. Furthermore, year 1989 and extended processing times result in low repayments, but this was correctly recognized, in the latter case, by the bank and stricter rationing occurred. The main implication from these results is that statistically significant variables, which enhance increasing repayments, only occur twenty-eight percent of the time. Thus it can be concluded that the technology used by the lender is some what flawed and loan default may be a major problem at GAIBANK because of this inefficient technology which fails to separate a sufficient number of creditworthy from non-creditworthy borrowers. 6.12 CONCLUDING REMARKS The results of this chapter imply that the rationing mechanism is partially defective in identifying good repayers. These results suggest that good repayers are those with increasing credit experience, rice, sugar cane, fishing, and livestock producers, and borrowers who provide a substantial amount of collateral relative to loan demand. These have been correctly identified for less strict rationing (Table 36). Similarly, when the bank hesitated (long delays), it had good reason to be concerned. On the other hand, however, screening and rationing might have been too lax in 1990 143 and 1991 and the bank has not rationed sufficiently borrowers with long grace periods. Moreover, difference in rationing across regions do not appear to be justified(with the exception of the better experience in Region 2), since no significant difference in terms of repayment were observed. GAIBANK will have to give a second look to the equity over investment variable, since it does not explain repayment. There may be problems in correctly measuring the borrower’s commitment. Differences in repayment according to crop, on the other hand, have not been sufficiently considered. Gender was not related to repayment, nor was it improved by joint applications. Net worth is also worthless in explaining repayment. There may be serious measurement problems in connection with this variable. TABLE 36 RESULTS FROM DIAGNOSTIC TEST No Rationing Rationing Creditworthy Sector I: credit Sector III: experience, Region 2, collateral/loan, fishing Non- Sector II: grace-period, Sector IV: delays Creditworthy year 1990, 1991 CHAPTER VH SUMMARY, CONCLUSIONS AND RECOMMENDATIONS 7.01 SUMMARY Loan default is the most serious problem in Development Financial Institutions (DFIs), since it destroys lending capacity, it increases marginal lending and borrowing costs, it denies new applicants access to credit, and it transforms lenders into welfare agencies. If financial deepening and institutional viability are two main goals in developing the financial sector, then detecting the causes of default and predicting its impact should be a key task for DFI management and policymakers. To carefully study the loan default problem, it must be placed in a correct theoretical framework. Previous research identified loan default as a function of the unwillingness and inability of borrowers to repay. Following in the new research emphasis at the Rural Finance Program at The Ohio State University, this dissertation has advanced the hypothesis that default is an endogenous phenomenon, resulting in part from a flawed credit screening mechanism, which fails to reject a sufficiently large number of non creditworthy borrowers, who thereby receive non-enforceable loan contracts. If the screening mechanism is flawed, then loan default will be significant, transforming the DFI into a welfare agency. 