The Supply- and Demand-Side Impacts of
Credit Market Information

Alain de Janvry∗, Craig McIntosh∗∗, and Elisabeth Sadoulet∗
September 2009

Abstract
We utilize a unique pair of experiments to isolate the ways in which reductions in asymmetric
information alter credit market outcomes. A Guatemalan microfinance lender gradually started using
a credit bureau across its branches without letting borrowers know about it. One year later, we ran alarge randomized credit information course that described the existence and workings of the bureau
to the clients of this lender. This pairing of natural and randomized experiments allows us toseparately identify how new information enters on the supply and the demand sides of the market.
Our results indicate that the credit bureau generated large efficiency gains for the lender, and thatthese gains were augmented when borrowers understood the rules of the game. The credit bureau
rewarded good borrowers but penalized weaker ones, increasing economic differentiation.

JEL Classifications: O16, O31, G14
Keywords: Credit markets, Credit Bureau, Asymmetric Information, Randomized Experiment∗

University of California, Berkeley. alain@berkeley.edu, esadoulet@berkeley.edu
University of California, San Diego, ctmcintosh@ucsd.edu
We are indebted to Michael Carter, Dean Karlan, Atif Mian, and Chris Woodruff for helpful suggestions, and to
USAID-BASIS for financial support.
∗∗

1. INTRODUCTION
Asymmetric information problems bedevil credit transactions, and these problems are
particularly severe in poverty-focused credit markets where borrowers lack collateral andcredit histories. A broad range of social capital-based lending mechanisms have emerged inrecent years to overcome these problems, helping more than a hundred million previously
unbanked borrowers enter credit markets over the past decade. Ironically, this surge inlending is increasingly undermining the very mechanisms that made such lending possible, as
competing lenders reduce each others’ ability to contract without formal collateral. In thisenvironment, credit bureaus can become an attractive means for reducing asymmetric
information.
We use a unique confluence of data and identification methods to analyze how
lending outcomes have responded to the introduction of a credit bureau in Guatemala’s
microfinance market.

In August of 2001 a major microfinance lender began to install

hardware permitting branches to communicate information with the bureau, a process thatwas completed in ten waves over the course of 18 months. The lender did not informborrowers of the use of the bureau, however, and we found knowledge of it to be almost
non-existent in a survey of borrowers implemented after the rollout of the bureau wascomplete. We therefore conducted a randomized training campaign in which we informed
5,000 borrowers of the use of the system, how the bureau worked, and the opportunities andrisks that it presented for them. We then used institutional data from the lender and from thebureau itself to track how a variety of lending outcomes emerged from this unusual structurein which asymmetric information was reduced on the two sides of the market at two different
points in time. We are thus able to disentangle the supply- and demand-side impacts of credit
market information.
A new experimental literature has sprung up in recent years working to separate theeffects on credit markets of moral hazard and adverse selection as they operate through theprice mechanism. Karlan & Zinman (2009) use a two-stage experiment in which borrowers
are first offered randomized interest rates in order to measure adverse selection effects, andthen among those who come for loans the rate is further randomized downward by surprisein order to isolate moral hazard. This provides an experimental decomposition of the impact
of interest rates on hidden information and hidden actions, a relationship originally positedby Stiglitz & Weiss (1981). What we offer here, however, is the estimation of a more primal
1

relationship, because we are able to see how asymmetric information alters outcomes in credit
markets directly, rather than via the indirect mechanism of interest rates. That is to say, rather
than holding the overall amount of asymmetric information constant and using prices to alter
who ends up on the credit market, bureaus offer lenders a vehicle through which they can
improve selection and alter behavior based on a first-order reduction in the quantity ofasymmetric information in the marketplace.1
Before the existence of the bureau, borrowers were supposed to disclose information
about past defaults and current debts on their loan screening forms. The improved screening
ability generated by use of the bureau results in large increases in profitability for the lender,
indicating that there was strategic behavior by potential clients over past loan information andthat it was indeed creating a substantial adverse selection problem for the lender. The
specific sequence through which the bureau was rolled out for use by the lender andinformation about the bureau randomly provided to borrowers allows us to identify
separately the roles of adverse selection, moral hazard, and incentives on group composition.
This two-fold experiment – natural and randomized – allows to perform this identificationthat is, to our knowledge, new to the literature.
To formalize these tests, we first develop a simple theoretical model of the lender’scredit scoring problem, and use it to understand the effects of the new information revealedin the bureau. We capture in the model an unusual feature of microfinance credit bureaus,
which is that they report on the behavior of groups rather than individuals when loans are madeto jointly liable borrowers. Because the lender can observe and correlate demographic
characteristics and outcomes from its own clientele in a scoring model, the new information
revealed by the bureau is orthogonal to what could previously be predicted. Despite this, weshow that the probability of the lender selecting a borrower in (out) as a result of the bureau
is increasing (decreasing) in the pre-bureau score, and that the variance of the prediction error
on the client quality is an increasing function of the size of the group in which a borrower
takes loans. We then specify how borrowers respond to the bureau both through a reduction
in moral hazard and through altering the process by which they screen new members in joint
liability groups.

1 In this sense our asymmetric information problem is more similar to Navajas et al. (2003), who consider alender choosing between a costly but effective screening technology and joint liability screening.

2

We find impacts of informational changes on both sides of the market. In terms ofadverse selection, the ejection rate rises by 15% when the lender first uses the bureau, but this
impact on the size of the loan portfolio is more than compensated for by new loans made toborrowers to whom the institution had never previously lent. Borrowers selected using thecredit bureau are better clients, with better repayment performance and higher growth of
future loans. Ongoing clients who are not ejected are able to take larger loans but theirperformance exhibits a small deterioration. The selection process benefits more women thanmen. In terms of moral hazard, the repayment performance shows a modest and temporary
improvement when borrowers become aware of the bureau. Groups then also exhibit anadverse selection effect, ejecting some worse-performing members, and unexpectedly it is
now women who lose access to credit more than men. We use data from the bureau itself todemonstrate that the training induces a 10% jump in the probability that a client will take aloan from an outside lender, and we see sharp differences across borrower types in the ability
to handle this surge in total debt. Overall, the bureau permits a substantial expansion of
credit among lenders in the system while simultaneously driving down delinquency.
2. REDUCING ASYMMETRIC INFORMATION
Credit bureaus are a formalized approach to information sharing among lenders. In
the absence of such an institution, lenders of non-collateralized loans must resort to an arrayof informal mechanisms ranging from joint liability (Besley & Coate, 1995; Ghatak &Guinnane, 1999; Gine & Karlan 2006), to relationship banking, and to information sharing inthe ‘credit officer lunch’ (McIntosh et al, 2006). Within social networks, North (1990) and
Greif (1994) show that the transfer of reputation and multilateral punishment can enable a‘localized honesty equilibrium’ in spite of the individual pursuit of self-interest.

The

broadening of this equilibrium to a ‘generalized honesty equilibrium’ supporting anonymousexchange in very large groups requires the emergence of institutional innovations to formalize
the transfer of reputation and the sharing of information (Platteau, 2000). Microfinancemarkets provide a particularly interesting environment in which to study the impact of thisformalization, both because of the rapid creation of bureaus in recent years and because
microfinance provides a continuous range of contracts across which informal and formalized
tools are interchanged (Navajas et al., 2003; Morduch, 1999; Morduch and Armendariz deAghion, 2005).
3

The decision to form a bureau is one fraught with strategic risks for lenders (Padilla &Pagano, 1997). Lenders are of course happy to see the data of others, and in general do nothave problems sharing ‘negative’ information (on default), but sharing their own ‘positive’
information (on current lending) creates the possibility that their competitors will try tocherry-pick their own best clients (Gehrig & Stenbacka, 2007). Set against this peril are
expected benefits from a decrease in portfolio risk (Campion & Valenzuela, 2001; Jappelli &Pagano, 1999), restraining multiple contracting by borrowers (McIntosh et al, 2006), and thepreservation of reputation effects through the formation of long-term credit histories(Vercammen, 1995; Padilla & Pagano, 2000)2.
Concern over a rising level of default in the Guatemalan market led the country’s
three major microfinance lenders (Genesis, BanCafé, and Banrural) to agree to the formationof a bureau (called Crediref) in 2001.3 Strategic fears about the use of the bureau were
alleviated through several simple mechanisms. First, only institutions that share information
into Crediref are allowed to consult it. Secondly, the system does not allow users to identify
the lender who issued the loan, respecting lender information privacy. The bureau has
attracted smaller lenders with the passage of time, and now contains data from 14 different
institutions.
Our setting does not allow us to observe directly the quantity of asymmetric
information in the marketplace. Genesis is a well-respected lender that used the typical laborintensive practices for screening loans prior to the creation of the bureau, including detailed
application forms and visits to the enterprise and home of the applicant. The bureau includes
the majority, but not all of, the major microfinance lenders and contains no information on
informal loans, utilities payments, or commercial banking activity. Therefore substantial
information existed in the market prior to the use of the bureau, and even subsequent to its
advent asymmetric information persisted. Nonetheless, we are able to conclusively detect the
implications of improved information in this marketplace. When the lender begins using thebureau, average loan sizes increase and the overall lending portfolio increases without anincrease in default (interest rates, typical of the microfinance sector, remain fixed over the
short run). This pattern, in the absence of any real awareness of the existence of the bureau
2 Note that both of these papers discuss the possibility that sharing too much information (too long a history in

Vercammen (1995) and information over types in Padilla & Pagano (2000)) can create a disincentive to effort.
3 BanCafé and Banrural are both national full-service banks which only share microlending information in
Crediref, and not information from their commercial banking divisions.

4among clients, indicates decreased adverse selection. When we train borrowers mid-loan on
the existence of the bureau, we see a modest repayment improvement within a given clientbase on loans administered prior to the training. This is evidence of pure moral hazard in themarketplace. Finally, the pattern of turnover in groups changes after the training, indicatingthat a form of adverse selection exists in the group selection process as well, despite the factthat theory typically predicts such groups to be perfectly informed over each others’ behavior.
Our study follows Genesis which has 39 branches distributed over most of
Guatemala. This institution staggered the entry of its branches into Crediref over the period
between August 20014 and January 2003 because a team of computer experts had to be sent
branch-by-branch to set up the hardware, software, and networks required to performongoing credit checks and the monthly data exchange.

