Evaluating loan collection performance: An Indonesian example

Vol. Printed in Great Britain.

’ World Development,

16, No. 4

pp. 501-510.

0305-750)
1988.

$3.00 + 0.00

@J 1988 Pergamon Press plc

Evaluating Loan Collection Performance: An Indonesian Example BRUCE R. BOLNICK* Northeastern University, Boston, Massachusetts Summary. -

This paper discusses the evaluation of loan collection performance in a specialized term-credit program introduced in Indonesia to promote the development of small-scale enterprises. After outlining pertinent characteristics of the credit program, the available collection performance indicators are described and appraised. It is shown that program managers and bankers had access to quite a lot of data on collection performance, but none that provided meaningful information for evaluating loan recovery. The paper then examines sources of the evaluation problem, and suggests methods for developing more accurate and less ambiguous indicators of portfolio quality, loan recovery rates, and ultimate bad debt costs.

Effective management requires evaluation. timely and reliable information on collection rates and loan losses, so that corrective actions can be initiated to deal with emerging problems before crises emerge. Accurate measures of bad debt costs are essential also for appraising (controlled) interest rate margins or (market) spreads. Yet data systems commonly fail to serve these purposes. These information problems are the focus of attention here. The discussion is based primarily upon the author’s experience with Indonesia’s largest program of selective credits, generally known by its acronym, KIW KMKP.4 The outline of the paper is as follows. Section 2 provides background information on Indonesia’s KIWKMKP program. Section 3 reviews and appraises the collection rate statistics that were available for monitoring the program’s performance as of mid-1982. Section 4 attempts to explain why inadequate loan recovery evaluation methods were being used, and how more effec-

1. INTRODUCTION Less developed countries (LDCs) commonly use credit market interventions to promote development objectives. Such policies have been strongly criticized in the academic literature, both in terms of their economic impact and financial viability.’ One major concern is that government-mandated and/or subsidized credits are highly prone to repayment problems that can undermine a program’s effectiveness and threaten the financial integrity of participating intermediaries. Special credit programs are instituted to redirect credit flows towards “priority” borrowers such as farmers or small-scale enterprises. Whether motivated by political interests or by economic analysis of market imperfections,* such special credit programs often - Kane (1984) might say inexorably - encounter serious loan recovery problems. These collection problems have been discussed frequently. Special attention has been directed to analysis of how repressive financial policies, other policy-induced economic distortions, and political mechanisms adversely affect loan quality through influencing the behavior of the lenders, as well as the ability and willingness of borrowers to repay.3 In this literature, though, a vital aspect of loan recovery has received very little attention: information problems. Because of both technical and institutional characteristics of special credit programs, the available information on collection performance may be both inaccurate and inappropriate for the purposes of monitoring and

‘This paper is based upon observations of internal Bank Indonesia collection rate statistics made while the author was serving as an Economic Adviser to Bank Indonesia, through the Harvard Institute for International Development (HIID). It examines collection rate monitoring-systems as of mid-1982. Because of bank confidentiality, and because the purpose of the study is to discuss monitoring issues rather than to report on specific data, only approximations to actual data are used here. The views expressed are those of the author, and in no way represent the opinions of Bank Indonesia or of HIID.

501

WORLD

502

DEVELOPMENT

tive evaluation instruments can be developed. Finally, Section 5 summarizes the analysis and concludes the paper.

2. INDONESIA’S

KIWKMKP

PROGRAM

Until 1983, credit market interventions were a major instrument of development policy in Indonesia, where the financial system has been dominated by five large state banks under the close scrutiny of the central bank, Bank Indonesia. Most interest rates were controlled by the central bank, as were lending ceilings that incorporated fairly detailed credit allocations. A number of “priority” credit programs were established, characterized by interest rates fixed at below-market levels, preferential lending terms, risk-shifting arrangements to reduce the bad-debt cost to handling banks, and government provision of low-cost funds. By 1982, the largest of the priority credit programs was KIWKMKP, then accounting for 15% of the rupiah value of outstanding state-bank credit. KIWKMKP had been introduced at the end of 1973 to provide Indonesia’s indigenous small-scale entrepreneurs with access to bank finance. The program provides both investment credits (KIK) and permanent working capital credits (KMKP) to small businesses in all sectors of the economy, on very favorable terms. For brevity, the analysis here will concentrate on KIK credits, for which the data base was far more extensive. Also, since the credit program has been described in detail elsewhere,’ the discussion here will focus on characteristics that are most relevant to the subsequent evaluation of collection performance data. Eight characteristics need to be understood. First, the KIWKMKP program provides term credits. Maturities for KIK loans averaged nearly four years. Long lags between lending decisions and statistics on recovery are therefore unavoidable. Second, the small size of individual loans (roughly $6,000 for KIK in 1981), high transportation costs, and customers’ frequent lack of reliable records, precluded standard loan-by-loan supervision - while heightening the need for a reliable monitoring system. Third, the loan portfolio grew very rapidly. Combined KIK and KMKP credits outstanding grew from under $250 million at the end of 1978 to over $1,500 million at the end of 1981 (using Rp.625 = $1). As a consequence, a large fraction of the outstanding portfolio represented relatively recent loans that tend to be less prone to repayment problems than older loans. The rapid growth also outpaced the capacity of bank