144 145 Motivated by these concerns, this dissertation has examined the technology utilized by DFIs to screen loan applications and ration credit to borrowers, in the expectation that they would be able to reduce loan default. An attempt has also been made to evaluate the efficacy of the screening technology, as borrowers conform or deviate from their contractual loan obligations. A theoretically plausible microeconomic model was developed and a diagnostic device was designed to test for the validity of several hypotheses. In general, the processing of borrower information through the screening mechanism influences loan contract terms and the repayment behavior of borrowers. More specifically, if the screening mechanism is flawed, then loan default will increase. On the basis of the theoretical model, this dissertation has used econometric techniques to identify the criteria used in the implicit screening and rationing technology by GAIBANK. Non-zero coefficients for the variables explaining the rationing ratio identify the screening criteria. The efficacy of this screening technology is then tested by contrasting it with the determinants of the repayment ratio. Data for this dissertation were obtained from a stratified random sample from GAIBANK’s loan portfolio in eight regions, using information drawn from borrowers’ files and accounting records. Owned by the Government of Guyana, GAIBANK provides credit to many different sectors, diversifying its loan portfolio across agriculture, manufacturing, industry, fishing, and forestry. It obtains loans from donor agencies, but does not provide savings opportunities to borrowers, although the evidence compiled suggests that borrowers save in financial form at other banks and have 146 significant non-financial wealth, diversified across various types of assets. Although GAIBANK appears to be profitable, it is recognized that loan default is a serious problem. The examination of the loan portfolio indicated that the financial viability of the institution is threatened. 7.02 THEORETICAL RESULTS The theoretical model developed in this dissertation indicated that lenders, in the presence of information asymmetries and being unable to charge an appropriate risk premium, ration credit because of their concern for loan default. Borrower characteristics, filtered through a screening mechanism, form the basis on which loan applications are approved or rejected. The first-order risk-adjusted profit maximization conditions for the lender generate an equation for the intensity of rationing (Rationing Ratio). Further, a diagnostic device exists that can be used to scrutinize the quality of the loan portfolio, while detecting whether or not the credit screening mechanism is flawed. Sequential decision making is observed when an applicant presents a loan request to the lender. After screening the application, the lender may or may not approve the loan request, depending on the quality of the borrower-classes in the loan portfolio. If accepted, the borrower may receive all or part of the amount demanded. The borrower, in turn, decides whether or not repayment is a rational investment, given that the borrower may or may not need to protect a credit reputation. The model in this dissertation captures these sequential decisions in the