Credit Information Systems in Less-Developed Countries:
A Test with Microfinance in Guatemala *JEL Classifications: O12, O16, L31, L13
Keywords: Credit Markets, Credit Bureaus, Asymmetric Information
Jill Luoto
University of California at Berkeley
Craig McIntosh
University of California at San Diego
Bruce Wydick
University of San Francisco
Short Title: Credit Information Systems

Jill Luoto: Dept. of Agricultural and Resource Economics, Giannini Hall, University ofCalifornia at Berkeley, Berkeley, CA 94720, e-mail: luoto@are.berkeley.edu
Craig McIntosh: School of International Relations and Pacific Studies, U.C. San Diego,
9500 Gilman Drive, La Jolla, CA 92093-0519, e-mail: ctmcintosh@ucsd.edu
Bruce Wydick: Department of Economics, University of San Francisco, San Francisco, CA
94117, e-mail: wydick@usfca.edu.
* We wish to thank Alain de Janvry, Betty Sadoulet, Tomas Rosada, seminar participants at
Princeton University, UC Berkeley, UC Davis, Participants in the December 2004 BASIS
Conference at UC Berkeley, and the 2002 WEAI meetings in Seattle for helpful comments andencouragement. Financial support from the Leo T. McCarthy Foundation, BASIS/USAID, andthe Fulbright IIE Program is gratefully acknowledged.

1

Abstract: Increases in formal sector lending among the poor have created aneed for credit information systems that provide potential lenders withborrower information. In this paper we present fixed-effects estimations that
attempt to measure the effect of a newly implemented credit information
system in Guatemala. Our results indicate that improved screening effects
from the system caused the level of portfolio arrears to decline approximately
two percentage points after it was implemented in branch offices.

We

observe an even more substantial and significant effect of the information
system in reducing late payments that occur during the loan cycle.

2

I. Introduction
The microfinance revolution has brought about unprecedented competition in credit
markets within many developing countries. Economists usually view competition favorably, but
increased competition in microfinance has resulted in a number of unforeseen difficulties.
Recent findings report greater competition between lenders has increased problems of borrower
over-indebtedness, reduced loan repayment incentives, and growing arrears for microfinanceinstitutions (MFIs) in competitive environments (Campion 2001; McIntosh and Wydick 2005).
The weakening performance of microfinance in competitive environments is due in partto the absence of information sharing in these markets. Because growing numbers of MFIs
increase the level of asymmetric information between lenders, credit information systems (often
called credit reporting bureaus or credit bureaus) can play a crucial role towards improving credit
market performance and, in turn, credit access for the poor.
The importance of information in credit markets is well established in seminal papers
such as Akerlof (1970) and Stiglitz and Weiss (1981). Credit information systems act as
information brokers that increase the transparency of credit markets. However, in many
developing countries, credit information systems are still in their infancy and information sharing
between lenders remains weak. As competition in microfinance lending intensifies in thesecountries, borrower information becomes all the more important. MFIs are increasingly utilizing
the services of credit bureaus to address a fundamental problem of all credit markets:
asymmetric information between borrowers and lenders that can lead to problems of adverse
selection and moral hazard. Motivated by industry survival amidst increasing competition, awide array of lending institutions in developing countries are becoming increasingly aware of theessential role that credit information systems play towards the creation and maintenance of an
efficient financial system.

3

This paper offers a descriptive account of the growth in credit information systems indeveloping countries and an empirical test of the effects of a newly implemented credit
bureau in Guatemala. Section II provides an empirical description of the credit reporting
environments found today in various parts of the developing world, offers a review ofprevious research on credit information systems, and gives a brief history of the developmentof credit information systems. In section III we present the data set and estimation techniqueused to capture the screening effects of the newly implemented Guatemalan credit bureau. In
Section IV we present our results, which largely confirm the positive role that information
sharing can play in improving lending performance. Our estimations indicate that improved
screening effects from the system caused the level of portfolio arrears to decline between 1-3
percentage points in the six months after it was successively implemented in each branch
office. We observe even more substantial effects of the system in reducing late monthly
payments made by borrowers. A cost-benefit analysis of the credit information systemshows that MFI investment in the system generated an estimated internal rate of return to theinstitution of 96.5%. Moreover, we find that in a competitive microfinance market, a
reduction in the default rate by our point estimate of 1.92 points would lower interest rates in
a competitive market by 2.59 percentage points. These positive effects are, of course,
sensitive to the impact of the system on reducing default rates. Section V concludes with
policy recommendations that stem from our research.
II. Credit Information Systems: Existence, Theory and Evidence
Credit Bureaus in the Developing World.
Throughout the developing world, the growing availability of consumer credit and theheightened competition between microfinance institutions have made the necessity of credit

4

information sharing all the more apparent. However, the extent and efficiency of informationsharing mechanisms vary greatly between countries and continents. Africa remains the region of
the world with the least developed credit information systems, yet the exploding microfinancesectors in many African countries have sparked interest in the feasibility of the creation of credit
bureaus to help manage borrower risk under heightened competition. Similarly, Asian
economies are witness to recent vast growth in credit bureau coverage, including increased
awareness of the necessity of information sharing among the multiple microfinance lenders inSouth Asia. For example, in Bangladesh, the “birthplace” of microfinance, increasing
competition between large microfinance lenders such as the Grameen Bank, the Bangladesh
Rural Advancement Committee (BRAC) and RD-12, has sparked a World Bank-assisted
initiative to introduce a specialized credit bureau into the country’s microfinance sector. This
credit bureau aims to build a national database in order to manage any systemic risk that might
be caused by the present uncoordinated competition among MFIs. The goal is that the credit
bureau can help to mitigate the rising problems of non-repayment of microloans and client
overlap among the largest MFIs in Bangladesh.1
Latin America arguably has the most extensive coverage of credit information systems
among developing regions, with credit information sharing recently being extended even into themicrofinance sector. A pertinent example is Bolivia. Prior to 1999 Bolivian law forbade theexistence of private credit bureaus (Campion 2001), believing credit data was too sensitive andimportant a topic to entrust to the private sector. Meanwhile, its public credit registry had
refrained from collecting information from its burgeoning microfinance sector while intensecompetition between MFIs was allowing clients to borrow from multiple institutions, and innumerous cases reaching unsustainable levels of debt, leading to skyrocketing default rates in the
microfinance sector in the late 1990s. The crisis led the Bolivian government to rewrite its laws