While this rollout was not

randomized, we perform a variety of tests on the sequencing and find that it was notendogenous to outcomes. Due to concerns over client reaction to this new bureau, therollout was not accompanied by any publicity campaign. Surveys we conducted in thesummer of 2003 found virtually no knowledge of the system on the part of clients.5 Having
suggested to the lender that this situation was likely to be inefficient as well as inequitable, we
worked together with the training department of Genesis to design materials for an
educational program.
The randomized training program was conducted from June to November of 2004.
For logistical reasons, we trained only group borrowers. We stressed the expanded
importance of behavior with one lender to the contracts now available from other lenders.
This point was made both in a negative fashion (meaning that repayment problems with any
participating lender will decrease options with other lenders) and in a positive fashion(emphasizing the greater opportunities now available for climbing the ‘credit ladder’ for those
who repay well).6 The training also discussed the information made available by the credit
bureau and the timing and circumstances under which checks of credit bureau records wereconducted by member institutions.
4 Between August 2001 and March 2002 the bureau was a precursor to Crediref known as Infornet, whose

information was later subsumed into Crediref.
5 Luoto et al. (2007). Note that if borrowers understood some component of the new system, then the rollout of
the bureau is accompanied by a muted moral hazard response from borrowers as well as the starker screening
effects..
6 Galindo & Miller, 2001. As a cautionary tale of the unpredictable consequences of training programs, Schreiner(1999) finds that the randomized Unemployment Insurance Self-Employment Demonstration actually discouraged
the most disadvantaged from entering self-employment.

5

The way in which borrowers respond to the bureau will depend on the contracts andinformation in use at the time the bureau is introduced. Genesis extends loans to individuals,
to Solidarity Groups (SGs) of 2-5 members, and to large Communal Banking (CB) groups.
CB loans are uncollateralized and rely entirely on joint liability and dynamic incentives toensure repayment. Thus CB borrowers are likely to be relatively credit constrained and to beunable (prior to the bureau) to readily substitute credit from one lender with credit fromanother. The bureau is only rarely used to check CB clients, with 2.5% of loans checked. SG
borrowers, on the other hand, are checked just as frequently in the bureau as individual
borrowers (63% of loans), and collateral and cosigning are commonly used. Thus SG clientsare likely to be less credit constrained and to be more able to obtain credit from outside
lenders, even in the absence of the bureau. Because individual borrowers have no joint
liability at all, we expect asymmetric information to be most severe for these borrowers, andhence the bureau should have the sharpest impacts on their selection, behavior, and access toloans.
For group loans, the bureau reports repayment and indebtedness information on thegroup as a whole, rather than on each member of the group separately. If an individual who istaking a loan in a group of 5 is checked in the bureau, the check will reveal the size of thegroup, the characteristics of the loan (loan size, term), and the repayment performance forthe whole group, but not for that specific person. Clearly, this noisy measure of individual
quality makes the bureau less informative about people who have taken loans only in large
groups. These large groups are supposedly governed by joint-liability lending rules, but often
credit officers are too well informed to punish an entire group because of a single badindividual, as the rules dictate they should. We suggest that the blunt reporting of group
information into the bureau will in fact strengthen the joint liability mechanism, and hence
the incentives for groups to be choosy about their membership, reducing adverse selection at
the group level. Good groups should try to expel bad members and good members of badgroups should be trying to leave them.
We now formalize the intuition of a bureau’s impact on behavior of the lender andborrowers with a simple theoretical model.7

7 In this paper we focus on the details of statistical discrimination when a bureau is used in a group lending

environment. For a theoretical environment which decomposes the adverse selection and moral hazard effects
of an improvement in lender information for individual borrowers, see McIntosh & Wydick (2007).

6

3. THEORY
3.1. LENDER’S SELECTION OF BORROWERS AND USE OF THE BUREAU.
In this section, we model the screening process applied by the lender and show how
the use of the bureau affects client selection. The screening process is formalized as the useof a scoring model based on observable characteristics collected by the lender and on
information available in the credit bureau, when in use.

We assume that the pool ofborrowers is unaffected by existence of a credit bureau, in accordance with the context of theempirical work that follows in which clients were unaware of the existence of the credit
bureau. Each borrower is characterized by a set of observable variables X. Assuming that thelender uses a linear regression to form the ‘credit score’, we can write the expected
profitability of a given borrower conditional on the observable information as

E (! L X )= X " .

The true, ex post profitability of lending ! L to an individual withobservable characteristics X is given by ! L = X " + a . Here a, the unobserved quality, must
be mean-zero and orthogonal to X because the score, X ! , exhausts all available information(meaning that any unobservable attributes will project into ! to the extent that they arecorrelated with X).
The credit bureau provides a signal on client quality. Lenders using group-lending
credit bureau information, however, face a fundamental problem in interpreting how muchinformation over individual quality can be inferred from this group behavior. Two primaryfactors drive this: first, the size of the group. Because an individual has an increasingly
strong effect over the group outcome as the size of the group decreases, repayment behavior
from small groups is inherently more informative than repayment from large groups. The
second factor is the extent of assortative matching which exists at the group level. In a
population which featured no assortative matching, the lender would recognize that therepayment performance of large groups was completely stochastic and therefore contains noinformation over individual quality.

If, however, borrowing groups feature assortative

matching over unobserved components of borrower quality, then group repayment
performance continues to contain some information about the expected quality of aborrower even as the group size becomes very large.

7

To capture these features, we parameterize the problem as follows: we assume thatgroups match assortatively around an intended average group quality ag ~ N (0, ! g2 ) . We
define assortative matching over the unobserved quality a because, to the extent thatmatching takes place on observables X, it has already been captured in the pre-bureau creditscore X ! . This process of assortative matching is imperfect, however, and the mismatch
between an individual’s actual quality and the intended quality of the group has twocomponents: a predictable element that would be revealed from a credit bureau informationu ~ N (0, ! u2 ) , and a completely unpredictable component of repayment behavior given by! ~ N (0, " !2 ) .
From here it remains only to observe that ! " {ag , u} because ! is by definition theresidual from the observation in the credit bureau, and that u ! ag by its definition as theresidual from the intended group quality.

Therefore we can write a = ag + u + ! , andbecause each of these three component terms is mean-zero and independent,
a ~ N (0, " a2 = " g2 + " u2 + " !2 ) . This is equivalent to saying that the average group quality

works like a random effect in individual quality. The strongest predictive information overindividual repayment therefore consists of the set {X , ag , u}.
The new information about an individual revealed by the group-lending credit bureau
information is the group average of the predictable components of quality: ! = ag + u , andthe size of the group from which the signal comes, N. Given the orthogonality of ag , u,
and ! , the group signal ! ~ N (0, " !2 = " g2 + " u2 N ).

In addition, E (a ! ) = ! , andestimating a linear scoring model for individual behavior, conditional on X and ! would leadto:

E (! L X , " )= X # + " .
The observed average group quality ! is a signal on a, and its precision given bycorr (a, ! ) ="!
is increasing in the variance of the group signal relative to the total"avariance, therefore decreasing in the number of members in the group from which the signal
comes.
8

Thus,•As N ! " , u ! 0 and so ! " ag ; meaning that only the intended average groupquality and nothing about individual deviations can be inferred from the bureau. Thisis still a meaningful signal on an individual’s quality.

•

As N ! 1 , ! " ag + u , which is the strongest signal over individual quality that canbe observed through the bureau (because ! is fundamentally unpredictable).
Based on these expressions, we now analyze which types of clients will be mostaffected by use of the credit bureau, either positively as differentially screened in with thebureau while they would not have been selected without the bureau, or negatively when thebureau reveals information that leads to their rejection.
Borrowers differentially screened in and out with use of the bureau
Borrowers differentially screened in using bureau information are those for whom

0 < # X ! $ " . Hence the proportion of the applicant pool that is offered a loan as aconsequence of the use of the credit bureau is:$ X" %Pr (X " + ! & 0 X " < 0 ) = ' (
).
#
* ! +

This expression shows the probability of being screened in to be positively correlated withthe pre-bureau score and with the variance in performance, although X and u are orthogonalin the population as a whole.8
Borrowers differentially screened out using bureau information are those for whom

! < # X " $ 0 (note that for current borrowers the vector X contains a history of inside
lending information, which it does not for those newly screened in). The probability that acurrent borrower be ejected as a result of the use of the credit bureau is:$ &X " %Pr (X " + ! < 0 X " ' 0 ) = ( )*.
+ #! ,

This is a decreasing function of the normalized score prior to the use of the credit bureau,
meaning it is the borrowers closest to being unprofitable and those with the largest variance

8 In the empirical section we will allow the variance in performance to vary with X.

9that are most likely to be ejected with the use of the credit bureau. This gives the first set ofclaims that we will take to the data:
Claim 1. When the credit bureau is consulted, and at given pre-bureau score, the decision toeject or select a client is directly related to the quality of the repayment performance
revealed by the bureau.
Claim 2. The probability that an applicant would be newly selected in (out) with use of thecredit bureau is an increasing (decreasing) function of her pre-bureau score.

The

probability of being newly selected in or out is increasing in the variance in performance
among applicants who are observationally similar.
Reduction of the prediction error.
Denote by ! the prediction error on the true profitability. From the expressions above we
can readily derive the following:var (" | X ) = # g2 + # u2 + # !2 > var (" | X , $ ( N % &) ) = # u2 + # !2
> var (" | X , $ ( N = 1) ) = # !2 .
In other words, even in very large groups a bureau is more useful than no bureau because it
allows lenders to remove the variance that comes from group-average quality. However, as
the group size decreases towards one it also allows lenders to remove the variance that comesfrom deviation from group average quality, and hence the bureau is increasingly useful asgroup size decreases.
Reduction of the inclusion error
A client will be mistakenly given a loan if the prediction error ! is so strongly negative that it
overwhelms an otherwise positive expected profitability E (! L ). We can thus calculate theprobability of mistakenly including a bad client (or mistakenly ejecting a good one) with ourdifferent benchmark levels of information:)() (Pr (X ! + a % 0 E (" )< 0 )= & (E (" ) # ),Mistakenly giving a loan to a bad client: Pr X ! + a < 0 E (" L )% 0 = & ' E (" L ) # $ ,Not giving a loan to a good client

L

L

$

where: E (! L )= X " and ! " = ! a in absence of bureau,10

N $1 2" u + " !2 with group bureau information.
N
Because the variance of the prediction error is higher when there are more sources ofandE (! L )= X " + # and " # =uncertainty, the likelihood of making a mistake without a bureau is higher for any predictedlevel of profit. Similarly, it is more likely that the lender makes a mistake with a bureau
reporting on large groups than one reporting on individuals.
Claim 3: The probability of erroneously selecting a bad client or rejecting a good client
decreases with use of the credit bureau. The quality of selection is higher for borrowers fromsmaller groups.
Without any observations on the quality of rejected applicants, we cannot verify thetheoretical results that the credit bureau would reduce the rejection of potentially goodclients, but we will verify the reduction in enrollment of bad clients.
Decision to use the bureau
In order to understand the influence of group size on the decision to use the bureau,
we must model how lenders form expectations over the group sizes that they will observe inthe bureau for a given borrower. Perhaps the simplest way to think about this is that thelender observes the group size of each borrower on inside loans (or loan application forcandidates), and expects that each borrower takes outside loans from groups of the same size.
Denoting this quantity by N̂ , the variance of the prediction error of the signal observed in
the bureau will then be " u2Nˆ # 1+ " !2 . Since the variance of the prediction error without theNˆbureau is " g2 + " u2 + " !2 , the benefit from using the bureau comes from the expected decreasein prediction error. Prior to consulting the bureau, expectation on profitability is based on
currently available information Eˆ = E (! L X ).
Imagine that checking any individual in the bureau entails a fixed cost F. If the costof making a loan to a borrower who turns out to be bad is cb , then a lender will only use thebureau if the expected decrease in inclusion error costs outweighs the fixed cost of thebureau, or if:

11

$
&
$ ( E (" L X ) %
b
&
'
(*&
c !" > F , where )* = *&& # g2 + # u2 + # !2 '
+
,
&&
+

%
( E (" X ) '','Nˆ ( 1 2
2# u + # ! ''Nˆ,Lconditional on E (! L X )" 0 . This is increasingly unlikely as the pre-bureau score increases.
As the score includes all observable behavior for current clients, this suggests that the lender ismore likely to seek the bureau’s information for poorly performing clients.
Similarly, if the costs of mistakenly rejecting a client who would have turned out to begood are the foregone profits c g , those who appear to be bad borrowers without the bureau
will only be checked if c g !" > F , conditional on E (! L X )< 0 . This is increasingly unlikely
as the pre-bureau score decreases.
Hence use of the bureau is more likely if (i) the pre-bureau score is close to breakeven; (ii) costs from mistakes are high, or the fixed cost of using the bureau is low; (iii) there
is strong assortative matching over group quality, so that ! g2 is large compared to ! u2 and" !2 ; and (iv) an individual takes loans from a small inside group. While we cannot identify
variation in the second and third dimensions of heterogeneity, we will confirm with the data
the other two results.
Claim 4: Use of the credit bureau is more valuable (and hence the lender is more likely toseek its information) for clients taking loans individually or in small groups, and for current
clients with low payment performance.
Claim 5: If the cost of carrying a bad client is high compared to that of rejecting a goodclient, one expects to see use of the credit bureau to induce large-scale screening out among
current clients, despite the direct information the lender already has on its clients.
3.2. CONTRACTS FOR ONGOING BORROWERS
We now turn to use of the bureau for on-going clients, and what it reveals on theremaining asymmetric information between the lender and its clients. Ongoing borrowers arethose for whom E (! L X )" 0 and E (! L X , " )# 0 . Our theory suggests three different
reasons why we may observe changes in the contracts offered to ongoing borrowers:
12

1. An ongoing borrower who is checked and continues to receive a loan must have beenfound to be of high quality, or to have little outside debt. The natural response is to increase
the loan size to this borrower, which in turn is likely to result in higher default for thatborrower than would have obtained if the loan size had remained at the pre-credit check level(McIntosh & Wydick, 2007).
2. A positive economic shock will likely improve repayment to all lenders. Therefore
individuals who are checked and found to be of high quality in one period could havereceived above-average shocks in the period in which they were checked. If business shocks
are i.i.d. over time, mean reversion will cause an increase in default among ongoing clients
immediately after the use of the bureau.
3. The change in contract offers may shift the demand side of the market as well; changes incollateral requirements or interest rates may alter credit demand from a single lender, or may
alter the preference ordering across lenders to which a borrower has access. This represents
a general equilibrium effect of the revelation of ! on borrowers, even though they areunaware of why the contract offer has changed.
Claim 6: Borrowers that are kept as clients after consultation of the credit bureau are more
likely to experience an increase in loan size and a deterioration in repayment performance
than those that would remain clients without consultation of the credit bureau.
3.3. IMPACTS FROM BORROWER TRAINING
With the rollout complete and no revelation of existence of the bureau to borrowers,
we have a ‘lender only’ bureau. The lender-driven adverse selection effects have by thentaken place, and borrowers have responded to the alteration in credit contracts, but they havenot incorporated the incentives created by the existence of the bureau into their decision
making. By training clients at this point, we interact only with those borrowers who are
deemed profitable with the bureau in use, and so we do not observe as large an effect as wewould have had we trained borrowers prior to implementation of the bureau (when ‘bad’
borrowers would have had a chance to improve behavior before it was observed).
Moral Hazard Effects.

13

We conducted the trainings in the middle of a group’s lending cycle, and so can lookfor discontinuous impacts of the training on groups of fixed composition. This allows us tocleanly isolate the intensive margin impacts of the training, which should arise purely as aresult of decreased moral hazard. Such moral hazard might arise from risky project selection(Ghatak & Guinnane, 1999), shirking in project implementation (Conning, 1999), or fromhidden outside debt in the presence of incomplete contracting (McIntosh & Wydick, 2005).
The fact that the bureau is reporting on the group signal ! indicates that the strengthof the incentive to reduce moral hazard effects is a function of N, the group size. Groupaverage reporting creates a classical free rider problem in which the benefits of risky behavior
are captured by the individual, whereas the costs are distributed among members of thegroup. We therefore expect that the restraint effects of the training, whereby inside default
will decrease because of strengthened repayment incentives, will be larger in Solidarity
Groups than in Communal Banks. Conversely, the new opportunities effects, whereby theexistence of the bureau expands the use of outside credit for good borrowers at the cost of
higher default, should be largest for those in Communal Banks.
Claim 7: The power of the credit bureau in curtailing moral hazard behavior is stronger forsmaller groups. The incentive for borrowers to reduce moral hazard is larger for those more
likely to seek loan opportunities outside the lender.
Group Borrower Selection
Although group members are not acquiring any new information on the othermembers, learning that of the existence of a credit bureau can still lead to changes in groupdriven selection. To understand the logic of this selection, we can formalize a group’s
decision as follows. Group profits can be written as a function of the mean characteristics ofgroup members, ! B ( X , a ) .9 Linearizing this system, we can write this expected group profit
as ! B = X " 0 + a # 0 . We assume that borrowers have access to some outside credit sourcewith profit ! * , and that prior to the training borrowers believe that their outside options areunrelated to their group performance in Genesis.

Because the training informs borrowers

9 We follow the convention in the joint liability literature in assuming that borrowers within group loans are

perfectly informed as to each others’ characteristics and actions, and hence the training alters only incentives andnot information sets for borrowers.

14

that the inside lender has already been using the bureau for a year or more, it has no
immediate effect on borrower profits from inside loans. The training’s sole effect is to makeborrowers aware that outside profits have become a function of inside group performance,
which we linearize as ! * = X " 1 + a # 1 .
To the extent that ! 0 = ! 1 and ! 0 = ! 1 , this knowledge induces no changes inselection on inside loans because the determinants of profitability are the same. Writing theelements of the vector ! as b1 ,..., b j ,..., bJ , we expect to see any characteristic for whichb j1 > b j 0 positively selected in Genesis subsequent to the training, and negative selection for

attributes with b j1 < b j 0 . Perhaps the most concrete example of this is that the bureau
reports heavily on missed intermediate payments10, whereas access to future loans in Genesis isbased almost exclusively on final repayment performance. Hence we expect to see that thetype of borrowers who miss intermediate but not final payments will be pushed out after the
trainings.
Claim 8: Group selection is expected to result in ejection of members whose repayment
behavior has relatively more weight in the credit bureau information than with the lender.
4. DATAThe main source of information for most of the analysis is the administrative datafrom Genesis, reporting all loans extended in its 39 branches from February 1999 to July
2005. This database includes some minimum information on all clients recorded at the timeof their first loan (gender, age, education, marital status, and urban/rural residence),
information on all loans (amount, term, periodicity of repayments, and the balance if any in
July 2005), and on all transactions on each loan account (payments on principal, on interest,and on fines, and balance). For group loans, information is given on the identity of themembers and the individual amounts they received, but repayment information is solely
reported at the group level. From the detailed accounts on payments, we constructed two
indicators of loan performance: the total fines paid as percentage of the principal, and10 Crediref reports include a line for each loan that categorizes each payment into ‘on time’ and a variety ofdelinquency categories. While there was some confusion among credit officers as to how to read this string, wefound they had no difficulty in differentiating a loan that had had intermediate problems from one that had not.

15

whether the final payment was settled more than 2 months after the original due date.
Information is also available on the dates at which the credit bureau was consulted for
information on each client or applicant, although no outcome is recorded.
Table 1 presents basic summary statistics that give a sense of the differences between
borrowers and contracts across the three loans types offered by Genesis during the six
months prior to use of the credit bureau. Communal banks are very different from individual
clients and solidarity groups. They are larger groups (on average 7.7 members taking loans vs.
4.5 in SGs) composed mainly of women (99.7% vs. 46.7% in SGs), with low education(41.4% uneducated vs. 26.5% in SGs), and with much smaller loans (US$324 per client vs.
$662 in SGs).
We also had access to the information recorded in the credit bureau on all Genesis
clients, as of October 2005. This includes 263,116 loans.

For each loan, the reported

information includes whether the loan is an individual loan or a group loan, its total amount(but not the number of members if group loan, which prevents us from inferring a potential
level of indebtedness), and a series of codes referring to up to 20 periods indicating whether
the repayment status was in good standing, in arrear, or if the payment was the last payment.
Crediref collects information from its members on a monthly basis, and only the date of thelast entry of information into Crediref is recorded. The earlier date is June 2002, while 40%
were still active loans and thus have information recorded in October 2005. From thesemonthly repayment status, we created an indicator variable for whether the loan had everbeen in arrears.
Matching clients from Genesis and Crediref databases was done from a common
identification code. Recognizing the Genesis loans in the Crediref database was done fromthe loan amount and from an approximate correspondence between the last dates recorded
for payment in Genesis and payment status in Crediref.
Use of the bureau to screen clients is not universal. The fixed cost of a check in
Crediref starts at $1.60 (decreasing to $0.67 per check when over 6,400 checks per month aremade), thus making Genesis less likely to investigate small borrowers. Almost no CB clients
have their records checked.

This is consistent with our theory indicating that the

informational gains from the bureau are decreasing in the expected group size, and hence weignore the CB borrowers throughout the analysis of the impact of lender use of the bureau.

165. EMPIRICS: THE LENDER BEGINS USING THE BUREAU
Table 2 provides some simple tests of exogeneity of the rollout of the bureau. Let
flbt be an outcome, such as loan delinquency, late fees, and group size, for loan l issued bybranch b in month t. We first calculate borrower-level changes in outcomes from the previous
loan, !flbt , for all individual borrowers and Solidarity Groups. We then calculate
branch/month averages of these changes in outcomes, separately for individual borrowers
and Solidarity Groups, for 1, 2, 3, and 4-6 months prior to entry in the credit bureau in July
2001, !f bt , and regress them on a set of month dummies ! t and a variable Tb which gives
the numerical month in which the bureau was introduced in branch b:(1)#f bt = ! t + " Tb + ubt .