rationing and repayment ratio 147 equations. In particular, lower marginal expected revenue leads the lender to ration credit more intensely to more risky borrowers, or it may lead the lender to screen the applicant out of the market. If the screening and rationing criteria are appropriate, this should be reflected in higher repayment ratios. If DFI management and policymakers are concerned about financial viability, then part of the solution lies in the need to allocate sufficient resources for the development of efficient screening technologies, improved credit inquiry procedures, and effective management information systems that process applicant-borrower information and repayments in a timely manner. These technologies will enhance the probability of selecting creditworthy borrowers and detecting loan default much earlier than is currently done. Additional lender actions after the loan has been granted, in monitoring borrower behavior and with firm collection measures when the contract becomes due, are also critical. 7.03 EMPIRICAL EVIDENCE AND POLICY IMPLICATIONS The empirical evidence obtained from the econometric results strongly suggest that: (1) A screening mechanism exists at GAIBANK, but it is partially flawed and requires modification, in order to increase the probability of selecting creditworthy borrowers. (2) Loan default has been increasing and it appeared to be higher for loans disbursed in 1990 and 1991, as compared to the late 1980s, due mainly to the need to disburse donor funds rapidly. Too quick a portfolio expansion, particularly to new borrowers, is always a major threat to a bank. Non-enforceable loan contracts were frequently agreed upon whenever extended grace periods on repayments were written into those contracts. Likewise, long processing times, due to incomplete information, are associated with a higher probability of loan default. These results imply that GAIBANK needs to reformulate and tighten its lending strategies in these areas. Furthermore, management and policymakers must seek ways to enhance the efficacy of the screening technology and improve the methodology for gathering and processing borrower information, so that more informed credit rationing decisions can be made. Computerizing its borrower information base and offering incentives to credit officers who have the largest portfolio of fully repaid loans without arrears and minimum refinancing and rescheduling are examples of what management can do to reduce loan default. The diagnostic device has shown that enforceable loan contracts have been identified in Region 2, where a high proportion of fully repaid loans are recorded, as compared to other regions. Similarly, borrowers with an extensive credit history at GAIBANK, borrowers who provided substantial collateral security relative to loan demand, and borrowers engaged in fishing projects are creditworthy. GAIBANK has recognized this and rations these borrowers less. 149 (5) Access to credit was easier in 1990 and 1991, when rationing was less intense and GAIBANK allowed extended grace periods on loans, but repayments were lagging behind. (6) Borrowers engaged in export activities, such as rice, sugar cane, and fishing, or borrowers who are in areas where the infrastructure is in working order, especially Region 2, repay their loans better than other borrowers. At the same time, Type II errors have been made in 1990, 1991 