5

allowing for the existence of private credit bureaus, such that credit bureaus now operate in
Bolivia with a special focus on microfinance loans.
Theoretical Work.
The subject of credit information systems operating in the microfinance sector has been
virtually untouched by the academic literature. McIntosh and Wydick (2004, 2005) show thatthe existence of a credit bureau may improve credit access for the poorest borrowers. Assumingthat credit markets are competitive, information sharing lowers lender costs through lower
default rates. This implies that in a zero-profit equilibrium, borrowers with lower levels of initialassets become added to micro-lender portfolios. Other research is mostly limited to a numberof case studies (Campion and Valenzuela 2001; Lenaghan 2001; Abreu 2001).
A large body of work, however, examines the role that information sharing will play inmore developed credit markets. Using a pure adverse selection model, Jappelli and Pagano(1993) analyze the factors that lead to endogenous communication between lenders in a credit
market. They find that information sharing is more likely to occur when the mobility ofhouseholds is high, the pool of borrowers is heterogeneous, the credit market is large and thecost of information exchange is low. Fear of competition can make lenders hesitant to share
their client information, yet a credit bureau is a “natural monopoly” with increasing returns toscale: when some lenders begin to share information, it creates an incentive for other lenders toshare information as well.
Vercammen (1995) and Padilla and Pagano (2000) argue that limits to the informationexchanged between lenders can lead to more optimal results. Vercammen uses a multi-period
model with adverse selection and moral hazard to show that a certain level of adverse selection
is required in a credit market in order to give rise to borrower reputation incentives and thus
aggregate welfare. He concludes that a system of full information sharing may be less efficient6than one designed to preserve some level of asymmetric information, such as limiting the lengthof borrower data that is maintained.
Padilla and Pagano (2000) focus on the effect of information sharing as a “borrower
discipline device” under perfect competition. They conclude that borrowers have greater
incentive to perform if lenders only exchange negative information, arguing that sharing positive
borrower characteristics can ease the negative impacts of default and mitigate the disciplinary
effect of a credit bureau. Jappelli and Pagano (2000) posit that the usefulness of credit bureaus
is reduced in developing countries where large informal sectors exist in which enforcement of
repayment compliance is difficult. They suggest that granting credit bureau access to informal
lenders would increase the credit bureau’s usefulness for both formal and informal lenders, due
to the economies of scale that defines the industry. Jappelli and Pagano also argue that better
information may lead banks to shift from collateral-based lending policies to more informationbased policies.
Margaret Miller’s (2003) Credit Reporting Systems and the International Economy provides acomprehensive source for the institutional aspects of credit reporting. Miller’s own chapter inthe volume uses results from a World Bank Internet survey to offer empirical data on the statusof credit reporting activities around the world. She shows how credit bureaus can provide
borrowers with “reputation collateral”, frequently viewed as more valuable than physicalcollateral by surveyed lenders. Furthermore, Miller argues that the types of data collected by acredit bureau often provide the best predictors of repayment.
Jappelli and Pagano (2002) provide an initial empirical investigation of the existence andimpacts of credit bureaus in various economies around the world. They find that the presenceof credit information systems is associated with broader credit markets and lower credit risk.

7

Nevertheless, rigorous evaluation methods on the effect of credit bureaus in developing
countries is non-existent in the literature, creating a gap which this paper attempts to fill.
Information Sharing Arrangements.
How does a credit information system improve the functioning of a credit market? As
shown formally in McIntosh and Wydick (2004), credit information systems first create ascreening effect that improves risk assessment of loan applicants, thereby raising portfolio quality,
which in turn reduces rates of arrears. Second, their very existence creates an incentive effect thatmay deter negligent borrower behavior as information about borrower behavior is shared among
lenders. Some borrowers who are on the margin of misusing borrowed capital may be dissuaded
from doing so if they sufficiently value future access to loans. In a competitive credit market,
these efficiencies are passed on to borrowers in the form of a lower cost of capital. Improved
informational flows thus enhance the efficiency and stability of the entire financial system. Yet
because of the public good characteristics of credit information systems, their natural emergencein the credit market is not always guaranteed.
Consequently, the breadth, depth and general efficiency of credit information systems
vary greatly between countries. Credit reporting, at some level, is a critical part of the financial
system in most developed economies; in developing countries it is often much weaker if notaltogether absent. This is because in a zero-information-sharing environment, repayment
discipline in credit transactions typically happens via the oft-repeated transactions between aborrower and a single familiar lender in less-developed countries (LDCs). However, because
borrowers often lack the ability to send signals of their creditworthiness to the entire pool ofpotential lenders in LDCs, they are more susceptible to borrowing terms being dictated by asolitary lender with whom they have had a past borrowing relationship. In this way
informational flows between lenders can paradoxically shift market power to borrowers.