This tests whether outcomes were changing at a different rate in branches thatreceived the bureau earlier relative to those that received it later, which is the identifying
assumption of an impact regression using branch- or borrower-level fixed effects. Results
reported in Panel A of Table 2 show no indication of any differences in rates of change inloan performance (loan delinquency and late fees) or number of members in solidarity groups
with the timing of branch entry in the credit bureau. These regressions were also tried
without month fixed effects and the results were similar.
We now look more closely at the repayment behavior over the short period prior toentry in the credit bureau. Figure 1 shows the observed branch level performance before andafter its entry in the credit bureau. The pattern for individual loans is quite clear with a net
discontinuity concomitant with use of the credit bureau. There is no particular pattern in the
few months prior to entry, which would create concern with a potential endogenous
sequence in the rollout, either in response to repayment problems (an Ashenfelter dip), or
following an on going improvement in performance. For solidarity groups, the picture is lesspowerful. The pattern is dominated by an overall increase in default among solidarity groups
that occurred between May 2000 and January 2002, but there is no evidence of a short term
pre-entry burst in default, nor of a break in the trend just prior to entry in the credit bureau.
These results are confirmed by regression of the average branch outcome fbt on a set ofmonth dummies ! t and variables m! n that denote the month n before entry in the credit
bureau, estimated over the period prior to entry in the credit bureau:
17

(2)

f bt = ! t + "1m#1,bt + " 2 m#2,bt + "3 m#3,bt + " 4 m#4,bt + ubt .

Results reported in panel B of Table 2 show no specific patterns, positive or negative, in themonths preceding entry of a branch in the credit bureau.
We thus proceed to utilize the staggered entry of Genesis branches into the bureau toestimate causal impacts on credit outcomes for individual borrowers and Solidarity Group
members. Because we observe whether each loan is issued to a new or to an ongoingborrower, we can disentangle the screening effects of the bureau on the extensive margin
from changes in contracts on the intensive margin. We can also track the differences overtime between borrowers who entered Genesis before and after the bureau was being used,and so measure the longer-term effects of improved information.
5.1. SELECTION OF CLIENTS USING THE BUREAU
We run a regression at the loan level to explain the difference in differences that
results from the staggered treatment dummy Rlbt on the loan performance flbt . Rlbt is one
for loans to clients that could have been checked by the bureau and zero for loans given
before the bureau was introduced in each branch, and hence measures the Intention to Treat
Effect of a branch having the bureau, not the effect of using it.11 Our basic regression
specification is then:(3)

flbt = ! t + ! b + " Rlbt + ulbt ,where ! t and ! b are month- and branch-specific fixed effects, and ulbt is an error term
clustered at the branch level.
Table 3 demonstrates the substantial increase in the fraction of clients selected in andout under use of the bureau. The sample includes all loans made by Genesis between
February 1999 and May 2004. In the top panel, we look at clients leaving. The dependent
variable f is equal to 1 for loans whose taker does not take a loan from Genesis afterwards.
The subscript t in equation (3) refers to the month in which the last payment is made. The
treatment of being screened is a dummy variable equal to 1 for loans whose last repayment
was made after the credit bureau became available in a branch or, to account for typical lapse

11 We measure the ITE

because the decision to check an individual is non-random, and so estimating theTreatment-on-the-Treated would require that we try to predict who in the branches without the bureau would
have been checked. Any unobservable in this equation would create bias.

18

between two loans, at most 2 months before that date. In expectation, the first use of thebureau for a given individual should reveal more new information than subsequent checks on
the same individual.

We therefore distinguish two treatment variables. The “first-timescreened” treatment applies to the first time a client would have been screened with thebureau, i.e., to the last loan granted before the availability of the bureau and with last payment
made no earlier than 2 months before the availability of the credit bureau, while the“subsequent screenings” applies to all subsequent treated loans.12 In the lower panel, thevariable of interest f is whether the loan is granted to an entering client. Time t refers to themonth in which the loan is granted, and the treatment variable is a dummy variable equal to 1
if the credit bureau is available to the branch in this month. We see that the bureau causes a
symmetric change in the percentage of individual borrowers who leave and who enter(column 1); both figures increase by roughly 14 percentage points (beyond base rates inwhich 42% of the clients are new and 51% leave across cycles prior to the bureau). In otherwords, there is a period of upheaval in the client base triggered by the use of the bureau. Thisdrastic change in the rate of ejection of clients suggests the value of the credit bureau evenfor clients on whom the institution has rich information from a history of lending, as
mentioned in claim 5. Figure 2 shows the large increase in new individual clients that occurs
for roughly six months after the bureau is implemented. For solidarity groups, the picture issomewhat more nuanced; individuals within these groups are much more likely to be
expelled, by 19 percentage points (column 2), but the groups themselves become more
durable as a result of the bureau (column 3). The net effect of decreased enrolment inexisting groups, expulsions, and the creation of new smaller groups is the dramatic decreasein average Solidarity Group size, illustrated in Figure 3, falling from 4.3 to 2.7 members. In
other words, the bureau causes the lender to rely less on joint liability as a screening tool.
Another illuminating comparison is looking at the Crediref records of ongoingborrowers who were checked and ejected from Genesis versus those who were checked andnot ejected. Table 4 draws this comparison. Ongoing borrowers are defined as those thathave had a loan with Genesis until at most 2 months prior to the introduction of Crediref intheir branch. Clients are sorted according to whether their Crediref record was checked or
not in the two months that follow availability of the credit bureau. Ejection is here again
12 This includes borrowers that were clients before the credit bureau that are checked later and clients selected

after the credit bureau.

19defined as the client not taking subsequent loans with Genesis. 13 As can be seen from thenumber of observations in each group, 59% of individuals and 63% of SG members arechecked, and of these 12% and 9% are ejected, respectively. While the decision to screen is
endogenous, and so no causal interpretation can be drawn from this comparison, it is clearthat Genesis is more likely to check the records of individual clients with the worst
performance. Among these clients, those who leave are about twice as likely (36.7% vs.
20.8%) to have defaulted on external loans than those who remain clients. Although thislooks like a substantial difference, it is small compared to the ten-fold difference seen
between first time candidates who are rejected and those who are taken as clients.14 Because
past performance is part of the information set for prior clients, we expect the influence of
the information in Crediref to be more muted for ongoing than for prospective borrowers.
For Solidarity Group members, although the information in Crediref signals those who will
be ejected, the internal information also conveys differences. The ejected clients come fromgroups with worse performance (33.7% of loans with arrears) than the non-ejected (16.8%).
Note that for all clients, the performance recorded in Crediref can be from both individualand group loans. The role of Crediref is thus particularly clear in helping discriminate amongindividual clients with mediocre internal performance. Together these results show an
extensive use of the credit bureau, even for clients on whom the lender has direct
information (claim 5), particularly for clients with repayment problems (claim 4).

The

information revealed by the credit bureau informed the lender’s decision on whether to ejecta client or not (claim 1).
Table 5 examines how the use of the bureau altered the characteristics of clientsselected in and out of Genesis, and so gives us a direct test of the theory outlined in Section
3.1 and claim 2. Our theoretical model indicates that what is learned through the bureau
should be orthogonal to the pre-bureau score, but that nevertheless selection in (out) will pickborrowers who are better (worse) on observables. It also indicates that the likelihood of
selection in and out will be increasing in the variance of repayment performance conditional
13 It thus also includes the clients that decide on their own not to renew their demand for a loan. Note also thatsome of the non-checked clients may have left on their own or be ejected by Genesis without checking theirrecords. These confounding factors however all play in favor of blurring the contrast that we are making in thetable between the internal performance of the checked and unchecked borrowers, as well as the external
performance of the ejected and not ejected.
14 Genesis records give the identity of all applicants and a date at which their Crediref record was checked.
However no further information is available on the applicants that are not accepted. We have thus no otherobservables to compare accepted and rejected applicants apart from their Crediref records.

20

on the score. In order to investigate these hypotheses, the first column of Table 5 gives
coefficients from a pre-bureau scoring model, and the second column reports how thevariance of outcomes relates to these same characteristics. We only use individual borrowers
for this analysis because we can relate their own performance to their characteristics without
confounding the influence of other group members. These are OLS estimations of
performance indicators on client attributes and time and branch fixed effects for the 13,550
loans granted before Crediref, from February 1999 to July 2001. The low performanceindicator is the ratio of late fines to principal paid over the whole loan cycle. The varianceindicator is the square of the residual in the performance indicator estimation. This is meant
to mimic the scoring model that Genesis could be using, although it is only based on thesubset of characteristics that are recorded in the Genesis information system to which wehave access, rather than the complete information that credit agents have from their personal
contacts with clients.

Results show that lower performance and larger variance inperformance are associated with male, some secondary education (for low performance), andlower age. None of the other covariates (marital status, ethnicity, some primary school
education as opposed to none, and rural/urban location) were significant in this simple
scoring model.15
The third and fourth columns of Table 5 report on separate regressions that measurechanges in the composition of entering and departing borrowers,(4)$
X ibt = ! t + ! b + " Ribt + # X ibt+ uibt .

X ibt , representing either gender, education, or age of the new entrant or the departing client,is regressed on the Ribt rollout dummy, controlling for the other two characteristics X ! . So
the coefficients ! should be interpreted as the effect of the bureau on the averagecharacteristic of new and departing clients, conditional on the other two characteristics.
Results show that the bureau pushes individual lending to be less male (more dropouts) andless educated (both less new clients and more dropouts), but has no effect on the age
composition. These regressions were performed on all new clients until May 2004, but only
on departing clients that have been selected before the credit bureau, to avoid confounding
15 With about half of the loans with no fines at all, the performance variation is dominated by the binary elementof whether there is any fine. And the variance is thus directly related to the predicted mean, which explains thatthe two regressions of low performance and variance are similar. This prevents us from separating these two
effects in the selection process.

21

potential selection effects.

Very similar results however are found when including all

departing clients, and without conditioning on the other two characteristics. This confirms
the theoretical result that the bureau causes large selection effects in client categories thathave low pre-bureau average and large variance in performance (claim 2).
5.2. EXTENSIVE MARGIN IMPACTS OF THE BUREAU
We now analyze the impact of the use of the bureau on the quality of the selectedclients. A good client pays back, but also takes repeated and increasing loans. In fact a major
problem of this and many similar microfinance institutions is the large turnover of clients that
only take one or possibly two loans. Since the selection of a new client entails large fixed
costs, stabilizing the clientele would bring large efficiency gains. By tracking the differences
over time between borrowers who entered Genesis before and after the bureau was being
used, we measure the longer-term effects of improved information.
In the first three columns of Table 6 we use loan-level data and the specification in(3) to measure the impact of the bureau on client repayment performance on their first loan.
The sample includes the first loan of all new clients selected between February 1999 and May
2004. The time index refers to the month the loan was granted, and the treatment variable towhether this selection occurred after the availability of Crediref. For loans given to individual
clients, where we would expect the effects of new information to be strongest, we see a 24%
drop in the share of loans that were charged important late fees. Loans more than 2 months
delinquent, which would be technically under default, are not changed. For group borrowers,
on the other hand, the primary effect is an increase of 42% in mean loan size. So thedecrease in the lenders’ adverse selection causes loan sizes to go up, while repayment on these
new loans does not deteriorate. Given that lenders’ profits are made on margin, loan size
increases of this magnitude with no corresponding increases in repayment problems translate
into substantial earnings for the lender.
The last three columns compare performance on subsequent loans for those selected
before vs. after the bureau, with an estimation similar to (3) at the individual client or SG
level:(5)

fibt = ! t + ! b + " Ribt + uibt .