and by providing borrowers with extensive grace periods on their loans. (7) Because only a few applicants have been screened out of the market (14 percent) and also because only a few borrowers have been quantity rationed (5 percent of the approved applications), too many poor projects are being financed that should have been rejected. In particular, the evidence from the diagnostic device suggests that a long processing time is a clear indicator of a non-creditworthy borrower who should be screened out of the market. 7.04 DIRECTION FOR FUTURE RESEARCH In view of the fact that only one formal financial institution has been studied in this dissertation, it is important that similar investigations be undertaken in other institutions, such as commercial banks, credit unions, and in the informal financial sector, so that the findings in these studies can be generalized across countries. In particular, it would certainly be interesting to find out how credit rationing and loan default problems are correlated in other loan markets. No less interesting, too, would 150 be a study of borrowers, using panel data, in order to ascertain what are the long-run relationships between lenders and borrowers. Likewise, testing whether borrowers with savings accounts are rationed less and whether they are good credit risks are important questions in this context. Another important issue is the fact that borrowers usually have access to different loans at the same time, or they may have a demand for credit covering short, medium and long-term requirements. Analyzing these relationships will enhance our understanding of how the financial technology works in various credit markets and environments. Similarly, studies which analyze lender behavior for screening consumption loans versus production and investment loans will also enhance our understanding of financial markets. 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APPENDIX A ECONOMETRIC RESULTS 158 159 PILE 11 b:qte.l31 UNIT 11 IS NOW ASSIGNED TO: b :q te.l3 1 SAMPLE 1 504 ;_RBAD (1 1 ) A0 A17 A18 A19 A20 A21 A24 A25 DYS RR B I D1 D3 D4 GP CB A2 A3 A4 A5 A6 A9 A10 A l l A12 A13 A15 A16 SL A26 A27 A28 A29 NW 34 VARIABLES AND 504 OBSERVATIONS STARTING AT OBS 1 |_TOBIT RR CE A3 A4 A5 A6 A8 A9 A10 A ll A12 A13 A15 BI A17 A18 A19 A20 A21 A25 A26 A27 A28 D1 D4 DYS GP SL NW /PCOV PITER-0 REQUIRED MEMORY IS PAR* 14 8 CURRENT PAR- 270 POR MAXIMUM EFFICIENCY USE AT LEAST PAR- 266 TOBIT ANALYSIS, LIMIT- 0.00 25 MAX ITERATIONS 68 LIM IT OBSERVATIONS 436 NON-LIMIT OBSERVATIONS NUMBER OP ITERATIONS - 5 DEPENDENT VARIABLE « RR VARIANCE OF THE ESTIMATE - 0 .S 2 4 0 8 E -0 1 STANDARD ERROR OP THE ESTIMATE - 0.22893 ASYMPTOTIC VARIABLE NORMALIZED STANDARD T-RATIO REGRESSION ELASTICITY ELASTICITY COEFFICIENT ERROR COEFFICIENT OF INDEX OF E(Y ) CB 0.26494E-01 0.15836E-01 1.6730 0.60651B-02 0.0201 0 .0 2 0 7 A3 0 .1 6 3 2 4 0 .1 5 8 7 1 1.0285 0.37369B-01 0 .0 1 0 8 0 .0 1 1 1 A4 -0.11780 0.16195 -0.72742 -0.26969B-01 -0.0070 -0 .0 0 7 2 A5 0.34147 0.16568 2.0610 0.78173B-01 0.0184 0 .0 1 8 9 A6 0 .2 8 7 7 1 0 .1 8 8 1 3 1.5293 0.65864B-01 0 .0 1 3 5 0 .0 1 3 9 A8 1 .3 4 8 4 0 .4 4 4 9 3 3 .0 3 0 7 0 .3 0 8 7 0 0 .1 4 8 1 0 .1 5 2 5 A9 1.1795 0.43766 2 .6 9 5 1 0 .2 7 0 0 3 0.0358 0.0369 A10 1.4348 0.44783 3.2040 0 .3 2 8 4 7 0 .0 4 1 3 0 .0 4 2 5 A l l 1.2381 0.43006 2.8788 0.28344 0.0469 0 .0 4 8 3 A12 1 .2 8 9 4 0 .4 5 2 2 0 2 .8 5 1 4 0 .2 9 5 1 8 0 .0 5 0 9 0 .0 5 2 4 A13 0 .8 6 7 4 0 0 .5 8 3 4 8 1 .4 8 6 6 0 .1 9 8 5 7 0.0037 0.0038 A15 1.7592 0.42768 4.1134 0 .4 0 2 7 3 0.0234 0.0241 BI 1 .9 0 8 4 0 .2 3 0 3 1 8 .2 8 6 4 0.43689 0.2274 0 .2 3 4 2 A17 0 .93256B-01 0 .3 4 2 4 4 0 .2 7 2 3 3 0.21349E-01 0.0176 0 .0 1 8 1 A18 -0.91520 0.71854 -1.2737 -0.20951 -0.0015 -0.0015 A19 - 0 .3 4 8 9 5 0 .3 4 6 3 4 - 1 .0 0 7 5 -0.79884E-01 -0.0037 -0.0038 A20 0.26992 0.34640 0.77922 0.61793E-01 0.0073 0 .0 0 7 6 A21 1.3716 0.36582 3.7494 0.31400 0.0329 0 .0 3 3 9 A25 0.82636 0.97889 0.84418 0 .1 8 9 1 8 0 .0 0 2 2 0 .0 0 2 3 A26 0.55059 0.46789 1.1768 0.12605 0.1045 0 .1 0 7 6 A27 0.73495 0.53729 1 .3 6 7 9 0.16825 0.0063 0 .0 0 6 5 A28 0.41899 0.47204 0.88762 0.95920E-01 0.0286 0 .0 2 9 4 D1 -0.29067 1.3454 -0.21605 -0.66543E-01 -0.0002 -0 .0 0 0 2 D4 -0.10346 0.72635 -0.14243 -0.23684E-01 -0.0274 -0 .0 2 8 2 DYS -0.47189E-02 0.68638E-03 -6.8752 -0.10803E-02 -0 .1 0 8 9 -0 .1 1 2 1 GP 0.89898B-02 0.60038B-03 14.974 0.20580E-02 0 .4 1 6 5 0 .4 2 8 9 SL 0.34853B-02 0.12380E-02 2.8153 0.79789B-03 0.0183 0 .0 1 8 9 NW -0.19064E-07 0.439S0B-07 -0.43369 -0.43643E-08 -0.0030 -0 .0 0 3 1 CONSTANT - 0 .5 6 5 7 4 0 .9 8 5 4 0 - 0 .5 7 4 1 2 - 0 .1 2 9 5 1 RR 4.3682 0.15380 2 8 .4 0 2 THE PREDICTED PROBABILITY OF Y > LIMIT GIVEN AVERAGE X (I) - 0.9998 THE OBSERVED FREQUENCY OF Y > LIM IT IS - 0 .8 6 5 1 AT MEAN VALUES OF ALL X ( I ) , E(Y ) - 0 .8 2 7 3 LOG-LIKELIHOOD FUNCTION- -41.149437 MEAN-SQUARE ERROR- 0 .4 3 2 4 9 0 6 0 E -0 1 MEAN ERROR--0.10873078E-01 SQUARED CORRELATION BETWEEN OBSERVED AND EXPECTED VALUES- 0 .6 3 8 3 1 _END 160 FILB XI b:qt.551 UNIT XI IS NOW ASSIGNED TO: b :q t.5 5 I I SAMPLE X 43 0 |_RBAD (XX) A8 AX7 AX8 AX9 A20 A2X A24 A25 A25A A3X DYS B I DX D3 D4 GP CB A2 A3 A4 A5 A6 A9 AXO AXX AX2 AX3 AX5 AX6 SL A26 A27 A28 A j 29 M9 MXX MXX5 MX5 MX6 RPR GL RFR DF NW 44 VARIABLES AND 430 OBSERVATIONS STARTING AT OBS X |_TO B IT RPR CB A3 A4 AS A6 A8 A9 AXO AXX AX2 AX3 AX5 E l AX7 AX8 AX9 A20 A2X A25 A26 A27 A28 DX D4 DYS GP SL NW /PCOV PIT E R -0 REQUIRED MEMORY IS PAR* X62 CURRENT PAR* 27 0 FOR MAXIMUM EFFICIENCY USB AT LEAST PAR- 263 TOBIT ANALYSIS, LIMIT- 0.00 25 MAX ITERATIONS 23 LIM IT OBSERVATIONS 407 NON-LIMIT OBSERVATIONS NUMBER OF ITERATIONS * 4 DEPENDENT VARIABLE - RPR VARIANCE OF THE ESTIMATE * 0 .6 2 7 S 2 B -0 X STANDARD ERROR OF THE ESTIMATE - 0 .2 5 0 5 0 ASYMPTOTIC VARIABLE NORMALIZED STANDARD T-RATIO REGRESSION ELASTICITY ELASTICITY COEFFICIENT ERROR COEFFICIENT OF’ INDEX OF E(Y> CB 0.33222B-0X 0 . X 6659E-0X X.9942 0.83222B-02 0.0302 0.0305 A3 -0.10059 0.X6865 -0.59644 - 0.25X98E-OX -0.0076 -0.0077 A4 -0.38390 0.X73X7 -2.2X69 -0.96X60B-OX -0 .0 2 5 3 - 0 .0 2 5 5 A5 -0.67355 0.X77XX - 3 .8 0 3 0 -0 .X 6 8 7 3 -0.0449 -0.0452 A6 -X .0 X 0 4 0.2X578 -4.6827 -0 .2 5 3 X 2 - 0 .0 4 7 7 - 0 .0 4 8 0 A0 0.87869 0.47894 X.8347 0.22011 0.X09X 0 .1 1 0 0 A9 0.3X784 0.466X5 0.68X85 Q.79620B-Q1 0.0095 0.0096 AXO 0.20334 0.47996 0.42367 0.50938B-01 0 .0 0 6 5 0 .0 0 6 5 AXX -0 .X 7 9 6 8 0.45806 -0.39228 -0 .450X2B-01 - 0 .0 0 7 4 - 0 .0 0 7 4 AX2 0 .5 3 5 8 0 0 .4 8 5 X 6 X .X 044 0 .1 3 4 2 2 0.0257 0.0258 A13 -0.X4236 0.66X96 -0.2X506 -0.35662B-01 - 0 .0 0 0 5 - 0 .0 0 0 5 AX 5 0.96297B-0X 0.44668 0.2X558 0.24123E-01 0 .0 0 X 4 0 .0 0 X 4 E l -0.87657E-0X 0.26037 -0.33667 -0.21958E-01 - 0 .0 1 3 0 - 0 .0 1 3 1 AX7 X.4 0 7 2 0 .4 0 6 4 8 3 .4 6 X 9 0 .3 5 2 5 0 0 .3 1 0 5 0 .3 X 2 9 AX8 