8

The most basic level of information-sharing between lenders involves sharing only
negative information, such as borrower defaults and arrears. The simple creation of a public“black list” produces both screening and incentive effects, mitigating both adverse selection andmoral hazard problems in the credit market. The existence of the blacklist helps lenders to
avoid risky borrowers, and the fact that borrowers want to avoid being on the black list
improves repayment incentives for borrowers who make it into the lending portfolio.
The most advanced information-sharing arrangements, however, include positive
borrower data in addition to the negative data. Positive data, or a “white list”, may include thedebtor’s overall loan exposure and guarantees, data from past credit history other than defaults
and arrears, and debtor characteristics such as employment, income or line of business (Jappelli
and Pagano 2000). The sharing of positive information allows for the debtor to create vital“reputation collateral” often in the form of a credit score, which can provide valuable
information to the credit market, and signal a borrower's individual credit worthiness to a largepool of lenders. As demonstrated in McIntosh and Wydick (2005), the sharing of positive
information helps to mitigate borrower over-indebtedness, lower default rates in the overall
credit market, and (in competition) to reduce equilibrium interest rates.

III. Empirical Work: Hypotheses and Data
The subject for our empirical work is CREDIREF, a newly implemented, specialized
credit bureau covering the microfinance sector of Guatemala. By the late 1990s, multiple loantaking by MFI clients had become so extensive that REDIMIF, an association of 19 MFIs,
joined to establish CREDIREF, a centralized microfinance credit bureau, which has beenfunctional since March, 2002, in the central and eastern areas of Guatemala. Provoked by adesire to avoid an industry-wide repayment crisis similar to that experienced in Bolivia,

9

CREDIREF’s operations have grown steadily since its inception. By January of 2003, its
database held information upwards of 120,000 borrowers. CREDIREF now collects both datafrom six of the largest microfinance lenders in the country, while other MFIs are slowly being
incorporated into the system.
Participation in CREDIREF includes a flat $70 monthly membership fee, plus anadditional cost per consultation that decreases as the number of consultations rises. The first
100 consultations made per month each cost $1.60, and prices decrease steadily to $0.67 per
consultation made over 6400 per month. Due to this fee structure and the potentially significant
fixed costs of upgrading systems to provide digital reports, some small MFIs are unable to afford
consultation fees. In other countries credit bureaus have alleviated this problem somewhat withthe solution of charging a membership fee that is scaled to the size of each participating
institution. (For instance, Bolivia’s public credit registry charges participating financial
institutions an annual fee equal to 1/1000th of their total assets (Campion 2001).)
CREDIREF collects and distributes a variety of data on microfinance borrowers,
including positive and negative payment information. The data it collects on a borrower include
the name, national identification number, size of current or most recent loan, whether the client
is in default on any loan or payment, size and frequency of payments on current loan(s), and a
two-year history on the borrower’s repayment record. The name of the lending institution is notincluded in a borrower’s credit report in CREDIREF, an attempt to mitigate fears of clientstealing between lenders. Additionally, CREDIREF does not provide a credit-scoring service,leaving the individual institutions responsible for performing their own evaluations of potential
borrowers.
We perform an impact analysis on CREDIREF’s introduction using data from theaccounts of Génesis Empresarial (“Génesis”), a large-scale MFI and CREDIREF member that10

has 40 branch offices located throughout the country. The different branches began using thecredit bureau at varying times. Panel data on the branch-level monthly arrears of Génesis
provides a dynamic measurement of the credit bureau’s impact as it was implemented office byoffice. To supplement this administrative data, during the summer of 2003 we carried out
fieldwork, researching the implementation of CREDIREF in Guatemala. We surveyed 184 of
Génesis’ clients from six branch offices to learn what changes the introduction of a credit
bureau has made in their borrowing behavior. The six branches visited were selected in orderto have a wide range of implementation dates as branches enter into the credit bureau (seeFigure 1 for a map of the branch offices of Génesis, including offices selected for surveys).
Génesis provides both positive and negative client information to CREDIREF and isconsulting the database on an increasingly high share of loans disbursed. In June 2003, forexample, Génesis branches made a total of 1266 consultations to CREDIREF’s database, of
which 787 provided information on outside borrowing activity, not otherwise observable to thelender. However, despite the growing importance of CREDIREF to Génesis operations,Génesis clientele had remained largely uneducated about the credit bureau’s existence andoperation. Of 184 surveyed clients from 6 branch offices from June to August of 2003, not one
was aware of the credit bureau’s creation.2 This finding is consistent with prior researchshowing virtually no attention has been paid to educating consumers about credit reporting in
Latin America (Miller 2003).
This dampens the effectiveness of the credit bureau, as knowledge of the existence ofthe credit information system is required to produce an “incentive effect”. If borrowers areunaware that their loan histories are being shared among various lenders and do not understandthe implications of this, borrowing behavior will remain unchanged and the total effect of credit
bureau implementation will consist of solely the screening effect.