personnel. While hiring and training proceeded very rapidly, both managers and staff at many branches were overworked and often relatively inexperienced. Fourth, the program had a prominent political profile as a major policy initiative to promote equity. Expansion of lending was a high priority, supported by large subsidies and considerable “moral suasion,” as well as genuine efforts to train bankers and improve procedures, terms and regulations. As the volume of lending expanded, so did the danger that the quality of new loans would deteriorate. Although “moving money” was undoubtedly a main objective when the program was smaller, by 1981 policy authorities were increasingly concerned with loan recovery, and with development of the banks’ lending capabilities. Fifth, most of the loan funds were provided by Bank Indonesia (backed partly by a World Bank loan), and 75% of the risk was borne by the stateowned credit insurance company, P. T. Askrindo. Consequently, Bank Indonesia (BI) played a major role in developing loan recovery statistics, while the incentive for handling banks to deal effectively with the problem was diluted.6 Sixth, since interest rates were controlled by Bank Indonesia, the adequacy of the gross interest spread for the banks was a matter of policy concern. KIWKMKP loans were intended to be profitable for the handling banks, and the bad-debt cost was a critical factor in judging profitability.’ Seventh, unpaid loans were often carried in the banks’ accounts and in aggregate outstanding balances even after being classified as bad debts. This practice exaggerated arrears and distorted collection performance measures. Finally, many bankers appeared not to consider short-term arrears as a significant loan recovery problem, on the grounds that cultural standards in Indonesia did not place great weight on promptness. If so. most standard arrears measures become quite difficult to interpret. It should be evident that the characteristics outlined here are not unique to Indonesia. Hence an analysis of the problems faced in monitoring collection performance for KIK/KMKP raises issues that may apply to special credit programs in other countries.

3. MEASURES OF KIWKMKP COLLECTION PERFORMANCE This section reviews the available information. Although banks and program managers had access to an abundance of data on KIWKMKP collection performance and portfolio quality,

EVALUATING

LOAN COLLECTION PERFORMANCE

careful inspection reveals that the information was far from adequate. The standard format for reporting portfolio quality on all types of bank credit in Indonesia is a four-category classification of loan “collectibility.” The four categories are: funcar (literally, “smooth”), kurung luncar (“less smooth”), diragukan (“doubtful”), and mucet (“stuck”). (Precise definitions can be found in Appendix A.) In mid-1981, over 90% of the KIK portfolio (by value) was reported as “smooth,” just over 5% as “less smooth,” and about 2% “doubtful.” Only a tiny fraction was classified as “stuck.” KMKP looked even better, with over 95% of the portfolio (by value) classified as “smooth,” and most of the remainder “less smooth.” These data suggest that the collectibility of the KIWKMKP portfolio was actually better than that of general short-term bank credits, and comparable in quality to large credits for donor-supported investment projects. Other available data, however, provided a less positive picture of the collection record. For example, a parallel reporting system provided data (for KIK only) on amounts due, repayments, and accounts in arrears. From these data, three types of collection rate indicators were routinely computed: 1. The “cumulative of time t= [sum of installments sum of installments

collection rate” (CUM) as paid since 19741 x 100 due since 1974

’

2. The “collection rate” (COLL) for period T (where T is a specific year, semester, or quarter) = [installments paid during 7l x 100 [installments

due during T] + [amount in arrears at start of T]

3. The “arrears rate” as of time t = [amount in arrears as of t] X 100 balance outstanding

as of t

In mid-1980 the “arrears rate” on KIK loans was almost 20% and the “cumulative collection rate” was just under 80%. Yet the “collection rate” for the year ending 30 June was hardly more than 50%. Bank Indonesia began in 1980 to develop a parallel reporting system to provide detailed figures on arrears by age (again only for KIK). This data set enumerated five categories of arrears, from less than three months overdue to more than 12 months overdue. It also tabulated advance payments and reschedulings (both relatively minor items). These arrears-by-age (ABA) data (for three pilot provinces representing

