How Rising Competition Among MicrofinanceInstitutions Affects Incumbent Lenders
Craig McIntosh*, Alain de Janvry, and Elisabeth Sadoulet**August 2004

Abstract
This paper uses data from Uganda’s largest incumbent microfinanceinstitution to analyze the impact of entry by competing lenders on client
behaviour. We examine the geographic placement decisions of competitors,
and find that lenders of different types cluster spatially and have pushed intorural areas in recent years. We observe that increased competition induces adecline in repayment performance and in savings deposited with theincumbent Village Bank, suggesting multiple loan-taking by clients. Urban
clients take multiple loans primarily from lenders with more individual
methodologies, while rural clients borrow from several group lenders.
Individuals who operate larger businesses are the ones most likely to leave
the incumbent Village Bank when a Solidarity Group lender enters themarketplace.

Keywords: microfinance, competition, credit markets
JEL classification: O16, D14, L1* International Relations/Pacific Studies, U.C. San Diego, 9500 Gilman Drive, La Jolla, CA,
92093-0519. ctmcintosh@ucsd.edu.
** Department of Agricultural and Resource Economics, U.C. Berkeley.

11. Introduction
The first decade of explosive growth in microfinance saw these institutions lending
as local monopolists. Lenders frequently divided up countries into regions where eachwould hold exclusive sway. As untapped markets have become increasingly saturated,
however, monopolistic positions fell and many countries now see lenders competing directly
for the same clients. This transition has caused a great deal of concern among incumbent
social capital-based lenders as they become less able to price the risk involved inuncollateralized lending and to use dynamic incentives to secure repayment when theirclients face a rising number of outside options.1
The microfinance movement in Uganda took off during the 1990s, while the country
was registering some of the most rapid growth in the developing world. The early ‘90s saw asmall number of subsidy-driven MFIs making loans in relative isolation; however by the late
‘90s MFIs were increasingly overlapping in their geographical coverage. Importantly, more
profit-motivated lenders had entered the fray, shifting the nature of competition between
lenders and expanding the choices faced by consumers. Because FINCA Uganda wasfounded in 1992 and had a broad geographical reach by the end of the decade, it serves as aperfect lens through which to view the impact of this rapid increase in competition.
Opinions among Ugandan microfinance practitioners regarding the importance ofcompetition during the period of this study vary. The chief executives of most of theinstitutions involved in this paper were interviewed on the topic, and few were worried
about competition insofar as it relates to the growth prospects of their institution. A
common concern, however, was that, wherever two or more institutions are operating, many

Social capital-based lending guarantees loans by relying on joint liability by a group of borrowers as opposed
to individual collateral.

12clients may be taking loans from several lenders simultaneously, or ‘double-dipping’. In
interviews with credit officers from several major MFIs, the reasons attributed to borrowers
for double-dipping were to smooth the timing of repayment of loans, to maintain cash flow,
and the fact that interspersing several smaller loans leads to a lower overall cost of credit
given the declining-balance interest calculations prevalent in the industry. Nonetheless, they
were unanimous in the opinion that this behaviour does drive up default rates. Even the
more aggressive institutions which directly target the client base of other lenders worried
about multiple loan-taking, indicating that while they could identify good clients, they were
not able to obtain information about pre-existing debt levels.
In this paper, we exploit the increasingly intense interactions between MFIs in
Uganda between 1998 and 2002 to analyze how borrowers respond to competition between
different kinds of lenders. We begin by reviewing the major theoretical predictions found in
the literature and, on that basis, develop four hypotheses explaining client behaviour that canbe taken to the data. We perform three different but related analyses. First, we examine thedeterminants of lender location decisions, and so gain an understanding of the market
segments occupied by different kinds of lenders. Second, we examine the effect of risingcompetition on group performance in the incumbent MFI. Third, we look at the differential
effect of competition from various kinds of lenders on individual clients with specificcharacteristics.
All of the stylized facts worded by chief executives are borne out by our analysis:competition does not affect the incumbent village bank’s ability to attract new clients nor
does it cause them to lose clients. However, with no formal information sharing mechanism
about clients’ credit histories in place, it does lead to a gradual deterioration in repayment

3performance and to a drop in savings, both of which are consistent with clients responding
to rising competition by engaging in double-dipping.