11

As a result, our empirical work consists of a test for the pure screening effect of thecredit bureau. We generate data to focus on the screening effect by writing the repayment
performance of a loan onto the day on which it was received. This generates the possibility of adiscontinuity in outcomes between those selected just before the use of a bureau and those
selected just after.
The implementation of CREDIREF was technologically complex, requiring the branch
offices of Génesis to improve hardware, software, and networking capabilities. For this reason,
the rollout was staggered over the course of 18 months, taking place in nine different waves.
Table 1 outlines the process.
We take the 34 administrative branch offices of Génesis that saw implementation of thebureau during the study period as the unit of analysis. Outcomes are calculated as monthly
averages, and the 1,504 bureau/month observations form an unbalanced panel because some
Génesis branches began operations after September 2000. We lack detailed, time-varying
control data on the branches and so while we are able to conduct a variety of statistical tests wemust in general assume that time-varying, branch-specific determinants of repayment areorthogonal to the structure of the rollout of the bureau.
One possibility that would affect the accuracy of our estimations would be if theinstallation of the new hardware and software needed to run the new system affected repayment
rates in other ways than the influence of the credit information system itself. We view this as
unlikely. Borrower repayment rates are influenced most directly by the screening of clients, theproductivity and behavior of borrowers, shocks received by borrowers, and the fieldwork
practices of loan officers. Since the introduction of the system left other functions of the MFI12unchanged except the added screening mechanism in the loan review process, it is unlikely thatthe introduction of new computers, servers, and software needed to operate the system had any
direct effect on loan repayment by borrowers
As a measure of loan delinquency, we feature the percentage of loans in a branch thatwere late as of the last payment made. This is a very sensitive measure of repayment which picks
up roughly twice the number of problems as the technical definition of default used by theinstitution (which includes only loans beyond a month overdue). We feature this measure
because it varies more than technical default, and also because it can be calculated directly fromthe accounts of the institution. The data otherwise available from the institution on technicaldefault in a month is a stock of bad loans, whose month-to-month changes are a composite offorgiveness and new defaults, and so is difficult to interpret.
We also examine three additional outcomes: the percentage of loans in a branch/monthon which a payment is ever late; the average number of late payments per loan in abranch/month; and the average number of months late for loans issued in a branch/month that
become delinquent, which measures the severity of repayment problems. Because somebranch/months have no delinquency, this variable is missing for almost half the observations.

13IV. Empirical Results
As a first take on the data, Figure 2 plots the percentage of loans ever late and thepercent delinquent over time. There is a clear visual improvement in the “percent ever late,”
while any potential decrease in delinquency appears more modest. The nature of the quasiexperiment used for identification can be more clearly seen in Figure 3, which lines up thestaggered entry of the branches into CREDIREF so that 0 indicates the month in which every
branch entered. Now there appears to be a clearer impact on both outcomes. “Percent everlate” jumps discontinuously and appears to have a kink in the slope, and while the effect ondelinquency is less clear, it does appear to have fallen fairly discontinuously from a mean of 20%
to a mean of 18% or so.
In order to formalize the analysis of a discontinuous treatment effect, we pursue an
iterative set of t-tests to examine how many months must be considered before a paired t-test of
pre- and post-treatment means within branches becomes significant. We begin by comparing
one month before treatment to one month after, then the mean across the two months before
treatment to the mean two months after, and so on. Figures 4 and 5 plot the results of thisexercise. The effect on delinquency appears to be continuous; the first month is very close tozero and insignificant, while by the fourth month we see significance of a difference of close to
three percentage points. The impact on “percent ever late”, however, does show discontinuity;
within the first month a difference of greater than four percentage points exists. While on
average over two months is required in order to smooth outcomes and reduce variance14

sufficiently to generate significance, the difference is essentially unchanged as the window
expands.
A more fine-grained set of t-tests is presented in Table 2. Here we test branch by branch
for pairwise differences in the pre-treatment and post-treatment levels of delinquency, using alltime periods available. The results are again quite clear; of the 13 branches with significant
changes at the 90% level, only one of those saw higher post-treatment delinquency; in theremainder it decreased. The branch with increasing delinquency was already an outlier in termsof levels; moreover, this branch, Zona 4, is located in a part of the capital which saw spiraling
drug-related violence during the study period.
T-tests provide an intuitive way of approaching the question of impact in a quasiexperiment, and have the additional advantage that in cases of autocorrelated outcomes, suchbefore-after mean comparisons remain consistent (Bertrand, Duflo and Mullainathan 2003).
Once we are including a longer time period in our analysis, however, it becomes necessary to
introduce controls for time in order to avoid spurious treatment effects.
Table 3 presents a set of analyses using month- and branch-level fixed effects:
Outcomeit = α i + γ t + δCBit + µ it , where α i is the branch-specific fixed effect, γ t is amonth dummy, CBit is the treatment indicator and µ it is an error term.
Two treatment indicators are used. The first, a dummy equal to zero before the bureau
was rolled out to a branch and one after, indicates the presence of the bureau, while a variablemeasuring the number of checks performed in the bureau each month measures the intensity of15