X .3 6 9 5 0 .8 2 9 5 0 X .65X 0 0 .3 4 3 0 6 0.0019 0.00X9 AX 9 0.48250 0 .3 9 2 2 X X. 2302 0 .1 2 0 8 7 0 .0 0 4 7 0 .0 0 4 7 A20 X.X052 0.4X389 2.6704 0.27686 0.0253 0.0255 A2X 0.974X3 0.427X6 2.2805 0.24402 0.0243 0.0245 A25 -0.74620 X.0X09 -0 .7 3 8 X 3 - 0 .1 8 6 9 3 - 0 .0 0 2 6 - 0 .0 0 2 6 A26 -0.85046E-0X 0.47543 -0.X7888 -0.2X304E-01 -0.0183 -0.0X84 A27 -0.X9740 0.55642 -0.35478 -0.49450E-01 -0.0018 -0.00X8 A28 -0.X8708 0.48XX8 -0.38879 -0.46864E-01 -0.0134 -0.0X35 DX -4.4X43 26.20X -0 .X 6 8 4 8 - 1 .1 0 5 8 -0.0031 -0.0031 D4 -0.X7806 0.72884 -0.2443X -0.44606E-0X - 0 .0 5 2 3 - 0 .0 5 2 7 DYS -0.X8557E-02 0.9540XE-03 -X.9452 -0.46486E-03 -0.0351 -0.0353 GP -0.43473E-02 0.72389B-03 -6.0055 -0.10890B-02 - 0 .2 5 8 3 - 0 .2 6 0 3 SL 0.22704E-02 0.X3487B-02 X. 6834 0.56874E-03 0 .0 1 4 8 0 .0 1 4 9 NW - 0 .89602B-08 0.52775E-07 -0.X6978 -0.22446E-08 - 0 .0 0 1 5 -0 .0 0 X 5 CONSTANT 3 .2 X 9 2 X .0 3 6 1 3.1072 0.80643 RPR 3.9920 0.X4249 28.0X6 THE PREDICTED PROBABILITY OF Y > LIM IT GIVEN AVERAGE X ( I ) * 0 .9 9 9 6 THE OBSERVED FREQUENCY OF Y > LIM IT I S - 0 .9 4 6 5 AT MEAN VALUES OF ALL X(I), B (Y) - 0 .0 3 2 9 LOG-LIKELIHOOD FUNCTION* -46.557676 MEAN-SQUARB ERROR- 0 .5 7 0 2 8 2 X 6 B -0 1 MEAN ERROR*-0 .3X6064X0E-02 SQUARED CORRELATION BETWEEN OBSERVED AND EXPECTED VALUES* 0 .4 5 0 8 2 L end 161 |_FILE 11 b:qt.5Sl UNIT 11 IS HOW ASSIGNED TO: b:qt.S51 [SAMPLE 1 430 I_RBAD (1 1 ) A8 A17 A18 A19 A20 A21 A24 A25 A25A A31 DYS BI D1 D3 D4 GP CB A2 A3 A4 A5 A6 A9 A10 A l l A12 A13 A15 A16 SL A26 A27 A28 A S 29 M9 M il M115 MIS M l6 RPR GL RPR DP NW 44 VARIABLES AND 430 OBSERVATIONS STARTING AT OBS 1 |_LOGIT GL CB A3 A4 A5 A6 A8 A9 A10 A ll A12 A13 A15 BI A17 A18 A19 A20 A21 A25 A26 A27 A28 D1 D4 DYS GP SL NW /PCOV P IT E R -0 REQUIRED MEMORY IS PAR- 161 CURRENT PAR- 270 FOR MAXIMUM EFFICIENCY USB AT LEAST PAR- 262 LOGIT ANALYSIS DEPENDENT VARIABLE «GL CHOICES a 2 4 3 0 . TOTAL OBSERVATIONS 3 0 4 . OBSERVATIONS AT ONE 1 2 6 . OBSERVATIONS AT ZERO 25 MAXIMUM ITERATIONS CONVERGENCE TOLERANCE - 0 .0 0 1 0 0 LOG OF LIKELIHOOD WITH CONSTANT TERM ONLY * > 2 6 0 .0 8 BINOMIAL ESTIMATE - 0.7070 MAXIMUM ITERATIONS REACHED ITERATION 26 LOG OF LIKELIHOOD FUNCTION - >226.01 ASYMPTOTIC WEIGHTED VARIABLE ESTIMATED STANDARD T-RATIOELASTICITY AGGREGATE NAME COEFFICIENT ERROR AT MEANS ELASTICITY CE -0.72695E-02 0.41598B-01 -0 .1 7 4 7 6 -0 .56374E-02 -0 .53288E-02 A3 -0 .4 2 7 4 1 0 .4 1 9 2 3 -1.0195 - 0.27537E-01 -0.25109B-01 A4 -0.97452 0.41830 -2.3297 -0.54722E-01 -0.59087E-01 A5 -0.99736 0.42737 -2 .3 3 3 7 -0.56594B-01 -0.60631B-01 A6 -0.78364 0.52183 -1.5017 -0 .31497E-01 -0.27716E-01 A8 2.5607 1.0635 2.4078 0.27093 0.20153 A9 1.4150 1.0459 1.3529 0.35965E-01 0.33826E-01 A10 0.30830 1.0519 0.29308 0.83826B-02 0 .98108E-02 All 0.29939 0.99569 0.30068 0.10441E-01 0 .12880E-01 A12 1.5967 1.0721 1.4893 0.65120E-01 0.73590B-01 A13 2.6880 2.1132 1.2720 0.79441E-02 0 .53462B-02 A15 0 .57827B-01 0 .9 2 1 2 6 0.62770B-01 0.71779B-03 0 .90716E-03 BI 0.37395 0.61942 0 .6 0 3 7 2 0•47105B-01 0.44866E-01 A17 -1 .1 7 5 0 0 .9 0 6 9 1 -1.2956 -0.22085 -0.20883 A18 -29.629 0.42631E+06 -0 .69501E-04 -0 .35026E-01 -0 .19728B-12 A19 -0.69536E-02 0.84007 -0.82775E-02 -0.S7542B-04 -0.54323E-04 A20 0.64850 0.93854 0.69096 0.12649B-01 0 .11155E-01 A21 -0.33912 0.94168 -0 .3 6 0 1 2 -0.72161E-02 -0 .88389E-02 A25 -28.333 0.38289E+06 -0.73999B-04 -0.83735E-01 -0.64661E-01 A26 1.1924 