2. Identification and Endogenous Placement
The major econometric difficulty encountered in studying the effects of competition
on client behaviour is the endogeneity of placement of competitors. New entrants makeplacement decisions based on the characteristics of a location, as evidenced in Section 5.
The implication is that cross-sectional variation in competition is sufficiently endogenous toother factors (and, potentially, directly to the outcomes themselves) as to make unbiased
cross-sectional identification of the impact of competition impossible.
With panel data on group outcomes, we resolve this problem by using group andtime fixed effects throughout the study.

This estimation structure removes all cross-sectional variation from the data, as well as the effects of any changes in outcomes that werecommonly experienced by all groups at a point in time. The impact of competition is thus
identified solely by group-level changes in outcomes that occurred subsequent to the entry
of a competitor.
When we turn to studying the effects of competition on individual clients in Section
7, we do not have a panel that would allow for individual fixed effects. However, since theendogeneity of placement is related to location, group-level fixed effects soak up any part ofoutcomes that is common to the whole group. We use variation between individual clientsand their group mean outcome in order to measure how the characteristics of that individual
affect dropout probability under competition.

This methodology removes any factors

experienced commonly by all members of a group, and so allows us to estimate individual

4interaction effects even in the presence of endogenous placement.

The assumptions

required for unbiased estimation are discussed in detail in Sections 6 & 7.

3. Increasing Competition with Different Degrees of InformationSharing: Four Hypotheses
The literature on microfinance competition, like the phenomenon itself, is quite
recent.

We review this literature to derive four hypotheses (H1 to H4) about how risingcompetition may affect client behaviour toward repayment performance and drop out rates.
These four hypotheses derive from different degrees of information-sharing as they affectenforcement costs, reputation, double-dipping, and financial deepening. Their implications
on repayment performance and dropout rates are summarized in Table 1. We distinguishthe sharing of ‘negative’ information (meaning mutual reporting of defaulters) from thesharing of ‘positive’ information (which functions like a credit bureau, giving comprehensive,
updated information on the total level of indebtedness of an agent seeking a new loan).
Although papers rarely directly address the effect of increased competition inabsence of information sharing on repayment rates, it can indirectly be inferred in somecases. Besley and Coate (1995) show how joint liability can help lenders in markets withoutobservable lending histories overcome information problems, but also that such contracts
remain vulnerable to strategic behaviour by the entire lending group. The implication is that
increased competition could lead to a higher incidence of group default. Marquez (2002)
highlights the fact that competition lowers the screening ability of the incumbent bank, thusincreasing the share of low quality borrowers among clients. In Petersen and Rajan (1998),
competition weakens the long-term relationship between a lender and his clients, thus

5

reducing the lender’s incentives to provide insurance in response to shocks.

Bothcircumstances would lead to lower repayment rates. Most pertinent is a paper by Hoff andStiglitz (1998) which examines the role that multiple uncollateralized lenders will play in
reducing each other's abilities to use dynamic incentives effectively. In the absence of anegative information-sharing network, this ‘enforcement cost’ hypothesis (H1) predicts a fallin repayment and an increase in dropout from the incumbent lender as competition rises.
In the event that information sharing is perfect, then dynamic incentives cannot be
weakened by the entry of new lenders. Indeed, since in such a framework it is the expected
future benefits of access to credit that make borrowers repay uncollateralized loans, a greater
perceived future benefit from the financial system can only increase the set of states in which
agents repay loans. So, while the drop out rate among clients of the incumbent lender may
increase as they take advantage of more remunerative opportunities, under perfect
information sharing they will guard their ‘reputation’ (H2) more closely as the number oflenders increases, leading to an improvement in repayment.
In the case where only negative information is shared, a weaker form of H2 prevails;
lenders cannot perfectly identify client quality, but they can verify that a client has not had
major repayment problems.

This weaker reputation effect present under negative

information sharing is denoted by H3a.