use. The strongest impact seen here is a huge decrease in the average number of missed
payments; this outcome responds strongly to both presence and intensity of use of the bureau.
The use of the bureau decreases the percentage of loans on which any payments are missed by
3.3 percentage points and the number of missed payments by 1.3. Interestingly, the impact of
the bureau on delinquency, at 1.1 percentage points, is both smaller than that seen in the t-tests,
and is insignificant. Regressions performed using less robust error structures resulted in an
outcome that was just significant at the 95% level, but the presence of autocorrelation withinbureaus sufficiently increases the standard error as to render a p-value of only 0.20. The numberof months by which late payments were late shows no change.
If instead of controlling for time using month dummies we use a linear time trend, we
can check not only for the mean effect of the bureau but for the presence of a kink in the rate ofchange of outcomes after the introduction of the bureau:
Outcomeit = α i + βt + δ CBit + ρ (CBit * t ) + µ it
Table 4 presents the results of estimations performed using fixed effects at the branch level anderrors robust to clustering within branches. The result for “delinquency” here is very much inline with the visual impression given by Figure 2; a level drop of about 2% with little change inslope. The results for “percent ever late” coincide with Figure 3; this outcome as well as thenumber of late payments and the months late variable show a strongly decreased trend after the
implementation of the bureau but no level effect. The implication is that while the bureau had afixed, small effect on delinquency, the other indicators saw an improvement which was16increasing in use-time of the bureau. These results would be consistent with the need to learn
how to use the bureau to maximum advantage.
An additional question is whether the magnitude of the treatment effect is related to thelevels of the outcome variables when the bureau was introduced. By taking the last outcome
variable observed before the use of the bureau and interacting this (demeaned) outcome with thetreatment dummy, we can answer this question. We continue to use branch- and month-level
fixed effects, and therefore do not need to include the un-interacted pre-treatment outcome
because it is subsumed in the branch-level fixed effect:
Outcomeit = α i + γ t + δ CBit + ρ (CBit * Outcome0i ) + µ it
The results of this exercise are presented in Table 5. Again we observe that the use ofmonth-level fixed effects eliminates the significance of the bureau on delinquency, but all threeof the other outcomes display strong negative interaction effects. The implication is that thehigher the level of the outcome was prior to the use of the bureau, the larger the fall in outcomesonce the bureau was introduced. Hence, the bureau is most effective at reducing late payments
and months in delinquency where these problems were worse before implementation.
Robustness Checks
The value of the quasi-experiment generated by the staggered rollout of CREDIREF formeasurement purposes depends on the timing of that rollout. If, for example, the sequencing of
the rollout is correlated with expected rates of change in the outcomes, then a bias will be
introduced. Equivalently, if branches in crisis at any moment in time were given the bureau and17the outcomes display a tendency toward mean reversion, then our impact estimate would be
biased downwards. If true, mean reversion would influence the observed interaction effects inTable 5.
In order to test for endogeneity in the sequencing of the rollout, we perform a numberof comparisons of pre-treatment averages with the time at which the bureau was received. This
analysis is performed both in levels; which tests for whether the sequencing was endogenous tothe outcomes, and in first differences, which tests for endogeneity in the rate of change. Time ofrollout is measured using a variable which begins at zero in January 2000 and increases by one
for every month thereafter. Table 6 presents the results of OLS regressions of these outcomeson the rollout time, and Spearman rank correlation coefficients for whether there is correlation
between the rank of the outcomes and the rank in which the bureau was received. The firstdifferences show no relationship to the sequencing of rollout, although the level of delinquency
is nearly significant at the 90% level in the Spearman test. Figure 6 plots this relationship, as wellas the fitted values from the OLS regression. We see that indeed there is a slight downward
trend (meaning that those with higher initial delinquency got the bureau first), but that the
problem is not severe.
Since rates of change are not correlated with the rollout, the primary concern raised bythis analysis is that, in the presence of mean reversion, some structural correlation may exist
between the order of the rollout and subsequent rates of change. To test for mean reversion,
Table 7 shows the results of similar OLS and Spearman tests for whether a correlation exists

18between the first observation of each outcome in the data, and the subsequent trend in thatoutcome prior to the introduction of the bureau. There is no evidence here for mean reversion,
an impression which is reinforced by Figure 7 which plots initial levels against subsequentchanges, along with the fitted OLS values. The absence of mean reversion in the data isreassuring not only in confirming the basic structure of this quasi-experiment, but in our ability
to draw casual inference from the results of the interactions presented in Table 5.
Assessing Costs and Benefit of a Credit Information System
Given that our results indicate significant impacts on portfolio quality, we investigate
whether the discounted benefits of a system such as CREDIREF exceed the discounted costs of
its implementation. We address several pertinent questions: Are the benefits to an individual
MFI of a credit information system, as we measure them, worth the cost of its implementation?
Based on our estimates, what is the probability that the net present value of such a system to acredit institution is greater than zero? And finally, what are the welfare implications of such asystem being implemented and used by all MFIs?
According to administrators, the costs to Génesis were principally comprised of
installation of hardware, new telephone lines, and additional furniture to each of their 40 branch
offices needed to support the system. (Here we convert all figures from Guatemalan quetzales, toU.S. dollars.) For each branch office, new hardware amounted to approximately US$8000, newtelephone lines US$450, and furniture approximately US$800. Total software costs to theinstitution were US$1,280, making the total costs of installation to Génesis equal to US$371,280.

19

(These assets were expected to need replacement or updating on average every three years; wetherefore use this three-year time horizon in our analysis.) CREDIREF is operated by anoutside entity, Banca Red, to whom Génesis pays the fixed monthly fee of US$70 plus an averageof US$1.14 for each consultation to the database plus a US$0.26 telephone charge. On averageadministrators report about 2,000 consultations per month, making the variable costs ofconsultations equal to US$34,400 per year.
At the end of 2004, the total value of Génesis’ portfolio of microloans was
US$25,441,273. We have several estimates of the effect of the information system in our
research. The overall average drop in observed branch-level pre- and post-treatment arrears wasshown to be 2.97 percentage points. Our fixed effects estimation using month dummies yields
an (insignificant) drop of 1.12 percentage points, while our estimation controlling for (before
and after) trends shows a (significant) drop of 1.92 percentage points. Given its significance andthe importance of controlling for trends, we use the latter in our analysis.
Not all loans in 30-day arrears are written off and represent a loss to the institution.
Consistent with the observed pattern in Génesis, we will thus assume that approximately half of
this amount can be recovered at little or no cost to the institution and/or is compensated for byinterest penalties, while the other half is either written off or is recovered only at a cost equal tothe debt itself. We will refer to this figure as the default rate. This would suggest that
CREDIREF is able to save Génesis US$244,745 per year in allowances for bad debt. Assuming a
discount rate of 10 percent (a rough approximation of the constantly varying weighted cost of20