The time variable refers to the month the client or SG was selected, and the treatment
variable to the availability of the bureau at that selection time. In column (4), the dependent22

variable is whether these clients or SGs have taken a second loan with Genesis or not. In
columns (5) and (6) we characterize these second loans by the time elapsed since the end of
the first loan, and the growth in loan size. The bureau causes significant improvements in theability to pick good stable clients, but only for people who take individual loans, a result insupport of claim 3. Individual borrowers selected with the bureau are 53% more likely thanthose selected before the bureau to go on to take subsequent loans. These subsequent loans
are taken somewhat sooner, and the size of these loans is 12% larger. Group borrowers, on
the other hand, display no impacts on subsequent performance. This is consistent with thejoint liability mechanism providing a richer information set when group borrowers arescreened without a credit bureau.
5.3. IMPACTS OF THE BUREAU ON ONGOING BORROWERS
Having seen the supply-side changes that occur on the extensive margin, Table 7
carries out the analysis on the intensive margin by including only borrowers who took loans
both before and after the bureau was being used in their respective branch. Here we can
include borrower-level fixed effects, and so the treatment effect measures changes incontracts for ongoing clients.

E (! L X )> 0andSince we have limited the sample to those for whom

E (! L X , " )> 0 , we follow a consistent cohort through theimplementation of the bureau. We estimate:(6)

filt = ! t + ! i + " Rilt + uilt ,for everyone whose first loan was before the bureau and last loan was after the bureau,continuing to cluster standard errors at the branch level.

The dependent variable is acharacteristic of the loan and the time index refers to the month in which the loan was
granted. The treatment variable indicates whether the loan was granted before or after theavailability of the bureau. Note that this estimation measures an intention to treat effect since
not all on-going clients were checked in the bureau, as seen in Table 4. This test provides ameasure of the intensive margin effects, but it may be biased by mean reversion in themanner suggested in Section 3.1.
Loan sizes for ongoing individual clients and SGs increase by 7.8% and 23.9%,
respectively, when the bureau is in place. However, for individual loans, this is accompanied
by a sharp worsening of repayment (claim 6). Such a worsening is not surprising if loan sizes
23

have increased, but evidence that mean-reversion is involved is provided by the extremely
low mean default rate among these ongoing clients: 2% versus an institutional average of over
4%. Thus the ‘treatment effect’ measured in the second column of Table 7 essentially shifts
performance among borrowers retained when the bureau comes in back to the unconditionalmean in the institution. Given the potential positive bias in the estimated parameter, wecannot conclude on the causal effect of the credit bureau on on-going borrowers, except forsaying that if there were any effect in reducing bad repayment performance it does not
dominate the selection bias effect.
5.4. AGGREGATE IMPACT OF THE BUREAU AT THE CREDIT OFFICER LEVEL
We conclude this section on the use of the credit bureau by the lender with somestatistics on the aggregate effect of using the bureau on the institution’s portfolio. The
introduction of the credit bureau in branches was accompanied by installation of new
hardware and software, and a substantial overhaul of the institution’s information system.
While this has improved the efficiency of the lender’s administration, credit officers did not
directly deal with any of these tasks. They continued to operate in the field using regularpaper forms to collect information that was digitalized afterwards by administrative
personnel. The only significant effect of the installation of the new computer system on thecredit officer performance therefore came from availability of the credit bureau information.
Credit officers received some training in reading the information provided by the bureau.
While this may have raised awareness of the importance of repayment performance, there
was no change in guidance for the traditional selection procedure that followed consultationof the credit bureau, nor in the incentives given credit officers to maintain good repayment intheir portfolio.

It is therefore unlikely that the measured changes in performance ofborrowers and efficiency of loan officers came from factors other than introduction of thecredit bureau.
We perform the analysis at the credit officer level, which allows us to control for
effects due to changes in the number and composition of credit officers. An efficiency effectof the bureau is the ability for a given employee to increase the number of new borrowers
whose applications he processes in a given period of time. Results reported in the first twocolumns of Table 8 use credit officer fixed effects in a regression on data at the credit
officer/month level:
24

(7)

f obt = ! t + ! o + " Rbt + uobt ,where the dependent variable is a characteristic of the portfolio of credit officer o of branch b
in month t, and the treatment variable is whether the credit bureau was available to the credit
officer in that month. Results show an increase by 64% in the number of new individual
clients and 149% in the number of members of new SGs, suggesting large efficiency gains inthe selection of clients. Total number of loans per month increases by 27%, and aggregate
performance also improves, with a large 33% (but statistically not significant) reduction in theshare of loans with large fines. This translates into a large 52% increase in branch level loans
and amount lent without deterioration of the portfolio at the branch level (columns 3 and 4).
A back of the envelope calculation of costs and benefits further indicates the financial
return to the lender of using the bureau. Investment costs are $9,250 per branch in hardware,
telephone, and furniture, plus $1,280 to the institution in software. This sums up to $9,280
per branch. These assets were expected to need replacement or updating on average every 3
years, which we take as benchmark. Variable costs are estimated at $72 per branch permonth. The main benefit stems for the increase in lending by $24,000 per month per branch,caused by use of the credit bureau by the institution (Table 8). The majority of loans are for
6 or 12 months, averaging 10 months. The cost of capital is 17% and the lending rate is 25%.
Since this additional lending was undertaken with no increase in the number of credit
officers, the benefit associated with it is likely a very large share of the 6.6% margin made on
10 months loans. Assuming 3% benefits would lead to a monthly benefit of $725. Net
benefit would thus be $653 per month, paying back the fixed investment in 14 months, muchbefore the anticipated 3 years before replacement.
6. EMPIRICS: BORROWERS LEARN THAT THE LENDER IS USING THE BUREAU
We now turn to the impacts of the randomized credit bureau training course that was
implemented 18 months after the staggered rollout was complete. The sample used in thisanalysis consists of seven branches randomly selected from the 39 Genesis branches torepresent the variety of Genesis clients. Within each of these seven branches, we randomly
selected a predetermined number of groups for treatment, the others forming the control

25

groups. 16 Observations are thus weighted in the analysis, in correspondence to the sampling
scheme.
Once selected, groups were notified that they were eligible to receive a free
information session, and they were requested by their credit officer to appear at a specifictime and place in order to receive the information. Attendance was entirely voluntary, and if
a group did not show up the first time, two subsequent efforts were made to call it for thesession. The percentage of chosen units that were in fact treated varies from 31% to 100%across branches, with an average response rate of 62%. The information sessions took placeover a period of four months, from July to November 2004, with the order in which groups
were called randomly defined. The timing of the treatment is thus specific to each treatedgroup and we assign the median of the treatment dates within each branch to the control
groups. The analysis that follows is based on loans granted from February 2003 on, when all
7 branches had access to the credit bureau, and with due date by June 2005. This includes
2582 loans taken by a total of 1040 groups, 573 selected for training and 467 control groups.
The quality of the randomization can be gauged from Table 9, panel A. Comparing
the mean values of group-average characteristics such as age, marital status, education,
gender, and ethnicity for the loans extended to the 994 groups that were active during the 17months preceding the randomization, from February 2003 to June 2004, we find no evidenceof significant differences between the selected and control groups. On loan performance, thesituation is less ideal. The selected groups perform somewhat better than the control groups,
even though, because of the large variation within each category, the difference is statisticallynot significant.
Because we use branch and month fixed effects in all the regressions, the relevant
question in terms of bias to our estimators is whether a significant difference remains whencontrolling for these fixed effects. By constructing false treatment effects within the pretreatment time period, we verified in Table 9, panel B, that none of the false treatment effects
are significant, suggesting that there are no serious biases when we use a double difference.
16 This selection

was done by randomly selecting one branch in each of seven groups of similar branches
constituted by credit officers with intimate knowledge of the institution. However, despite the randomization,
the average characteristics of the groups from these selected branches do not perfectly match those of the nonselected branches. We therefore limit the analysis to the groups from the selected branches. Within each branch
the percentage of groups that was selected for training was also randomly assigned values from 25% to 87%
with the objective of looking at spillover effects. We however found no such differential effect and thus ignore
this issue in this paper.

26

Because of the relatively high non-response rate, our analysis focuses on the Intention toTreat Effect. Assuming that the non-experimental implementation of the training program
would have a similar non-response rate, the ITE is in fact the quantity of interest for thelender.
The interpretation of the ITE is complicated by the fact that some borrowers mustcertainly have understood at least some of the implications of the bureau before the training.
As a general matter, knowledge of the workings or indeed the existence of Crediref was very
low among clients; not one of 184 clients surveyed in the summer of 2003 was aware that
information was being shared between microfinance lenders.

That said, certainly someclients would have possessed better information, or at least more realistic expectations, overthe process of information sharing. Such clients will appear to have a lower impact (andhence a smaller moral hazard response) simply because they learned less from the sessions. A
causal impact of the treatment, then, is composite of the amount that was actually learned
and how what was learned effects behavior.
6.1. THE INTENSIVE MARGIN: IMPACT WITHIN A LOAN CYCLE
Since group composition is fixed within a loan cycle, the instantaneous impact of theinformation program on inside repayment isolates the moral hazard effect that arises fromthe desire to use reputation to leverage credit from other sources. Hence in the short run our
experiment raises the value that clients place on good performance to access outside credit.
Over time, the repercussions of changes in group membership undertaken due to learning
about the bureau begin to have their own effects upon inside repayment, adding adverse
selection to moral hazard effects.
The trainings occurred mid-loan for 735 of the groups, and so we have the ability to
see whether a given group of people change their behavior once the existence of the bureau isrevealed. This analysis is conducted at the loan payment level, separately for Solidarity Groups
and Communal Banks. The observations are the different intermediate payments made on
the loans that were active at the time of the treatment. Because repayment problems tend tocome only after a certain time is elapsed, we control for where in the loan cycle therepayment takes place. A complication occurs in that loans are of different lengths andrequire various numbers of intermediate repayments.