“Absence of bad reputation” should lead to

improved repayment and to a weak increase in dropout. A different strategic problem undernegative-only sharing is presented by McIntosh and Wydick (2004). If there is asymmetric
information as to borrowers’ total indebtedness levels, lenders who coexist with other credit
suppliers will be unable to gauge risk in lending accurately. If only dynamic incentives
prevent default, impatient individuals will take advantage of this, and the result is that theincumbent’s impatient clients do not drop out but rather ‘double-dip’ (H3b) or take multiple

6

loans, and so repayment performance will fall although dropout may not rise. This dynamic
exists independently of the degree to which negative information about past defaults isobservable. A related argument is found in Kletzer (1984), who examines the dynamics ofsovereign default in international lending markets where creditors cannot observe the total
indebtedness of less developed countries.
Since these ‘enforcement cost’, ‘reputation’, and ‘double-dipping’ hypotheses all
hinge on the degree of information-sharing, an attempt to measure the response tocompetition is functionally a test of the degree to which positive and negative information istransmitted, despite the absence of any formal credit reporting mechanism. The net effecton repayment under negative-only sharing is indeterminate; however a worsening of
repayment disqualifies perfect information sharing, and unchanged dropout indicates thatnegative information sharing is neither perfect nor totally absent.
Finally, the “financial deepening” (H4) literature (Greenwood and Jovanovich(1990), King and Levine (1993), Pagano (1993)) examines the complementarities andincreasing returns to scale that may exist in the overall deepening of the country’s financial
sector. As such, the entrance of other financial institutions might be expected to improve
the repayment performance of current borrowers through a kind of general equilibrium
effect, while leaving dropout unchanged. This hypothesis that financial deepening will
improve outcomes across the board is independent of information sharing.
This literature review makes it clear that the theoretical predictions of the response
to competition depend to a large extent on the degree of information sharing between
lenders.

The differential predicted outcomes of increasing competition on repayment

performance and client dropout, according to the degree of information sharing, allow us to7

test which of the four hypotheses is not rejected by the data. This is what we proceed to doin this paper.
Important for incumbent micro-lenders is also to know which types of individuals
are most likely to be drawn away from them by competition. Conning (1999) and Navajas,
Conning, and Gonzales-Vega (2002) present models in which differential abilities atcollateral collection, screening, and monitoring lead different types of lending institutions tofocus on different client bases. According to this theory, given that our data consist solely of
current clients in a village-banking institution, we would expect to see the ‘best’ clients being
drawn away by lenders who invest more in screening and offer better terms. Additionally,
given the pre-eminent role of collateral in mitigating moral hazard in lending, clients who
depart for higher-level lenders should be those that possess the assets that are of use to
those lenders as collateral and as screening tools.

4. Data
The data for this analysis come from several sources. First, and most importantly,
we have group-level information provided by FINCA/Uganda, an institution that gave only
Village Banking-type loans at the time of the study (although now, like many MFIs, it isdiversifying the range of products it offers).. While there are currently over 1,000 lending
groups in the organization, some groups are so new as to preclude their use in this test, anddata are missing on others, leaving us with 780 groups for this study. Competition was
revealed by asking members of FINCA groups to identify, from a list of the eleven othermajor lenders in the country, whether they are present in the area, how long they have been in
the area, and the travel time from the meeting place of the FINCA group to the other lenders’meeting place. This information allows us to construct time-series on the number and8

proximity of competitors retrospectively. Although subjective, we argue that this is theproper way to think about such competition: we should only attempt to measure the impact
of competition from an institution on clients’ behaviour when is has become known tothem. The administrative data give outcomes of all loan cycles that extend back for roughly
two years before the date of the survey and for one year after the survey (December 1998 toAugust 2002).
Second, we have individual surveys conducted on the members of these same
FINCA groups that give us basic information on their asset holdings and the characteristicsof their businesses. The logistical hurdles involved for FINCA employees in collecting sucha large amount of data led to some groups being skipped altogether, leaving us with 6517
questionnaires (from 470 groups), or approximately 50% of the expected census, sufficiently
complete. Student t-tests performed on differences in competition for groups with andwithout individual surveys are not significant. These individual data allow us to examine
how individual characteristics affect the probability of dropping out of FINCA in the face ofcompetition.
Third, we have district-level data compiled from the District Resource Endowment
Profile Survey (DREPS) conducted by the Uganda Bureau of Statistics and Development
Consultants International in 1997. This survey contains data on 65,000 households’ credit
demand, access to amenities, landowning, and so forth.2 From these, we construct variables
describing the prevailing district-level supply for and demand of credit at the beginning of2 This survey interviewed an equal number of households in all of Uganda’s districts; the weights that theyused to generate national-level averages have been criticized as being inaccurate, and so the survey has not been
widely used. We do not need the weights to estimate district-level averages of variables, however, and so infact the survey design proves ideal for our purposes.

9the study.3 The fourth data source is the Statistical Abstract from the 1996 census, which
provides data on population, urbanization, and education at the district level.