funds to the MFI through a combination of commercial borrowing and below-market loans),
this yields a net present value of the CREDIREF information system over three years to Génesis
of US$185,570, or US$61,857 per year. The net present value is fairly insensitive to the interest
rate; implementation of the system carries a colossal internal rate of return of 96.5%. It is,however, sensitive to the reduction in default rate yielded through implementation of the system.
For the net present value of implementation of the system to remain positive, the reduction in
30-day arrears must exceed 1.34 percentage points. A standard error on the estimate of 0.841
implies that implementation carries a projected positive net present value at slightly more than a
75% confidence level, assuming the level of 30-day arrears reduction is the only stochastic
element in system implementation.
The reduction in arrears from the system should also reduce the interest rate offeredby the institution if microfinance markets are competitive and borrower information is widely
accessible. If economic profits per dollar loaned are zero, then (1 − d )(1 + r ) − (1 + c + F ) = 0 ,where d is the default rate, r is the interest rate, c is the cost of borrowing, and F is theadministration cost per dollar on a new loan of average size. Using December 2003 figures for
Génesis we have (approximately) that d =0.04, r = 0.36, and c = 0.10. Solving for F, we find thatin equilibrium F would be equal to about 0.15. If we solve for the institutional interest rate anddifferentiate with respect to the default rate, we obtain∂r 1 + c + F=≈ 1.35 . Thus for our∂ d (1 − d )2

estimated system impact of a 1.92 percentage-point decline in the default rate, we would estimate
a decline in a competitive interest rate of approximately 2.59 points.

21

V. Conclusions and Implications for Policy
Credit information systems help to build an efficient financial system by promoting
transparency in lending. They are effective tools towards mitigation of adverse selection andmoral hazard in credit markets, and have been found to lower overall default and interest rates
and improve the pool of borrowers in formal credit markets. A survey of credit information
systems worldwide reveals that developing countries are quickly realizing the importance andusefulness of information sharing, and that there has been burgeoning growth in theimplementation of such systems in the last decade, particularly in Latin America and Asia. The
explosion in microfinance activity in developing countries has contributed to this need. We
presented evidence that the beneficial effects of credit information systems are to be foundwhen bureaus are utilized in the microfinance sector. We believe that the increased competitionin many regions among microfinance lenders has made credit bureaus a necessary step towards
financial sector stability.
CREDIREF has had a strong impact in terms of decreasing the prevalence of missed
payments in Génesis. The introduction saw a discontinuous drop in the percentage of clients
missing a payment, and the effect gathered strength over the course of the time the bureau wasused. The fall in the percentage of clients missing payments is 4.5 percentage points plus anadditional .3 percentage points per month the system is used. Impacts on the number of latepayment is similarly dramatic. Evidence for the impact of CREDIREF on delinquency is more
mixed; using t-tests or a regression with fixed effects and a linear time control we find asignificant reduction of 1-3 percentage points as a result of the use of the system, where in someestimations the effect becomes insignificant. The implication is that there was a non-linear fallin delinquency after the bureau that was common to all of the branches; how we ascribe
causality for this drop determines whether or not the causal effect of the bureau is significant on

22

delinquency. The number of months by which late payments are late shows no immediate
improvement but there exists a gradual improvement over time; two days shorter delinquency
period for each month that CREDIREF is used. There is evidence that CREDIREF has thestrongest impact in environments where the repayment problems were worst to begin with.
How are we to interpret this strong fall in missed intermediate payments without acorrespondingly strong impact on eventual delinquency? One answer would relate to theadditional kinds of information to which credit officers gain access through the use of thesystem. If we assume that borrowers who have had a given type of repayment problem with one
lender will continue to have the same kinds of problems with other lenders, one possible
explanation presents itself. Applicants for loans already go through extensive screening
procedures which include interviews with neighbors and business associates. It may be that pastdefault is a sufficiently dramatic and public event that pre-existing screening mechanisms were
sufficient to catch such applicants. Missing payments, however, is likely to be the private
information of the lender and borrower, and so it may be this more nuanced understanding ofpast performance that is the unique contribution of CREDIREF to the information set
possessed by credit officers. Seen from this perspective, the fact that the impact on intermediatepayments is much stronger than on eventual default is reasonable.
An extension of this work is the importance of the incentive effect formally derived in
McIntosh and Wydick (2004) in realizing the full benefit of a positive information-sharing
network. Our finding during fieldwork that not one surveyed client in Guatemala had beenaware of the credit bureau’s existence was disturbing. With borrowers unaware of their role in
an information-sharing arrangement, incentive effects cannot be realized. This causes the fullpotential for credit bureaus to remain unrealized in improving credit market performance.
Given that the estimates of impact given here consist solely of the screening effects, they should23be viewed as a lower bound on the full impact of the introduction of a robust credit-reporting
system whose rules are well understood by borrowers. Client outreach and education in thisregard may have efficiency benefits in addition to being equitable.
As credit markets continue to expand and overlap, the functioning of credit bureaus
takes on greater importance. While the microfinance revolution provided many with access tocredit for the first time, its ability to continue to do so depends upon sustainability of themarket. The potential for credit bureaus is immense in this area.

24

REFERENCES
Abreu, Saúl. 2001. “Case Studies of Selected Credit Bureaus in Several Latin American
Countries.” GTZ: Department 41, Financial Systems Development and Banking Services(October).
Akerlof, George. 1970. “The Market for ‘Lemons.” Quarterly Journal of Economics 84 (3): 488-500.
Bertrand, Marianne, Esther Duflo, and Sendhil Mullainathan. 2003. “How Much Should We
Trust Difference-in-Differences Estimates?” Working Paper, Massachusetts Institute ofTechnology, Department of Economics.
Campion, Anita. 2001. “Client Information-sharing in Bolivia.” Journal of Microfinance 3(1).
Campion, Anita, and Liza Valenzuela. 2001. “Credit Bureaus: A Necessity for Microfinance?”
Microenterprise Best Practices, Development Alternatives, Inc., Bethesda, Maryland.
Del Villar, Rafael, Alejandro Díaz de León, and Johanna Gil Hubert. 2003. “Regulation of
Personal Data Protection and of Credit Reporting Firms: A Comparison of Selected
Countries of Latin America, the United States, and the European Union.” In Credit
Reporting Systems and the International Economy, ed. Margaret J. Miller. Cambridge, MA: MIT
Press.
Hoff, Karla., and Joseph E. Stiglitz. 1998. “Moneylenders and Bankers: Price-Increasing
Subsidies in a Monopolistically Competitive Market.” Journal of Development Economics 55:
485-518.
Jappelli, Tullio, and Marco Pagano. 2002. “Information Sharing, Lending and Defaults: CrossCountry Evidence.” Journal of Banking and Finance 26 (10) (October): 2017-2045.
Jappelli, Tullio, and Marco Pagano. 2000. “Information sharing in Credit Markets: A Survey.”
CSEF Working Paper no. 36, University of Salerno.