To make these repayments

comparable, we therefore divide the length of each loan cycle in 10 equal time intervals,
27

which we refer to as deciles, and we control for the deciles rather than the rank of therepayment. We thus estimate:(8)df plt = ! l + ! t + # ! d D plt+ " Tplt + u plt ,dwhere f plt is an indicator of performance for payment p made at time t on loan l that wasdactive at time of treatment. The decile dummy variable D plt
is equal to 1 if the payment

belongs to decile d. The treatment variable, defined at the payment level, Tplt is set equal to 1
if the payment p is in loan l taken by a group g that was selected for treatment and t > ! g , thetreatment date for group g. Results are reported in the first column of Table 10.
Performance on the final payment and overall loan performance can only be analyzed
by simple difference between treatment and control groups within a branch. This is donewith a loan level estimation:(9)

flb = ! b + " Tlb + ulb ,where the treatment variable indicates if the group was selected for treatment or not. Results
are reported in columns 2 and 3 of Table 10.
We see in column 1 of Table 10 that there was no significant change in performanceon intermediate payments as a result of the training. The fraction of loans which were late asof their final payment, however, decreases by 10 percentage point (over a base value of 20
percent) with the training, but only for SGs (column 2). Changes in the fraction that
ultimately pays large fines or goes into default are not statistically significant, but show large
point estimates of -7 percentage points (over averages of 14 and 19%, respectively) for SGs
and -3 percentage points (over averages of 3.5 and 4.5%) for CBs, suggesting important
improvement in payments after the information session, particularly for the first (column 3).
This is consistent with claim 7 that the power of the credit bureau in curtailing moral hazard
is higher for smaller groups.
6.2. IMPACT ACROSS LOAN CYCLES
We have data on repayment behavior from Genesis for one year after theintervention. Over this intermediate time frame, we expect the moral hazard impacts todominate although, in groups that take one or more loans after having received theinformation, repayment behavior is also plausibly being affected by the selection response of28

group members. These impacts are measured by estimating the repayment performance atthe loan level over the long period February 2003-June 2005 (excluding loans that were active
at time of treatment). We use group fixed effects estimators:(10)

flgt = ! t + ! g + " Tlgt + ulgt ,where flgt is a measure of repayment performance of loan l of group g with last payment attime t. The treatment variable Tlgt equals 1 if the group g was selected for treatment andt > ! g , the treatment date for group g.

Results are reported in column 4 of Table 10. With only a few months ofobservations after the information courses, the evaluation of the repayment performance isbased on very few post treatment date loans (181 SG loans and 23 CB loans), and none of
the CB loans, from either treated or non-treated groups, bore a large fine or were in default.
We however observe a significant decline of 6 percentage points in delinquency for SG loans
for the treated groups relative to the non-treated groups.
6.3. THE EXTENSIVE MARGIN: IMPACT ON GROUP COMPOSITION
We argued in Section 3.2 that changes in group selection induced by the training willarise as a result of differences between ! 0 , the determinants of profitability on inside loans,
and ! 1 , the determinants on outside loans (claim 8).
We therefore begin our analysis of selection after the training by asking what kinds ofborrowers were repaying well before they were aware of the use of the bureau. We estimate
the correlations between group repayment performance and the average characteristics ofgroups in the seven branches that were in the training experiment during the period February
2003 to the treatment date,(11)

flgt = ! t + X lg " 0 + ulgt ,where X lg is a vector of group-average attributes for each loan. We use only SG borrowers
because the group-level characteristics are not consistently entered for CB clients. The
purpose of this regression is for us (the econometrician) to observe these correlations assuggestive information on which types of members are likely to be worse performers, because
we have no information on individual behavior. In contrast, group members observe eachothers’ individual behaviors and thus act on the basis of perfect information.
29

Perhaps the most interesting correlate of repayment performance seen in the first twocolumns of Table 11 concerns the share of female borrowers in the group: women are more
likely to have small repayment problems which result in fines (with a t-statistic of 1.6), butthey are less likely to have loans more than 2 months delinquent (or substantial late fees
above 1% of the principal, not reported). This may be related to the fact that the large
majority of these women are primary care-givers in the household, and so as business people
they must multitask in a manner that causes more frequent small lapses. We also see thatgroups with more educated members are worse payers.
With this basic sense of the correlates of repayment performance, we move to an
analysis of the training’s impact on group composition. We use the average characteristics of
those entering and those leaving existing solidarity groups in the aftermath of the training.
New clients are defined as members that join a group after the first loan of the group, anddropouts as those that quit the group before the last group loan. Members that do notparticipate in a particular loan cycle but return to the group for a subsequent loan are counted
as continuing borrowers. As there are not enough observations on groups with entry anddeparture of members to use a group fixed effect estimator, we use branch fixed effects:
X lgbt = ! t + ! b + " Tlgt + ulgbt .

(12)

Observations are weighted by the number of people represented by each group average.
Results are reported in the last two columns of Table 11. As we might expect in anenvironment where borrowers were fully informed about each other’s quality prior to thetraining, there are no sharp compositional impacts of the training. And with a small numberof observations, standard errors are large. But again the most intriguing result is on gender:
SGs swing toward a more male composition after the training, with women more likely todropout (17 percentage points increase in women share among dropouts) and less likely (butnot significantly) to be accepted. Given that we expect the primary reason for differential
selection after the training to be characteristics which are more important to outside lenders
than inside lenders, it appears that the greater propensity of women to miss intermediatepayments, combined with the detailed reporting of arrears on intermediate payments in
Crediref, has decreased female access to SG credit. The difference between the coefficientson leaving and entering clients for women is 0.42, suggesting that the turnover of a member
is 42 percentage points less likely to be a woman than it would have been without thetraining.
30

6.4. BEHAVIOR WITH OTHER CREDIREF LENDERS
A key assumption for a credit bureau to create incentives on client behavior is that itallows good borrowers to use the reputation they acquired with a particular lender for gaining
access to outside borrowing opportunities. We therefore conclude this section on the effectof informing clients on the existence of the credit bureau with some support that thismechanism is at play.
Because we have access to data from the bureau itself for Genesis clients, we can test
directly for the ways in which improved understanding of the credit system altered their
behavior with other lenders. The entry of Genesis client data into the bureau took place alongwith the staggered rollout of use of the bureau, and hence as of the time of the training all‘inside’ information had been observable to other lenders for a year or more. However, tothe extent that clients did not know that the bureau existed, they possessed a form of
reputational capital of which they were unaware. Treatment effects of the training thus arise
from differences in the process by which borrowers seek outside loans, not from a systematic
shift in the supply of credit.
We characterize a client’s outside borrowing by the number of loans taken from other
lenders (the quantity is difficult to interpret for group loans) and by the repayment
performance of each loan as to whether there has been any late payment. The analysis uses
only Genesis clients who were members of a group at time of treatment, and their treatment
status is that of the group to which they belonged.17
The date recorded for each loan in Crediref is that of the last data entry, whichcorresponds to the closing date of the loan (except for the current loans which have their lasttransaction recorded in June 2005). In this analysis we consider as pre-treatment all loanscompleted before the treatment date. Post-treatment loans are those for which payments
were recorded after the training date. We estimate the following equation at the client level:(13)# igb = ! b + " S g + uigb ,17 When clients belonged to two groups, they were considered treated if at least one of their groups was treated.

About 3% of the control SG clients (20% of the control CB) changed group, joining a treated group after thetreatment date. We also perform the analysis by attributing them the status of treated starting from the datethey joined the treated group. Results are very similar and not reported here.

31where ! igb characterizes the change in outside loans reported in Crediref from the pretreatment to the post-treatment period of individual i from group g in branch b, ! b represents
the branch level change in outside borrowing for the members of the control groups, and S g
is a dummy variable indicating that the group g was selected for treatment. The parameter !
measures the ITE effect of the information sessions. Errors are clustered at the group level.
We use as indicator of outside borrowing the change in the number of loans and whether
individual i started taking outside loans after the treatment date. Some of the loans classified
as treated may have started before the information sessions; we have no way to know.
However there is no reason for the number of such loans to differ across treatment andcontrol groups, and hence it is absorbed in the branch fixed effects.
The repayment performance on outside loans is analyzed with the following branch
fixed effects regression at the loan level:(14)

flibt = ! b + ! t + " Tlit + ulit ,where flibt is a measure of performance for loan l taken by individual i from branch b lastrecorded in Crediref at time t. The treatment variable Tlit is equal to 1 if individual i wasmember of a group selected at time of treatment and t " ! i , the treatment date.
Results are reported in Table 12. They show treated SG starting to take outside loans
27% more frequently than mean value in the control group, and CB members increasing thenumber of outside loans and starting to take outside loans by 77% and 33%, respectively.
This impact represents a substantial increase in credit received. Note that the change inreported outside borrowing includes a direct reporting effect associated to the expanding
coverage of the bureau, which probably accounts for most of the observed change in thecontrol group. And there is weak evidence of a positive effect on performance for SG
borrowers with less ever missing a payment.
Who among these members responded to the training by taking on outside loans andhow did they fare? Table 12 reports the contrast in ITE for good and bad Genesis clients aswell as for more and less experienced clients. Good clients are defined as never having had alate repayment with Genesis (meaning they are members of good groups for their grouploans), while those named bad clients have had at least one late repayment.

Moreexperienced clients are those who had 4 or more loans with Genesis, of any type, individual32

or group, before the information session, and less experienced clients 3 or less loans with
Genesis. Results show that good CB clients respond to information about their public
reputation by starting to take outside loans or increasing the number of loans taken outsidewhile bad clients do not. Less experienced CB clients respond by starting to take outsideloans more than their experienced counterparts. However, they show a sharp and significant
deterioration in their outside payments. This negative effect on performance does not
happen with SG borrowers, who to the contrary tend to improve their outside performance.
One possible explanation for this response by CB relates to the extensive use of
dynamic incentives in microfinance. Loan sizes begin very small and grow slowly, holding
clients credit-constrained and thus putting a high value on the future relationship with thelender. While we expect the bureau to reinforce dynamic incentives with the credit system as
a whole, the rise in default among CB clients indicates that the bureau may have a perverse
effect by expanding credit access to new borrowers too fast.
7. CONCLUSION
We utilized an unusual pair of experiments which allowed us to decompose theimpacts of a new credit bureau into two parts: what happens when a lender observes new
information about borrowers, and what happens when borrowers become aware that lenders
can observe this new information. We find that the new information in the hands of thelender has stark impacts. There is a large increase in the turnover of the client base,particularly in the 6 months after introduction of the bureau to a branch. Large numbers ofindividual and SG clients are ejected, and the categories of clients ejected are indeed thosewith lower average repayment performance (males, more educated borrowers) and larger
variance in performance (males). There is a dramatic improvement in the repayment
performance of new individual clients and in the size of the loans made to new SG
borrowers. The new individual borrowers are better future clients in that they are more likely
to take subsequent loans and have faster growth in their borrowing. For solidarity groupborrowers, the bureau induces a strong swing toward smaller groups and allows the lender toincrease loan sizes without causing repayment problems. At the level of the lender, there is ahuge increase in employee efficiency, with the average credit officer selecting 63 percent more
new individual borrowers over an average of 6 per month, increasing the number of loans by
27%, and this without any deterioration in portfolio performance. The reduction in adverse33

selection from improved credit market information therefore presents strong benefits for
lenders, generating more clients, larger loan portfolios, and improved repayment.
When group borrowers learn of the bureau and what it implies for them, we also see
significant impacts on borrowing.