1.1458 1.0407 0.21849 0.20833 A27 1.2890 1.3260 0.97214 0.99048B-02 0 .10111B-01 A28 1.1722 1.1677 1.0038 0.71363E-01 0.72356E-01 D1 2 7 .7 0 5 0 .55355B+06 0 .50049E-04 0.16376E-01 0 .10942E-12 D4 -2 6 .3 8 3 0 .38289E+06 -0 .68906E-04 -6.5965 -6.4623 DYS - 0 .95130E-03 0 .21506E-02 - 0 .4 4 2 3 4 -0 .15312E-01 -0 .15750E-01 GP 0.11133E-02 0.15964B-02 0.69741 0.56348E-01 0.54409E-01 SL 0.48964E-02 0.44870E-02 1 .0 9 1 3 0.27127E-01 0.22989E-01 NW 0.68439E-07 0.14609E-06 0.46847 0.95620E-02 0 .85138E-02 CONSTANT 2 5 .7 3 1 0 . 38289E + 06 0 .67202E-04 6 .5 3 9 9 6 .3 6 1 2 LOG-LIKBLIHOOD(0) - - 2 6 0 .0 8 LOG-LIKELIHOOD FUNCTION - 2 2 6 .0 1 LIKELIHOOD RATIO TEST 6 8 .1 3 3 1 WITH 28 D .F . MADDALA R-SQUARE 0 .1 4 6 5 CRAGG-UHLBR R-SQUARE 0 .2 0 8 8 3 MCFADDBN R-SQUARE 0 .1 3 0 9 9 ADJUSTED FOR DEGREES OF FREEDOM 0.70306E-01 APPROXIMATELY F-DISTRIBUTED 0 .1 5 6 1 1 WITH 28 AND 29 D.F. CHOW R-SQUARB 0 .1 5 0 8 4 PREDICTION SUCCESS TABLE 0 ACTUAL 1 3 9 . 2 6 . PREDICTED 1 8 7 . 2 7 8 . NUMBER OF RIGHT PREDICTIONS = 3 1 7 . PERCENTAGE OF RIGHT PREDICTIONS 0 .7 3 7 2 1 EXPECTED OBSERVATIONS AT 0 - 1 2 6 .0 OBSERVED 1 2 6 .0 EXPECTED OBSERVATIONS AT 1 - 3 0 4 .0 OBSERVED 3 0 4 .0 SUM OF SQUARED "RESIDUALS" - 7 5 .6 4 3 WEIGHTED SUM OF SQUARED "RESIDUALS" - 4 1 5 .9 0 APPENDIX B SURVEY INSTRUMENT 162 163 GAIBANK SURVEY OF BORROWER INFORMATION 1987 - 1991 SECTION 1: Borrower Information - Characteristics and Demographies. A.l File No. ______— A. 2 Year 1987 ^ A. 5 Year 1990 □ A. 3 Year 1988 --- A .6 Year 1991 □ A. 4 Year 1989-^--- 1 REGIONS A. 7 Region 1--- |---1 A .12 Region 6 □ A. 8 Region2 |---| A .13 Region 7 A. 9 Region 3 |-- | □ A .14 Region 8 □ A.10 Region 4 | — | A. 15 Region 9 □ A.11 Region 3 | | A .16 Region 10 □ rrnNOMlC ACTIVITY;_ A.17 Rice------|--- 1 A .21 F ishing □ A. 18 Sugarcane | — | A.22 F o restry □ A. 19 Food Crops------|-- 1 A.23 Food P rocessing A.20 Livestock ^ ^ □ V.24 O ther Agri □ t.25 In d u stry □ DISTANCE T O BANK: D istance from home to Bank Branch TYPE o r OWNERSHIP: A. 26 Male □ A .29 Croup □ A. 27 Female □ A. 30 Company □ A. 28 J o in t □ A. 31 Age of Principal Borrower CREDIT AND FINANCIAL HISTORY OF BORROWER CREDIT HISTORY B. 1 No of previous loans approved At the time of approval/rejection was the applicant/borrower: a) In the repayment cycle on any other loan from Galbank B.4 ' Yes □ B.5 No □ b) If Yes. was the borrower in Arrears/default B.6 Yea □ B.7 NO □ BALANCE SHEET ASSETS LIABILITIES i NEW WORTH B.8 Cash S_ B .15 GAIBANK Dobt $_ B.9 Savinas Account Balance S_ B .16 O ther Bank Debt 0 B.10 Value of Crops/Livestock S B.17 Other Liabilities B.ll Inventory/Stock S B .i n Net worth S.12 Accounts Receivable/Debtors S 165 cont.. BALANCE SHEET ASSETS B.13 Machinery and Equipment S_ B. 14 Land and Building $_ LAND TENURE C.l Transported land owned (acres) C.2 Private Land Leased (acres) C.l State Land Leased (acres) C.4 Squatting (acres) C.5 Land rented (acres) C.6 Registered Land (acres) C.7 Acres £inanced with loan SECTION 2: FINANCIAL TECHNOLOGY AND CREDIT RATIONING SOURCE OF FUNDS AND INTEREST RATE D.l I.D.B. □ D.4 GAIBANK □____ D.2 C.I.D.A. □ D.5 E.E.C. ____ □ 0.3 E.I.B. □ 0.6 P.L. 480 ____ □ 0.7 Contracted Intorest rate 0.8 Average interest rata 1987 B.9 Average interest rate 1908 0.10 Avoragc interost rate 1989 0.11 Average interest rate 1990 0.12 Average interest rate 1991 166 LOAN PROCESSING TIME AND QUEUING CDSTS BY BORROWER