25

Jappelli, Tullio, and Marco Pagano. 1993. “Information sharing in Credit Markets.” Journal of
Finance 48 (5) (December): 1693-1718.
Kitsin, Samma. 2004. “Credit Information-sharing in Thailand”. Conference on Credit Bureau
Development in South Asia, mimeo.
Lenaghan, Tom. 2001. “Microfinance and the Market for Credit Information in El Salvador.”
Microenterprises Best Practices, Development Alternatives, Inc., Bethesda, Maryland.
McIntosh, Craig and Bruce Wydick. 2004. “A Decomposition of Incentive and Screening
Effects in Credit Market Information Systems.” Working Paper, University of California
at San Diego/University of San Francisco.
McIntosh, Craig and Bruce Wydick. 2005. “Competition and Microfinance.”
Journal of Development Economics, 78 (2) (December): 271-298.
Miller, Margaret J. 2003. “Credit Reporting Systems around the Globe: The State of the Art in
Public Credit Registries and Private Credit Reporting Firms.” In Credit Reporting Systems
and the International Economy, ed. Margaret J. Miller. Cambridge, MA: MIT Press.
Morduch, Jonathan. 1999. “The Microfinance Promise.” Journal of Economic Literature 37 (4):1569-1614.
Olegario, Rowena. 2003. “Credit Reporting Agencies: A Historical Perspective.” In Credit
Reporting Systems and the International Economy, ed. Margaret J. Miller. Cambridge, MA: MIT
Press.
Padilla, Jorge A., and Marco Pagano. 1997. “Endogenous Communication among Lenders andEntrepreneurial Incentives.” Review of Financial Studies 10 (1) (Spring): 205-236.
Padilla, Jorge A., and Marco Pagano. 2000. “Sharing Default Information as a Borrower
Discipline Device.” European Economic Review 44 (10) (December): 1951-1980.

26

Vercammen, James A. 1995. “Credit Bureau Policy and Sustainable Reputation Effects in
Credit Markets.” Economica 62(248) (November): 461-478.
Viewpoints. (Fair Isaac Publication) October/November 2003, http://www.fairisaac.com.
World Bank. “Maps of Private and Public Credit Reporting Institutions,” 2002.
http://econ.worldbank.org/programs/credit_reporting/topic/16017
Stiglitz, Joseph E., and Andrew Weiss. 1981. “Credit Rationing in Markets with Imperfect
Information.” American Economic Review 71 (3) (June): 393-410.

27

TABLE 1
Order of Branch Introduction to Credit Bureau:
Month ofIntroduction Branch Name
August, 2001 Metro Dos, Zona 4, Santa Lucia
February,

Guatemala Personal, El Castano, Chimaltenango, Antigua, San Juan Sacatepequez,

2002

Tecpan, El Castano

April, 2002

Coban, Salama, San Pedro Carcha, San Cristobal, Tactic

June, 2002

Esquintla, Suchitepequez, Retalhuleu, Chiquimulilla, Guastatoya

August, 2002

Jutiapa, Jalapa, Cuilapa

September,
2002

Zacapa, Chiquimula, Esquipulas

October,
2002

Peten, Poptun, Melchor de Mencos

November,
2002

Izabal, Raxruha, Sayaxche, Morales, Los Amates, El Estor

January, 2003

La Libertad

Not
Included:Pozo Maya

Note: Prior to March 2002, dates signify when offices began consulting INFORNET, a complementary risk-management database to CREDIREF.
INFORNET closed operations in May of 2003 and now CREDIREF operates alone. INFORNET provided only negative information on press
publications, court rulings, any public record information, credit card and bank account information. Treatment is considered to be the same whether
date of introduction is to CREDIREF or INFORNET for each branch.

28

TABLE 2
Summary Statistics of Delinquency across Branches:
PrePostTreatment
Treatment
BranchArrears
Arrears
3 Metro Dos, Zona 4
2.65
5.25
4 Metro Tres, El Castano
22.16
25.98
5 Chimaltenango
11.12
12.65
6 Antigua
16.00
18.09
7 Esquintla
24.05
20.41
8 Suchitepequez
12.85
11.80
9 Retalhuleu
17.71
16.15
11 Izabal
23.91
16.99
12 Zacapa
19.96
11.67
13 Chiquimula
19.46
22.72
14 Coban
32.96
29.41
16 San Juan Sacatepequez
18.98
19.22
17 Jutiapa
21.09
20.20
18 Jalapa
23.36
17.49
19 Peten
15.36
14.82
21 Cuilapa
21.48
15.39
22 Esquipulas
17.44
18.70
23 Chiquimulilla
16.36
14.36
24 Santa Lucia
16.44
18.35
25 Salama
16.51
10.16
26 Guastatoya
31.60
24.44
28 San Pedro Carcha
23.54
22.35
29 Rabinal
32.34
16.52
31 Raxruha
19.56
10.17
32 Poptun
15.84
9.98
16.81
11.52
33 Sayaxche
34 La Libertad
15.10
16.82
35 Morales
24.74
18.40
22.35
15.02
36 Los Amates
37 San Cristobal
24.54
21.51
38 Tactic
35.04
23.16
39 El Estor
37.34
16.03
40 Melchor de Mencos
18.01
11.37
41 El Castano
8.69
8.73
Overall:
20.09
17.12
*=90% significant, **=95%, ***=99%.