Informed solidarity groups show an immediate

improvement in repayment performance, with a sharp decline in delinquent final payments,
solely due to reduction in moral hazard. Subsequent loans also exhibit a sharp decline indelinquency due to reduction both in moral hazard and adverse selection in group formation.
There are, by contrast, no impacts of information about the credit bureau on the repayment
performance of CB. For SG, there are important group compositional shifts, with a decline infemale membership. Female SG members have less delinquent loans, but they have moreintermediate repayment problems, which are documented by the credit bureau, to theirdetriment. Both SG and CB clients use their knowledge of the bureau to start getting accessto credit from outside lenders. For CB members, outside loans taken by good clients do notlead to repayment problems. This is not the case for the less experienced clients for whomtaking more loans leads to repayment difficulties.
Ironically, in several ways the impact of the credit bureau experiment confirms theefficacy of group lending in combating asymmetric information. First, the improvement in
the lender’s screening ability is larger for individual borrowers than for SG borrowers,
indicating that the degree of asymmetric information was lower for group loans to begin with.
Solidarity groups become smaller when bureau information is available, suggesting that thebureau acts as a substitute for information and incentives otherwise provided by larger
groups. The trainings have no impact on the inside repayment performance of communal
bank borrowers, where group incentives should have been the strongest to begin with. And
finally, those less experienced CB borrowers who increase their net indebtedness run into
repayment problems, indicating that the credit system in the absence of the bureau wasproviding as much credit as these clients could manage without default. Hence, while ourresults show that outcomes can be improved with the use of a bureau, they also highlight thevalue of group lending in enabling lenders to combat asymmetric information.
Genesis is a well-respected lender that used standard best practices for MFI loan
screening prior to introduction of the bureau. Despite this, our results demonstrate that
substantial asymmetric information remained in this market, because the bureau was highly
effective at improving credit market outcomes. Since bureaus are a relatively low-cost34

intervention, this implies that they should be made a part of efforts to achieve financial
deepening in developing countries. Their use appears to be clearly to the benefit of lenders,and in a competitive market, this should lead to lower interest rates for borrowers over time.
The losers from the introduction of a bureau are those borrowers who are screened out as aresult of the information, and ongoing borrowers who may lose insurance opportunities as aresult of the decline in solidarity group size. We show that group reporting can in fact
reinforce the group mechanisms that underlie microfinance lending. The ultimate outcome isefficiency gains for the innovating institution, gains for the more capable borrowers, andincreased economic differentiation across agents.

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Conning, J. 1999. “Outreach, Sustainability, and Leverage in Monitored and Peer-monitored
Lending.” Journal of Development Economics, 60: 51-77.
Galindo, A., & M. Miller. 2001. “Can Credit Registries Reduce Credit Constraints? Empirical
Evidence on the Role of Credit Registries in Firm Investment Decisions,” IADB working
paper.
Gehrig, T., & R. Stenbacka. 2007. “Information Sharing and Lending Market Competition
with Switching Costs and Poaching.” European Economic Review, 51: 77-99.
Ghatak, M., & T. Guinnane. 1999. “The Economics of Lending with Joint Liability: Theory
and Practice.” Journal of Development Economics, 60: 195-228.
Gine, X., & D. Karlan. 2006. “Group versus Individual Liability: A Field Experiment fromthe Philippines.” Yale Economic Growth Center working paper No. 940.
Grief, Avner. 1994. “Cultural Beliefs and the Organization of Society: A Historical andTheoretical Reflection on Collectivist and Individualistic Societies.” Journal of Political
Economy, 102: 912-50.
Jappelli, T., & Marco Pagano. 1999. “Information Sharing, Lending and Defaults: CrossCountry Evidence.” Journal of Banking and Finance, 26(10): 2017-45.

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Karlan, D., and J. Zinman. 2009. “Observing Unobservables: Identifying Information
Asymmetries with a Consumer Credit Field Experiment”. Econometrica, forthcoming.
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McIntosh, C., & B. Wydick. 2007. “Adverse Selection, Moral Hazard, and Credit Information
Systems.” University of California at San Diego.
Morduch, Jonathan. 1999. “The Microfinance Promise.” Journal of Economic Literature, 37(4):1569-1614.
Morduch, Jonathan & Beatriz Armendariz de Aghion. 2005. The Economics of Microfinance,
Harvard University Press: Cambridge, Mass.
Navajas, S., J. Conning, & C. Gonzalez-Vega. 2003. “Lending Technologies, Competition andConsolidation in the Market for Microfinance in Bolivia.” Journal of International
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North, Douglas. 1990. Institutions, Institutional Change, and Economic Performance. Cambridge
University Press, Cambridge.
Padilla, J., & M. Pagano. 2000. “Sharing Default Information as a Borrower Discipline
Device.” European Economic Review, 44: 1951–1980.
Padilla, A.J., & M. Pagano. 1997. “Endogenous Communciation among Lenders andEntrepreneurial Incentives.” Review of Financial Studies, 10 (1): 205-236.
Platteau, Jean-Philippe. 2000. Institutions, Social Norms, and Economic Development, Harwood
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Schreiner, M. 1999. “Lessons for Microenterprise Programs from a Fresh Look at the
Unemployment Insurance Self-Employment Demonstration.” Evaluation Review, 23 (5):504-526.
Stiglitz, J., & A. Weiss. 1981. “Credit Rationing in Markets with Imperfect Information.”
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Economica, 62 (248): 461-478.
36

Table 1. Summary statistics on the Genesis loan portfolio six months prior to any
entry in the credit bureau

Number of clients
Number of groups
Characteristics of clients
Percent female
Percent married
Percent with no education
Percent with some primary education
Percent with more than primary
Average age
Percent ethnic
Percent rural
Average number of loans
New clients per month
Dropout clients per monthCharacteristics of groups
New groups per month
Dropout groups per month
Number of members

Individual borrowers

Solidarity Groups

Communal Banks

6543

8055
1722

10449
1356

48.6
78.7
11.1
66.7
21.0
43.2
27.9
21.7
3.6

46.7
77.7
26.5
68.6
4.0
41.2
53.7
26.5
6.8

99.7
84.6
41.4
55.8
2.4
39.7
-1
-1
4.3

544
490

667
754

902
830

106
118
4.5

91
81
7.7

Characteristics of loans
Total current loans
8111
3094
New loans per month
518
516
Loan size per client (US$)
1137
662
1
Missing information
Data source: Genesis administrative data. Genesis portfolio for the period February-July 2001.

1977
327
324

37

Table 2. Tests of exogeneity of the credit bureau rollout
Branch level monthly average performance
Loan more than 2 months
Late fees
Number ofdelinquentas share of loan size
members
SG
Individual
SG
IndividualSG
Panel A: Monthly average of loan-on-loan changes
Month Crediref began
0.0013-0.0009(1.10)(0.68)

0.0001(1.39)

0.0003(0.95)

0.0022(0.81)

Observations
R-squared

1079
0.06

983
0.04

1079
0.05

983
0.09

0.0242(0.59)
0.0149(0.44)
0.0195(0.58)
0.0202(0.84)

0.0009(0.20)
0.0042(1.06)
0.0017(0.55)
0.0007(0.27)

0.0043(0.94)
0.0050(1.34)
0.0043(1.25)
0.0037(1.52)-0.2143(0.87)
0.1960(0.68)-0.1558(0.77)-0.0616(0.40)

983
0.05

Panel B: Monthly average performance
Month prior to Crediref
0.0330(0.39)
Month 2 prior to Crediref
0.0968(1.21)
Month 3 prior to Crediref
0.0244(0.45)
Months 4-6 prior to Crediref
0.0339(0.91)

Observations
360
413
360
413
360
Number of branches
34
35
34
35
34
Absolute value of t-statistics in parentheses, robust standard errors clustered at the branch level. *significant at 5%; ** significant at 1%.
Panel A: Branch/month level weighted regression with month fixed effects, for pre-treatment period,
January 1998 to July 2001. "Month Crediref began" gives the number of months between January
2001 and the introduction of crediref in each branch.
Panel B: Branch/month level regression with branch and month fixed effects, for pre-treatment
period, during the 12 months prior to each branch entry into Crediref.

38

Table 3. Impact of the credit bureau on screening borrowers in and out
Individual borrowers
Fraction leaving
First time screened1
Subsequent screenings
Observations
Number of branches
Fraction entering
Treatment effectBorrowers within ongoing
Solidarity Groups
Entire Solidarity Groups

0.133(7.86)**-0.020(0.75)

0.194(9.39)**
0.001(0.08)-0.429(7.71)**-0.275(4.62)**

39,95839

51,32437

21,04837

0.149(4.22)**-0.012(1.66)

0.268(5.03)**

Observations
39,95848,362
21,048
Number of branches
39
35
37
Absolute value of t-statistics in parentheses, robust standard errors clustered at branch level.
* significant at 5%; ** significant at 1%
Loans extended February 1999 to May 2004. All regressions include branch and month fixed effects.
1
Last loan taken before the availability of Crediref and paid off at most two months before this date.

39

Table 4. Loan performance of checked and ejected borrowers
Not checked(1)
Individuals
Internal performance prior to Crediref
Late fees > 1% of principal (%)
Loan more than 2 months delinquent (%)
External performance on loans seen in bureau
Percentage of loans with arrears
Average size of loans with arrears (US$)
Number of observations
Members of Solidarity Groups
Internal performance prior to Crediref
Late fees > 1% of principal (%)
Loan more than 2 months delinquent (%)
External performance on loans seen in bureau
Percentage of loans with arrears
Average size of loans with arrears (US$)

8.8
1.4

Checked in Crediref
Not ejected
Ejected
(2)
(3)

13.4
1.8

12.5
1.3

20.8
48.9

36.7
77.4

1663

2152

291

6.9
3.0

7.2
2.4

15.3
7.4

16.8
32.9

33.7
105.1

t-stat for test ofdifference(1) vs (2) (2) vs (3)(4.7)**
(0.9)

(0.4)
(0.5)
(6.2)**
(1.7)

(0.4)
(1.2)

(4.5)**(4.7)**(6.6)**(4.0)**

Number of observations
1662
2562
244
* significant at 5%; ** significant at 1%
Performance on loans taken by current clients from June 2000 to the entry into Crediref. Current clients are defined
as having had a loan until at most 2 months prior to the entry into Crediref. Clients are designated as checked if
their Crediref record was checked in the first two months of availability of the bureau, and among them, as ejected
if they did not take subsequent loans. Not checked refers to continuing clients whose records have not beenchecked in Crediref.

40

Table 5. Impact of screening on individual client composition
Correlates of loan low performance prior
to use of bureau
Low
Variance in
Performance1
performance2
Male

0.0034(4.85)**
0.0039(2.62)**-0.0002(4.94)**

Some secondary education
Age

0.0010(2.31)*
0.0006(0.69)-0.00004(3.03)**

Impact on borrower characteristics3
New clients
Dropouts
ITE
ITE
0.028
(1.27)-0.111(5.14)**-0.309(0.71)

0.077(6.10)**
0.039(2.98)**
0.034(0.15)

Observations
13,55013,55016,063
7,352
R-squared
0.09
0.04
Absolute value of t-statistics in parentheses, robust standard errors clustered at the branch level.
* significant at 5%; ** significant at 1%
1
Low performance is measured by the ratio of late payment fines to the principal. Regressions include branch andmonth fixed effects. Observations are all individual loans extended February 1999 to July 2001.
2
Regression of square residuals from the performance regression, including branch and month fixed effects.
3
Each coefficient comes from a separate regression of the row title characteristic on the treatment and the othertwo characteristics, with branch and month fixed effects. New clients for the period February 1999 to May 2004.
Dropout clients from February 1999 to the first screening after the availability of the credit bureau.