503

nearly one-third of total KIK lending) showed that over 80% of arrears were more than three months overdue, and roughly two-thirds were more than six months old. Fully half were more than 12 months overdue, indicating the tendency of the banks to accumulate old debts rather than write them off. Credit insurance claims filled at P. T. Askrindo provided yet another indicator of loan recovery. Discussions with bankers suggested that Askrindo coverage was virtually universal on KIWKMKP credits, and that virtually all qualifying bad debt claims, apart from very small balances, were in fact filed. Although Askrindo records could be matched to lending data, the author is not aware of such tabulations being available routinely. One special report as of mid1980 showed that cumulative claims to Askrindo amounted to over 5% of the outstanding balance of KIK loans, and somewhat more than 10% of cumulative installments due. Because cumulative installments due must exceed the cumulative value of matured loans, the 10% figure provides a lower bound on the historical bad-debt ratio. The actual loss rate may have been as much as twice as high. If so, the banks’ 25% retention of losses, on top of the Askrindo insurance premium (1.5%) would exceed the gross interest rate spread on KIK loans. This possibility alone argues eloquently for more reliable data on collection performance. Clearly, the volume of information on KIIU KMKP loan recovery and portfolio quality was ample. Yet how can one interpret reports showing that: only a few percent of outstanding debts are classified as bad or doubtful; but arrears account for a fifth of the portfolio, with half of the arrears more than a year overdue; and collection rates range from 50 to 80%, depending on which formula is used? The fact is that none of the existing, contradictory statistics provided managers with meaningful information about collection performance or the scope of bad debt problems. The remainder of this section presents justification for this claim. Two fundamental problems afflicted the loan recovery monitoring system. The first is the familiar data-processing gremlin known as “garbage-in garbage-out.” Though it is not possible to gauge the extent of data error, evidence of inaccuracies in the raw data was not hard to find. For example, different data sources that should have been mutually consistent could generate collection rates differing by almost 15%, using the same formula (author’s calculation). Also, arithmetical anomalies were not uncommon, such as: data showing collection rates in excess of 100%; cumulative figures that dropped

504

WORLD

DEVELOPMENT

from one period to the next; or arrears subtotals from one data source that exceeded corresponding totals reported through a different channel. On several occasions consultants visited bank branches to investigate extremely poor recorded collection rates, only to find that the data were wrong. Even the portfolio collectibility reports (smooth, less smooth, etc.), which were subject to scrutiny by bank supervisors, were often viewed with suspicion - off the record. Especially for small credits, quality judgments were not supported by careful loan reviews. Section 4 will address the question of how such data inaccuracy can occur and how it can be remedied. Here, the point is simply that the danger of inaccurate data cannot be emphasized too strongly. The second basic problem is that the various measures and indexes used to monitor loan recovery all suffered from serious analytical shortcomings that are not widely understood. Consider first the collectibility classification (percentage of the outstanding portfolio classified as “smooth,” etc.). These figures make sense for short-term commercial loans supported by systematic loan review, but in other contexts they can be seriously misleading. With term lending, for example, it is difficult to interpret any index of loan quality defined using the outstanding portfolio (OP) as the denominator. The ratio of “stuck” credit, or “doubtful” credit, or “less smooth” credit to OP will decline sharply during periods of rapid growth, and rise sharply when lending growth decelerates even without any change at all in the actual quality of the loans. Furthermore, because OP consists of a mixture of young and old loans, the magnitude of the bad debt rate is greatly understated even in a steady state. To illustrate, suppose that all loans have a twoyear maturity, and that 50% of them become bad debts at maturity. Let OP consist of $50 of this year’s loans and $50 of last year’s loans. Although 50% of all loans go bad, only 25% of OP will show up as bad debts, due to the fact that only half the portfolio falls due this year. Though data users may mentally adjust for such ambiguities, the fact remains that figures on collectibility of the e&ring portfolio are not clear indicators of smooth sailing or rough seas. The same problem arises when OP is used as the base for computing an “arrears rate.” In addition, the arrears rate (arrears/OP) overstates the collection problem to the extent that measured arrears include payments only a short period overdue. It is overstated still more to the extent that the numerator and denominator are each bloated by non-recoverables that ought to have