a) Date application made b) Date Loan approved ______c) Date of first disbursement d) late of rejection ______e .l No. of days for approval (b - a) e.2 No. of effective days for approval (c - a) e. e.3 No. of days for rejection (d - a) e.4 Average daily wage/salary paid to principal borrower 1 ______e.S borrower Queuing oosts days 6 wages i day NOMINAL LEADING AUTHORITY S Senior Assistant f .l Approved I 1 w £ ' Mmager □ □ f.C Regional Msuger f.2 Board of Directors □ f.3 General Manager □ f. 7 Rejected □ f.4 Deputy General □ Manager LOAN AMOUNT AND STRUCTURE g.l Loan amnt applied for $ g.2 Loan amount approved S g.3 Borrower equity invested $ In7 g.4 Short Term Loan □ 1 g.6 Long Term Loan | | 8-S Meditrn Term Loan | COLLATERAL AOTrTED AS SECURITY 1.1 Debenture i 1.2 Charge on Assets/Property S 1.3 Assignment of Sale i 1.4 Third Party Guarantor S I.S Deposit of Title Docuicnts S 1.6 Mortgage i 1.7 Leases over 21 years EXPLICITY TRANSACTION CDSTS PAID BY BOIIRIWEK k .l Loan Appraisal fees uaid on loan 1 k.2 Legal fees paid on loan S k.3 Other tees and charges S k.4 Comncrcial Bank Special fees for L.CS J LOAN DISBlTSCIgKT a) Date of first disburscncnt b) Date of last disbursement L.l No. of days for total disbursement (b - a) L.2 Total No. of disbursement made ______L.3 Total value of disbursements ______168 SECTION 3 LOAN REPAYMENT DEFAULT DATA AT 12/31/91 LOAN REPAYMENT AND CONTRACT DATA a) Date of last disbursement b) Date of f ir s t Installment M.l No. of days in the grace period (b * a) M.2 No. of contracted installments ______M.3 No. of actual installments mack: ______M.4 Total Principal repaid S ______M.5 Total interest repaid $ ______M.6 Total default fees paid S LOAN STATUS AT fEOMER 31. 1991 ' Is the loan fully disbursed M.7 Yes (_____ I M.8 No □ Is the loan in arrears: M.9 Yes □ M.10 No | | Is the lean current and up-to-date with repayments of principal and interest M.11 Yes □ M.12 No lias the loan been repaid in full: M.14 No M.13 Yes M.15 Repaid in full without arrears I 1 M.16 Repaid in full with arrears 169 Uaa the Loan ooani Refinanced M.15 Yea M.18 . | __j NO □ Raacttoduled M. 3 Yea 1 J M-7.0 NO □ flofinanaod and ItsctiaAUed M.21 Yea 1 1 M.22 NO LI VALUE OF PRINCIPAL lEPAYMNT ACZUAL AMXWT PRPICTPAL DY CONTRACT PAID 0.1 Valin of Pnncaoal Inatal 1 0 . LI 0.2 Value of Princi m l JLnstal 2 0 . 12 0.3 Value of PnnciDal Inatal 3 0 .1 1 0.4 Value of Prxncioal Inatal 4 0 .1 4 0.5 - do - Q. 15 0.6 - do - Q. 16 0.7 - do - Q. 17 o .s - do - Q. 18 0.9 • do - 0 .1 9 0.10 Value of Principal Inatal 10 0 .2 0 value o r p o m e s t repayment ACTUAL AMOUNT INTEREST BY CXUTRACT PAID K .l Value of Interest Inatal 1 R .ll R.2 Value of Intareat Inatal 2 R.12 R.3 R.13 n.4 R.14 R.5 R. 15 R.6 R. 16 R.7 R. 17 R.8 R.18 R..3 R. 19 R.10 Value of In terest Inatal 10 R.20 170 ccwrRncnrvL and a c h r l DIME OF REPAagrr OCNTRACT nA3E ACTLD\L DME REPAID H.l Date Of Inatal 1 H.U H.2 Date of Inatal 2 H.12 H.3 H.13 H.4 H.14 H.5 H.15 H.6 H.16 H.7 H.17 H.8 H.1B H.9 H.19 H.1 0 H.20 APPENDIX C MAP OF GUYANA AND LOCATION OF GAIBANK OFFICES 171 172 WHERE TO PINO US GAIBANKrS OFFICES - PRESENT AND PROPOSED •Morutu Charity fin *AnnaA*9lhi Onooinaamint .) C _ ••Hartem Partita • • a « or *• t own / • C l M O f M ^ Fort weiilnfton •Now Aimuroam •T ain Unoene'Xv ) •BIMX Sinn Poioer i •C ornvw ion Full time Existing offices Part time offices Prooosed office Indicates the respective Regions