29

Change2.60 ***
3.82
1.53
2.09
-3.64 *
-1.05
-1.56
-6.92 **
-8.29 ***
3.25
-3.55
0.24
-0.89
-5.86
-0.54
-6.09 **
1.26
-2.00
1.92
-6.35 ***
-7.16 **
-1.20
-15.82 ***-9.40 **
-5.86 ***
-5.30
1.71
-6.34 *
-7.33 *
-3.03
-11.88-21.31 ***
-6.64
0.03
-2.97 ***TABLE 3
Fixed-Effect Regressions with Month Dummies:
Treatment Variable:
Dummy for use of
Bureau# Checks/Month#
Outcome Variable:

Coeff

SE

P>|t|

Coeff

SE

P>|t|

obs

% Delinquent-1.122

0.860

.201

0.0005

0.006

.933

1,504

% Ever Late

-3.326**

1.490

.032

-0.0150

0.010

.131

1,504

# Late Payments-1.311***

0.401

.002

.0084***

0.002

.000

1,504

Months, if delinquent-0.222

0.488

.652

-0.0049

0.003

.121

824

*=90% significant, **=95%, ***=99%.
All regressions are weighted by size of loan portfolio, and use robust clustered standard errors.

30

TABLE 4
Fixed-Effect Regressions with Before/After Trends:
Outcome Variable:

Coeff

SE

P>|t|

% Delinquent

# obs
1,504

Time trend

0.019

0.057

.740

Bureau dummy-1.924**

0.841

.028

Trend after bureau-0.117*

0.068

.094

% Ever Late

1,504Time trend-0.312*

0.163

.063

Bureau dummy

0.379

3.065

.902

Trend after bureau-0.799***

0.230

.001

# Late Payments

1,504Time trend-0.069*

0.039

.082

Bureau dummy-0.253

0.664

.705

Trend after bureau-0.159***

0.054

.006

Months, if delinquent

824

Time trend-0.190***

0.031

.000

Bureau dummy-0.145

0.506

.777

Trend after bureau-0.070**

0.032

.036

*=90% significant, **=95%, ***=99%.
All regressions are weighted by size of loan portfolio, and use robust clustered standard
errors.

31

TABLE 5Interactions with Outcomes Before Rollout of Bureau:
Coeff

SE

P>|t|

#
obs

Credit bureau dummy-1.334

0.885

.141

1,440

Int. w/ pre-bureau level-0.087

0.083

.306

Credit bureau dummy-4.585***

1.401

.003

Int. w/ pre-bureau level-0.313***

0.069

.000

Credit bureau dummy-1.429***

0.324

.000

Int. w/ pre-bureau level-0.444**

0.036

.000

Credit bureau dummy-0.219

0.342

.526

Int. w/ pre-bureau level-0.451***

0.050

.000

Outcome Variable:% Delinquent% Ever Late
1,440

# Late Payments
1,440Months, if delinquent806*=90% significant, **=95%, ***=99%.
All regressions are weighted by size of loan portfolio, and use robust clustered standard errors.

32

TABLE 6
Tests for Endogeneity of Rollout.
Coeff

OLS

P>|t|

SpearmanP>|t|Levels of outcomes:% Delinquent-2.239.27.11% Ever Late

-2.359.52.23% Delinquent-0.118.61.45% Ever Late

-0.274.42.50First differences:

Independent Variable:
Month in which bureau received, Jan 2000=1.

33

TABLE 7
Test for Mean Reversion.
OLS

Spearman

Average monthly change prior
to bureau

Coeff

P>|t|

P>|t|

% Delinquent

0.005

.74

.74

% Ever Late

0.012

.61

.32

Independent Variable:
Initial level of outcome.

34

FIGURE 1
Geographic coverage of Génesis branches:(Marked branches were part of field study undertaken summer 2003.)

19

34

5

3
4
7

35

FIGURE 2

10% delinquent/% ever late
20
30
40
50

60

Change in Delinquency over Time

2002

2003

2004

year
% delinquent% ever late36

FIGURE 30% delinquent/% ever late
20
40

60

Change in Delinquency around Rollout

-20

-15

-10

-5
0
5
months from rollout% delinquent

10

15% ever late

37

20

FIGURE 4
T-tests of Bureau Impact, % Delinquency
64% Delinquency

2% delinquent

0
1

2

3

4

5

6

7

8

9

-2

10

11

12

Lower
Upper

-4
-6
-8
Length of Window, months

38

FIGURE 5
T-tests of Bureau Impact, % Loans Ever Late

% Loans with any missed payment

4
2
0
1

2

3

4

5

6

7

8

9

10

11

12

-2
% ever late
-4

Lower
Upper

-6
-8
-10
-12
Length of Window, months

39

FIGURE 6

Percent of loans delinquent/Fitted Values
5
10
15
20
25
30

Delinquency by month bureau introduced

2002

2003

2004

year
Percent of loans delinquent

40

Fitted Values

FIGURE 7

Pre-Crediref Chg. in delinquency/Fitted Values
-6
-4
-2
0
2
4

Test for Mean Reversion

0

20

40

60

Initial delinquency
Pre-Crediref Chg. in delinquency

41

Fitted Values

1

See World Bank’s CGAP Note Book 1: “Dimensions and Dynamics of MFI Competition in

Bangladesh” for a more complete description of this project.
2

Clients were asked both about the existence of a bureau in abstract, and specifically about theirknowledge of CREDIREF, and in both cases they were not aware that a bureau was sharing
their information.

42