been written off. On the other hand, the index understates arrears by tabulating only the amount actually overdue, rather than the amount at risk. For instance, on a loan for which $5,000 is outstanding, only $100 may be in arrears; but the full balance may be in trouble. These errors work in opposite directions, precluding any generalization about the net distortion - and any meaningful interpretation of the reported figures. The “collection rate” (COLL) and the “cumulative collection rate” (CUM) avoid using OP as the basis for comparison. They are, however, beset with equally severe difficulties. (See Appendix B for a more formal analysis.) The annual collection rate (COLL) will be discussed first. This index has been recommended by World Bank experts as a useful indicator of repayment performance.’ Indeed, in its project agreement with Bank Indonesia, the World Bank required KIK collection reports to use this formula. Hence, COLL might be referred to as the “World Bank’s” collection rate index. Despite such authoritative support, COLL serves the role poorly. Its fundamental weakness is that the denominator (payments due, including outstanding arrears) grows steadily as old arrears accumulate. Recall that bad loans tend to be carried on the books in Indonesia. Consequently, the index can decline in value though collection performance is not getting worse - and even if collection performance is actually improving! (See Appendix B.) Such anomalies were observed in practice. As a consequence, many Bank Indonesia officials quite rightly considered this World Bank index to be of dubious value. The alternative index, the cumulative collection rate (CUM), was considered by some BI officials to be more meaningful. One reason for this preference may have been that CUM invariably showed far better collection performance than the annual rate, COLL. (Appendix B proves that this must be so.) Unhappily the CUM index, too, is seriously flawed. First, CUM becomes insensitive to recent collection performance very quickly, since the cumulative total of past amounts due and past repayments easily dominate the calculation. Furthermore, changes in CUM from period to period do not provide valid signals about the direcrion of change in actual collection performance. As shown in Appendix B, the value of CUM can register an increase even if collection performance is in fact deteriorating. Here too, such anomalies were observed in practice. The arrears-by-age data system promised more discerning measures of collection performance. But at the time the author’s connection with the

EVALUATING

LOAN

COLLECTION

program ended (and nine years after the lending program began) this system was producing only large data tables (of unverified reliability). Convenient analytical statistics for spotting problems were as yet unavailable. The Askrindo credit insurance claims data also lacked analytical substance and utility as a monitoring tool. The indicator most often compiled cumulative claims as a percentage of the outstanding portfolio - uses a denominator (OP) that bears little relation to the value of matured accounts, the appropriate base. Moreover, credit insurance claims exaggerated bad debt losses to the extent of subsequent recoveries (which were about 10% of subrogations at the time). In summary, the available array of measures used in Indonesia provided bankers and program managers no reliable, pertinent information about loan recovery. The indicators did not provide clear, timely signals to identify banks, branches, and sectors requiring corrective attention. The data did not provide unambiguous signals about changes in the quality of recent loan approvals. Nor did the data provide valid measures of the bad-debt cost being borne by banks handling the KIK/KMKP program. How did these evaluation problems arise, and how might they be remedied? These questions are addressed next.

4. IMPROVING THE EVALUATION COLLECTION PERFORMANCE

OF

Improving the evaluation of collection performance in a program like KIWKMKP requires both reliable data and appropriate statistical indicators. The data problem will be addressed first. A number of straightforward procedural measures can contribute greatly to improving the reliability of collection rate data. Audits of all collection rate reports can be incorporated into the regular supervision scrutiny by bank regulators. The paperwork can be reduced by simplifying and consolidating reports. In addition, whenever new data systems are developed, such as the arrears-by-age reports discussed above, it is imperative that pilot tests include careful field appraisal of reporting accuracy. Such procedural remedies, however, will not suffice if inaccurate data are but a symptom of more basic systemic problems. To understand this, consider the decision problem faced by a hypothetical bank branch manager, who is coping with rapid expansion under conditions of serious staffing constraints. The manager must decide on the appropriate allocation of resources: operations compete with (manual)

PERFORMANCE

505

paperwork; standard business competes with special programs; lending competes with collection; and internal accounts compete with special external reports. The manager’s solution is influenced by the knowledge that career advancement in the bank bureaucracy depends primarily on seniority, as long as lending is expanding, the branch’s books are in order, and no disasters occur. Also, the state-owned bank is not subject to strict profit discipline, especially for loan programs supporting political objectives. The manager perceives a special credit program as an externally-imposed burden, for which “moving money” is the most pressing consideration. It is “government money,” not “bank money,” that is at risk, and a large fraction of any losses are covered by subsidized credit insurance. Moreover, the percustomer costs of supervision and paperwork seem out of proportion to the small sums of money involved in these special credits. Finally, the manager knows that special reports are subject to neither internal nor external audit. Under the circumstances, it would be surprising if the branch manager did not accord a low priority to accurate handling of special collection rate reports on special credit programs. Therefore, procedural fixes alone will not generate more reliable collection rate data. Improved staffing, better training, and more effective motivation are also required. Bank Indonesia tended to handle motivation as something to be taught in training programs, and reinforced with heavy top-down pressure. In the long run, however,. the best motivation comes from internahzmg incentives. This can only be accomplished with more fundamental reforms that increase the handling-bank role in funding loans, bearing risks, and setting interest rates in a competitive, more profit-oriented banking system in short, policies that encourage financial deepening. By the mid-1970s officials were undoubtedly cognizant of the danger of letting collection rates get out of hand, based on experience with a special credit program for rice growers. Until 1979, however, the KIWKMKP program was growing slowly; bank lending behavior, if anything, was viewed as overly conservative. So it is probably valid to say that expansion of lending, not loan quality, was the overriding concern. After the program took off, though, collection rate monitoring did assume a higher priority, as evidenced by both public and private remarks of the central bankers. Why, then did authorities not move more expeditiously to remedy the flaws in the monitoring system? Part of the answer is simply lags, com-