Table 6. Impact of screening on the extensive margin: Performance of new clients
Performance of first loan
Future behavior
Loan more Average loan size
Probability ofMonths until
Growth ofLate fees > than 2 months per borrower
taking subsequent subsequent
subsequent loan
1% of principal
delinquent(US $)
loan
loan taken
per borrower
Individual loans
Treatment effectObservations
R-squared
Mean of dependent variable
Solidarity Group loans
Treatment effect-0.056(4.29)**

0.008(0.54)

79(1.10)

0.240(8.40)**-0.127(1.97)*

0.122(2.35)*

13040
0.02
0.23

13040
0.01
0.08

13040
0.01
882

13040
0.11
0.45

5815
0.04
1.98

5814
0.05
1.37

0.005
(0.24)-0.008(0.35)

209(6.85)**

0.069(1.14)

0.029(0.18)

0.039(0.54)

Observations
5443
5443
5443
5443
2881
2881
R-squared
0.03
0.02
0.07
0.14
0.04
0.07
Mean of dependent variable
0.14
0.11
495
0.53
1.6
1.35
Absolute value of t-statistics in parentheses, robust standard errors clustered at branch level. * significant at 5%; ** significant at 1%
New individual clients and new solidarity groups, from February 1999 to May 2004. All regressions include month and branch fixed
effects.

41

Table 7. Impact of screening on the intensive margin: Performance of ongoing borrowers

Individual loans
Treatment effectObservations
R-squared
Number of borrowers
Mean of dep. variable
Solidarity Group loans
Treatment effectLate fees > 1% ofprincipal

Loan more than 2months delinquent

Average loan size perborrower (US$)

0.036(2.29)*

0.023(3.74)**

82(2.15)*

11203
0.04
3256
0.120

11203
0.02
3256
0.020

11203
0.19
3256
1058

-0.009(0.67)

0.009(1.39)

208(6.60)**

Observations
8796
8796
8796
R-squared
0.03
0.02
0.31
Number of borrowers
1149
1149
1149
Mean of dep. variable
0.040
0.020
872
Absolute value of t-statistics in parentheses, robust standard errors clustered at branch level.
* significant at 5%; ** significant at 1%
Sample of borrowers who have taken loans before and after the credit bureau. All loans extended
after February 1999 and closing by June 2004. All regressions include individual and month fixed
effects, and dummies for loan cycle.

42

Table 8. Aggregate effect of use of the bureau for screening on credit agent performance
Credit officer portfolio
Average value
Treatment effect1
New clients per month
Individual clients

1.56

Members of new Solidarity Groups

1.83

Members of new Communal Banks

2.41

Loans per month
Number

7.17

Total loan amount (US $)11,340Loan performance
Loans with late fees >1% of principal (%)

0.06

Branch level portfolio
Average value
Treatment effect2

0.991(5.08)**
2.73(7.37)**
0.27(0.47)

6.39

1.927(3.31)**
2,343(1.80)

29.38

7.49
9.89

46,461

5.85(5.12)**
15.62(5.41)**-0.18(0.07)
15.319(5.99)**
24,161(3.08)**

-0.02
0.17
-0.003(1.20)(0.20)
Loans more than 2 months delinquent (%)
0.04
0.007
0.08
0.013
(0.51)(0.75)
Absolute value of t-statistics in parentheses, robust standard errors clustered at the branch level.
* significant at 5%; ** significant at 1%
1
Each coefficient comes from a separate regression on the treatment, with month and credit officers fixed effects. Monthly
indicators for 425 credit agents on loans extended after February 1999 and closing by June 2004.
2
Each coefficient comes from a separate regression on the treatment, with month and branch fixed effects.

43

Table 9. Randomization tests for the information experiment
Solidarity Groups

Communal Banks

Panel A: Comparison of pre-treatment covariates and outcomesControl
Selected
t-stat of test
Control
Selected
t-stat of test
groups mean groups mean of difference groups mean groups mean of differenceGroup characteristic:
Number of members
Loan amount per capita (in US$)
Percent divorced
Percent widowed
Percent with no education
Percent with some primary educ.
Percent with more than primary educ.
Percent female
Average age

2.28
886
1.61
4.10
3.34
89.5
6.80
55.3
38.8

2.27
854
1.46
4.51
4.38
90.4
5.02
50.5
37.7

(0.15)(0.47)(0.20)(0.24)(0.99)(0.57)(1.03)(1.01)(1.26)

9.12
297
1.00
3.69
34.0
61.9
4.11
100
36.0

9.28
296
0.48
4.15
35.9
60.5
3.44
99.9
35.6

(0.94)(0.02)(1.04)(0.96)(0.81)(0.76)(0.67)(0.97)(1.29)

Loan performance
Late fees > 1% of principal (%)
Loan more than 2 months

11.2
5.88

6.80
4.17

(1.66)(0.53)

0.62
0.96

0.00
1.15

(0.92)(0.65)

Number of observations

354

680

299

285

Panel B: False treatment effect on repayment outcomes
Borrowers pays late fees > 1% of principal

-5.21
-0.41
(1.45)(0.08)
Loan more than 2 months delinquent
-0.67
-3.23
(0.34)(1.48)
Observations
819
368
Loans taken from February 2003 to June 2004 by the 581 groups participating to the training experiment. Observations
weighted according to the sampling design.
Panel B: Regressions of loan level performance on a false treatment variable, with group and time fixed effects. The pretreatment period is divided in two equal periods with a false treatment set in September 2003. All coefficients multiplied by
100.

44

Table 10. Impact of information on performance

Outcome:
Solidarity Group loans
Delinquent payment4
Amount of late fees (US$)

Intermediatepayments1
ITE-5.51(1.76)
0.42(1.46)

Within loan treatment
Across loans treatment
Final payment Loan performance
Difference2
Difference2
Loan performance3
ITE
ITE
ITE
-10.22(2.30)**-0.35(0.31)

Late fees > 1% of principal 4
Loan more than 2 months delinquent4
Number of observations
Communal Bank loans
Delinquent payment4
Amount of late fees (US$)

4015

396

0.88
(1.76)-0.15(1.25)-1.65(0.44)-0.48(0.50)-6.76(0.86)-6.87(0.79)
396

0.98(0.24)-6.46(3.39)**
1237

Late fees > 1% of principal 4-3.30–(1.20)
Loan more than 2 months delinquent4-3.09–(1.30)
Number of observations
3420
339
339
566
Sample in columns 1-3 includes the loan that was current at the time of the treatment for each of the 735 groups. Sample
in column 4 includes all loans granted from February 2003 and closed by June 2005, except the loan current at treatment
time. Observations weighted according to the sampling design. Absolute value of t-statistics in parentheses, robust
standard errors clustered at branch level. * significant at 5%; ** significant at 1%.
1
Each coefficient corresponds to a separate regression of an outcome on the treatment variable, loan and time fixed effects,
and dummy variables for payments deciles.
2
Each coefficient corresponds to a simple difference between treated and control groups.
3
Each coefficient corresponds to a separate regression that includes group and time fixed effects. None of the 23 loans
taken after the training have late fees higher than 1% of principal or are 2 months delinquent.
4
Coefficients multiplied by 100.

45

Table 11. Impact of information on Solidarity Group size and composition
Correlates of repayment
performance1
Late fees
Loan > 2 months
amount (US$)
delinquent
Number of members-0.56(0.33)

0.76(0.92)

Impact on the averagecharacteristics of clients 2
New clients
Dropouts
ITE
ITE
New/dropout members-0.029-0.023(0.91)(0.90)

Average characteristics by group
Female ratio

5.68
-5.51
-0.2520.167(1.60)(2.42)*(1.45)(2.05)*
Primary education ratio
28.48
13.01
-0.155-0.105(3.77)**(7.71)**(2.02)*(1.45)
Some secondary education ratio
42.60
9.43
-0.0060.023(4.53)**(4.48)**(1.02)(1.73)
Average age
-0.24
0.01
-7.095-1.492(1.34)(0.05)(1.88)(0.85)
Number of observations
921
921
143
216
Absolute value of t-statistics in parentheses, standard errors clustered at the branch level.
* significant at 5%; ** significant at 1%
1
Loan level regression of the repayment performance on group characteristics, with branch and time fixed effects, forloans granted from February 2003 on and due before the training date. Coefficients for delinquent loans are
multiplied by 100.
2Each coefficient corresponds to a separate regression of the characteristic of the group new/dropout clients on thetreatment effect, with time and branch fixed effects. Observations are loans with new/dropout clients for thecharacteristics, but all 1738 loans for the number of new/dropout members.

46

Table 12. Impact of information on outside borrowing
Number of clients
Solidarity Group members
Treatment effect - ITE

1247

Mean value in control group
Heterogeneity among Solidarity Group members3
Less experienced clients - ITE
698
More experienced clients - ITE

549

Good client - ITE874

Bad client - ITE

373

Communal Bank members
Treatment effect - ITE

4172

Mean value in control group
Heterogeneity among Communal Bank members3
Less experienced clients - ITE
2717

Change in numberof loans 1

Start taking
outside loan1

Ever missed
a payment2-0.010(0.13)
0.370

0.100(2.47)*
0.370-0.062(1.91)
0.13

0.012(0.14)-0.075(0.61)-0.032(0.35)
0.031(0.26)

0.145(2.99)**
0.034(0.55)
0.072(1.45)
0.120(1.88)-0.054(1.45)-0.102(2.15)*-0.011(0.42)-0.237(2.91)**

0.178(2.75)**
0.230

0.117
(3.59)**
0.360

0.023
(1.03)
0.08

0.144
0.147
0.047
(2.50)*(4.00)**(2.23)*
More experienced clients - ITE
1455
0.243
0.041
-0.014(2.07)*(0.87)(0.37)
Good client - ITE
3572
0.206
0.122
0.025
(3.04)**(3.48)**(1.91)
Bad client - ITE
600
0.010
0.099
0.062
(0.07)(1.31)(0.63)
Absolute value of t-statistics in parentheses. * significant at 5%; ** significant at 1%
1
Analysis at the client level, with branch fixed effects, and standard errors clustered at the group level.
2
Analysis at the loan level, with time and branch fixed effects, and standard errors clustered at the group level.
3
Experienced (less experienced) clients are clients having had at least 4 (less than 4) loans with Genesis. Good (bad) clients
had no (at least one) delinquent repayment before.

47

Figure 1. Improvement in performance when the bureau is used

Figure 2. Increase in new individual loans when the bureau is used48

Figure 3. Decrease in the size of solidarity groups when the bureau is used.

49