506

WORLD

DEVELOPMENT

pounded by inexperience. A number of measures were at various stages of development. But in the absence of an immediate crisis it takes time for a large, bureaucratic system to identify problems, to develop solutions that work, and to implement and de-bug the appropriate measures. More subtle factors may also have been at work, about which one can only speculate. Authorities may have felt comfortable with ambiguity, out of fear that accurate collection rate reports could generate unpleasant and undeniable facts that might slow the program’s momentum. Or problems may have been brushed aside as a result of bureaucratic expediency. Ultimately, though, the basic reforms and institutional changes needed to internalize motivation undoubtedly evolve in response to broader policy concerns, largely independent of the concerns noted here. The second basic infirmity needing to be remedied .was the problem of inappropriate statistical indicators. As seen in Section 3, the indicators being used were of little value for evaluating the quality of the outstanding portfolio, current loan recovery performance, or the ultimate bad debt cost of the credit program. The institutional factors outlined in the previous two paragraphs probably affected the development of collection rate indicators, as well as the quality of the data base. Even if superior indicators had been developed, they might not have been adopted with alacrity. But in fact, the analytical shortcomings of the available statistics were not well understood. Assuming that officials do indeed want more accurate information, what collection performance indicator should be compiled? Consider first the indicators of portfolio quality. Recall that the current data system involved categorizing loans in terms of “collectibility.” This reporting system allows a loan payment to be overdue by up to six months before a loan is classified as “less smooth,” or substandard. Yet available arears-by-age (ABA) reports indicated that the collection rate on arrears less than 90 days past due could be less than half that on new installments due. This evidence suggests that even three months is too long a period to wait before considering a loan as “less smooth.” How long a period is appropriate? Rather than adopt some other arbitrary time period, it is preferable to rely directly on ABA reports. This system provides entirely objective and standard descriptive information, unlike the ambiguous and qualitative “collectibility” reports. Descriptive ABA reports can be evaluated in conjunction with measures of recovery rates (see below) for various categories of arrears, either in aggregate or for specific banks, regions, and sectors.

It is also possible to monitor the evolving quality of new loans if ABA data can be disaggregated by cohort, according to date of loan approval. From such data, a normal life cycle of quality deterioration can be established. On this basis one can flag quite clearly, and after only a few quarters, a deterioration in the performance of recent loans - in the aggregate, or by bank, province and sector. Similarly, one can distinguish between a bank with a mature high-quality portfolio and one with a young, but inferior portfolio, even when both show the same aggregate arrears rate. Any evaluation of portfolio quality should measure problem loans in terms of the amount at risk. For this purpose, the balance on accounts in arrears is more suitable than the amount in arrears itself. In addition, arrears indicators are distorted to the extent that non-recoverable sums are carried on the books. Hence, development of more discerning portfolio quality measures should be accompanied by tighter standards for writing off bad debts. To complement (stock) measures of portfolio quality, it is also important to have “collection rate” (flow) indicators that focus directly on recovery of amounts due. Even without ABA data, it is easy to compile loan recovery indicators that are less ambiguous and less prone to anomalous behavior than the measures described in Section 3. A simple improvement would be to generate separate ‘collection rate” indicators for new amounts due and for collection of arrears, rather than a single composite. With reliable ABA data, far more refined measures of loan recovery effectiveness can be obtained. Recovery rates can be calculated for each arrears age group. One can then obtain a robust composite measure of e.xxpetted collection performance, showing the ultimate collection rate implied by the prevailing pattern of recovery rates. (For details, see Appendix B.2.) As with indicators of portfolio quality, these flow measures of collection performance can be compiled for specific banks, regions, or sectors, as well as for the credit program as a whole. Sophisticated data analysis of the sort discussed here is probably beyond realization without computerized banking operations. Computerization, however, is spreading quickly in Third World banking. Improved data systems and monitoring reports can be phased in as branches become automated. In deciding whether to introduce new reporting systems, the costs need to be appraised quite soberly and balanced against the benefits. But for programs such as KIWKMKP, the benefit of an improved management information may be the

.

EVALUATING

LOAN

COLLECTION

difference between success and failure. One last collection performance measure is still to be considered: the ultimate bad debt cost. For a term credit program like KWKMKP, the observed frequency of bad debts represents experience on loans disbursed years earlier. Moreover, loans reaching maturity during any one period represent a variable mixture of cohorts ranging from two years to over eight years old. The other side of the coin is that the bad debt cost of current lending will not be known for years to come. In short, observed bad debts do not provide a valid measure of prospective losses on outstanding loans or on current lending activity. At a minimum, historical bad debt rates should be calculated in a more consistent manner. For KIWKMKP, the best statistic that can be computed from available data would be the ratio of credit insurance claims to matured loans, adjusted for subrogation recoveries. (Indeed, one of the more valuable benefits of a credit guarantee scheme may be to generate data of this sort!) The level of this cumulative statistic would still be a muddled composite of varying historical circumstances, but period-to-period changes would provide valid signals about the bad debt cost on the new cohort of maturing loans. A better measure of historical bad debt rates could be developed if existing credit insurance claims records were compiled by year of approval. Far more useful for monitoring purposes, though, would be predictive indicators of the prospective bad debt cost of outstanding loans and recent approvals. This is not a fanciful objective. Two types of predictive estimates can be generated, given appropriate data. First. suppose that collection performance data and historical bad debt rates are available, by year of approval. One can then establish a time profile of past collection rates that led to observed bad debt outcomes, and use this profile as a basis of comparison for loan cohorts that have yet to mature.’ As a simple example, it might be observed that during their first two years, collection rates on loans approved during 1985 were 20% below standard for that stage of the repayment cycle. One could then project an ultimate loss ratio 20% higher than the historical standard - if nothing is done to improve performance. Alternatively, data on recovery rates by age of arrears, discussed above, can be used to calculate an “expected bad debt ratio.” This statistic would provide a measure of the ultimate bad debt ratio for the outstanding portfolio that would result from the prevailing pattern of collectibility by age of arrears. (See Appendix B.2.) If arrears recovery rate data were available by year of

PERFORMANCE

507

approval, an expected bad debt ratio could also for particular loan cohorts using the profile method outlined in the previous paragraph. These projection techniques are far from airtight. For example a business downturn, rather than deteriorating appraisal standards or supervision practices, could be responsible for a temporary decline in collection performance. Nonetheless, the measures suggested here have the great advantage that they make full and consistent use of the available information. These techniques can produce early warnings about changes in, or cross-section variations in, prospective bad debt costs. be projected

5. SUMMARY AND CONCLUSIONS This paper has explored in depth the problems encountered in evaluating loan collection performance in the KIWKMKP special term credit program for small-scale enterprises in Indonesia. After a brief discussion of the characteristics of the KIWKMKP credit program, the indicators of collection performance used in the program were explained and critically assessed. It was shown that program managers and bankers had access to ample data on collection performance; yet they had no meaningful information to serve vital management needs. The paper then addressed the question of how to obtain reliable data and more meaningful statistical indicators of portfolio quality, collection rates, and ultimate bad debt costs. Both institutional and technical aspects of the problem were discussed. It is hoped that many of the lessons from this study of the KIIVKMKP program are applicable to other countries and to other types of loan programs. Of course, the development of reliable means to evaluate collection performance is but one essential task for credit program managers. Managers must also confront the problem of diagnosing loan recovery difficulties and finding effective remedies. More generally, management must also be concerned with evaluating the economic impact of special credits, the operational efficiency and opportunity costs of such programs, and their effects on development of a sound, competitive system of financial intermediation. The strong criticism of special credit programs so widely expressed in the academic literature provides a loud and clear warning against complacency.

WORLD

508

DEVELOPMENT NOTES

1. For prominent critical discussions of interventionist credit programs in developing countries, see (infer alia): Shaw (1973); McKinnon (1973); Von Pischke er al. (1983); and Adams er al. (1984). 2. See, for example, Khatkhate and Villanueva (1978); Anderson and Khambata (1984); Braverman and Gausch (1986); and Levitsky (1986). 3. In addition cent papers that include Adams Gausch (1986);

to the Kane reference in the text, rediscuss repayment problems, infer alia, and Vogel (1986); Braverman and and Levitsky (1986).

4. KIK = Kredir Invesrusi Kecil (Small Investment Credits); KMKP = Kredil Modul Kerja Permanen (Permanent Working Capital Credit).

by the fact that they had no power to foreclose on collateral. This was the legal responsibility of a stateowned collection agency (PUPN). Reliable figures on PUPN recoveries were unavailable. but it was generally considered that foreclosures on small loans were uncommon. 7. The interest rate on KIK loans (in the early 1980s) was 10.5%. with 100% of the loan amount rediscounted at an average interest cost of 4.32%. Adding in the 1.5% credit insurance premium, an interest spread of 4.68% was available to cover 25% of bad debt losses as well as all operating costs. Studies undertaken by Bank Indonesia to evaluate the adequacy of these spreads were inconclusive, in part due to uncertainty about the bad-debt costs.

5. For more complete discussions of the KIK/KMKP program, see Bolnick (1982); Nelson and Bolnick (1986); and Bolnick (1987).

8. Casley and Lury (1982). In their book. the formula is expressed in somewhat different terms, but one can easily show that their formula is identical to the one discussed here.

6. Even though the banks would bear only 25% of losses. their incentive for loan recovery was heightened

9. This technique is similar to that used by pediatricians to gauge a child’s growth.

REFERENCES Adams, D. W. et al. (Eds.). Undermining Rural Development with Cheap Credit (Boulder. CO: Westview Press, 1984). Adams, D. W., and R. C. Vogel, “Rural financial markets in low-income countries: Recent controversies and lessons.” World Development. Vol. 14, No. 4 (1986). pp. 477--188. Anderson, Dennis, and Farida Khambata. “Financing small scale industry and agriculture in developing countries: The merits and limitations of ‘commercial’ Developmenr and Cultural policies.” Economic Change, Vol. 32 (1984). Bolnick, Bruce R., “Concessional credit for small scale Bulletin of Indonesian Economic enterprise.” Studies, Vol. 18, No. 2 (1982). pp. 6.5-85. Bolnick, Bruce R., “Financial liberalization with imperfect markets: Indonesia during the 1970s; Economic Developmenr and Cultural Change, Vol. 35, No. 3 (1987). Braverman, A.. and J. L. Gausch, “Rural credit markets and institutions in developing countries,” World Development, Vol. 14, No. lo/11 (1986). pp. 1253-1268.

Casley, Dennis J., and Denis A. Lury, Monitoring and Evaluation

of Agriculrure

and Rural

Development

Projects (Baltimore, MD: Johns Hopkins University Press, 1982). Hanson, James A., and Roberto de Rezende Rocha. High Interest Rares, Spreads,

Two Studies .(Washington. DC: World Bank Industry and Finance Series Vol. 18. 1986). Kane, Edward, “Political economy of subsidizing agricultural credit in developing countries,” in Adams PI al. (1984). Khatkhate, Deena R., and Delano Villanueva. “Operation of selective credit policies in less developed Vol. 6. countries: A survey,” World Development, No. 7/S (1978) pp. 979-991. Levitsky, Jacob, World Bank Lending to Small Enterprises: A Review (Washington, DC: World Bank Industry and Finance Series Vol. 16, 1986). McKinnon, Ronald I., Money and Capiral in Economic Developmenr (Washington, DC: Brookings Institution, 1973). Nelson, Eric N., and Bruce R. Bolnick. “Survey methods for assessing small credit programs: Evaluating the economic impact of KIWKMKP credits,” in David C. Cole (Ed.), The lndonesinn Financial Sysretn (Singapore: Institute for Southeast Asian Studies. 1988). Shaw, E. S., Financial Deepening in Economic Developmenf (New York: Oxford University Press. mediation:

1973).

Von Pischke, J. D., Dale W. Adams. and Gordon Donald, Rural Financial Markers in Developing Counrries (Baltimore, MD: Johns Hopkins University Press, 1983).

and the Costs of Inter-

APPENDIX A: CLASSIFICATION An installment credit is classified as “smooth” when payment is current, or when a single installment is in

OF LOAN COLLECTIBILITY’ arrears for a period of less than six months. The loan is classified as “less smooth” when arrears exceed the

I

I

I

a

EVALUATING

LOAN

COLLECTION

above limits, but not more than two insrallments have been missed, covering a period of less than 12 months. The category “less smooth” also applies to loan accounts which have been overdrawn. for less than three’months. A loan which does not qualify for one of the first two classifications is “doubtful” if considered to be collectible, with collateral valued at 75% or more of the balance due. If it is uncollectible, it is still considered “doubtful” as long as the collateral covers the full

APPENDIX

PERFORMANCE

509

debt - notwithstanding the banks’ inability to foreclose. Otherwise a loan is “stuck.” For KMKP. “smooth” refers to loans with no interest arrears of two months or more. and no overdrafts. The category “less smooth” allows an extra month of interest arrears, an overdraft up to three months. or arrears of less than three months after the due date of the principal. “Doubtful” and “stuck” are defined in the same manner as above.

B: SOME COLLECTION

RATE ANALYTICS

1. Collection rate formulae

D are both failing. Example: Let D = 10, A = 100. R(D) = 0, and R(A) = 0.10. Then R/D = 1.O and CUM

For the purpose of analysis. the following notation is used:

is rising despite the poor current collection performance, and irrespective of whether this performance has gotten better or worse.

D = repayments falling due during a reference period,

t.

CD = cumulative repayments due as of the beginning of period t. R = repayments received during period t. CR = cumulative repayment> received as of the beginning of period r. In this context, the “cumulative collection rate” (CUM) can be expressed as: CUM

(at the beginning of period

CUM

(at the end of period

t) = -$$ x 100.

t) = CR + R x 1oo CD + D

The “collection rate” (COLL) Bank can be expressed as: COLL

favored by the World

R

=

D + (CD -CR)

x loo

Proposition 3. The COLL index can register a decline even though collection performance is (i) stable or (ii) improving. Proof: (i) For any lending program in a steady state at R* < D’ each period, with R(A) = 0 and no bad debts being written off. CD-CR will erow steadily. Hence, COIL asymptotically approaches zero. (ii) Given that COLL can decline with a stable collection performance, and that COLL is a continuous function of R, it follows that within a sufficiently small range of improvements in R(D)/D and/or R(A)/A the index COLL would still decline. More precisely. COLL declines as long as R increases by a smaller percentage than D+A. This condition is easily satisfied if last period’s recovery performance was poor so that arrears (and thus D+A) are increasing rapidly. Example: In period t, D=lO. R(D)=0.6. A=4, and R(AjIA=O, implying COLL = 43%. Then in period t+l, D=lO, R(D)/D rises to 0.7, A=S’(due to last period’s poor collection performance) and R(A)IA remains 0. Then COLL drops to 38% despite the improvement in R(D)lD.

Proposition

I. The end-of-period

stock measure CUM always exceeds the flow measure COLL. Proof: Observe that CUM differs algebraically from COLL in that the term CR is added to the numerator rather than being subtracted from the denominator. Moving from CUM to COLL the numerator increases proportionally more than the denominator, as long as

2. The “expected collection performance” (ECP) index Based on arrears-by-age data one can define the following quarterly atTears recovery rates:

R + CR < D + CD.

WT) =

Proposition 2. The CUM index can register an improvement even though collection performance is deteriorating. Proof: The change in CUM during period r,

T. = percentage of amounts less than 3 months overdue at the beginning of T which are collected during T. WT) = percentage of amounts 3 to 6 months overdue at the beginning of T which are collected during T. B(T) = percentage of amounts 6 to 9 months overdue at the beginning of T which are collected during T. P4(T) = percentage of amounts 9 to 12 months overdue at the beginning of T which are collected during T. WT) = percentage of amounts more than 12 months overdue at the beginning of T which are collected during T.

CR + R --CD + D

CR CD

is positive on the condition that RID > CR/CD. The latter term is less than one as long as past collection performance has been less than perfect. Define R(D)/D as the collection rate on payments newly falling due, and R(A)IA as the collection rate on arrears. where R(D) + R(A) = R, and A = CR - CD. The condition RID > CR/CD is satisfied if R(A) is sufticiently large. In turn, R(A) may be large simply because of a large accumulated volume arrears, even if R(A)IA and R(D)/

Pi(T)

percentage of amounts falling due during quarter T which are collected during quarter

WORLD DEVELOPMENT

510

These arrears recovery rates for each period Tcan be used to compute a composite “expected bad debt ratio” (EBDR) that measures the ultimate proportion of bad debts implied by the prevailing (period r) pattern of collectibility by age of arrears:’

PI(T) = 66l200 = P2(7-) = l-1.1/180 = f3(7) = 6.6/110 = PJ(7-j = 3160 = P5(7-) = 61300 =

EBDR = ;$u [I - Pi(T)].

Out of the 26% of payments due that are not collected contemporaneously (i.e., 1 - 0.74). 33% would be collected during the ensuing quarter, and so forth. Overall 14% would remain uncollected five quarters after the period due, given the observed pattern of collectibility by age of arrears. Hence EBDR = 14%. and ECR = 86%. In contrast, the COLL measure would indicate that only 45% of the amounts due during quarter T have been collected during the period. The contention here is that the 86% figure is far more useful information.

Correspondingly, the “expected collection rate” (ECR) can be defined as: ECR = 1 - EBDR. To illustrate, examine the following pattern of collection performance, adapted from an actual observation in Indonesia: PO(T) =

0.33 0.08 0.06 0.05 0.02.

740/1000 = 0.74

NOTES 1. These definitions are from Bank Indonesia, Pedoman Penyusunnn Loporun Bank-Bank (“Guide to Compiling Banking Reports”) (Jakarta 1979).

2. Compounding probabilities in this manner is entirely analogous to the demographer’s calculation of life expectancy from age-specific mortality rates.