4.1. Lender Typology
The lending institutions that compete with FINCA are classified into three types:
1. Village Banking (VB) institutions make loans to groups of twenty or more, require
no collateral, use joint liability, require savings as a mandatory part of the lending
package, do not conduct extensive screening, and generally charge higher interest
rates and give smaller loans than other institutional lenders. Institutions in Uganda
that belong to this category are Uganda Women’s Finance Trust, Exodus, Ugafode,
FOCCUS, Mednet, and Feed the Children.
2. Solidarity Group (SG) lenders lend to joint-liability groups of approximately fiveindividuals, usually require some kind of collateral, screen clients’ businesses prior tolending, and disburse bigger loans at lower cost on more flexible terms than VBs.
Institutions in this category are Pride, Uganda Microfinance Union, and FAULU.
3. Individual lenders disburse loans to individuals, require a larger degree ofcollateralization, conduct much more screening, and give the largest loans and thebest terms. Institutions in this category are Commercial Microfinance and Poverty
Alleviation Project.4

In a section entitled 'Future Credit Demand' there are several questions on household demand for ahypothetical future loan from a Credit scheme for Income Generating Projects. The unmet credit demand is
the response to the question, "What would be the total amount of the loan preferred?". No interest rate wasgiven to the respondents, hence this can be interpreted as a measure of unmet demand at current interest rate.
4 Poverty Alleviation Project has been categorized as an individual lender because it disburses very large loans
to individuals or projects at low (subsidized) interest rates, and so resembles an individual lender more than a
SG or VB lender.
310

4.2. Competition measures
Throughout this analysis, we use three measures of competition defined as follows:- Presence, indicating whether there is any competitor known to the group.
- Number, which records the number of competitors.
- Proximity of the closest competitor, if there is any, measured by Proximity =

1
.
1+ d

Table 2 shows the prevalence and increase in competition faced by the incumbent
FINCA groups, distinguishing rural and urban environments. We see that while competitionof all types is less intense in rural areas, VB lenders are actually closer to FINCA in villages,
presumably because the size of the community is itself limited. In all cases, the increases incompetition during the period are much larger in rural than in urban areas, particularly from
SGs and individual lenders.

5. Competing Lender Location Decisions
Here we measure the extent to which competing lenders locate on the basis of local
characteristics. We estimate the change in the number of competitors between December
1998 and March 2001 around each incumbent FINCA group. Potential factors explaining
increases in competition are district-level characteristics (from a survey conducted in 1997-98
and census data of 1996), and two group-level characteristics (urban or rural environment,
and whether there is presence of a commercial bank known to the clients). This regression
can be expressed as:∆C j = α + β1 X j + β 2 Z j + ε jwhere ∆C j is the change in competition experienced by FINCA group j over the period, X j
is a vector of group-level characteristics, Z j a vector of district-level characteristics for group11

j measured before the period, and ε j an iid error term. Regressions to explain the changes incompetition faced by FINCA groups from each of three kinds of potential entrants to themarket are run separately and reported in the three columns of Table 3. Results clearly showthat competition is strongly determined by cross-sectional variation in characteristics:

VB are aggressively locating in rural areas during the period analyzed.

We see a clustering effect: all lenders are more likely to enter markets characterizedby high pre-existing MFI penetration, close banks, and high pre-existing utilizationof formal banks.



During the period, all types of lenders were more likely to locate in rural districts.

More VBs and SGs than individual lenders entered, but this may be an overall trend,
not necessarily a clustering effect.

6. Impact of Competition on Groups
6.1. Average Effect of Competition
The analysis is done on 766 FINCA groups that experienced 2 to 11 loan cycles
during the period of observation between December 1998 and August 2002. Table 4 shows
the wide range of outcomes experienced by the different groups during the study.
The influence of competition on these outcomes is analyzed with the following
fixed-effects regression5:
O jt = α t + f j + β A jt + θ A2jt + γ VB CVBjt + γ SG CSGjt + γ IND CINDjt + ε jt

5 While it might appear sensible to use a random effects specification, the problem arises that we have strong
cross-sectional identification (endogenous placement) while the dynamic treatment effects are relatively subtle.
This means that a random effects specification focuses on the between-group variation and does not capture
the treatment effect. We, therefore, are required to use only within-group variation, with a fixed-effectspecification, to identify the treatment effect, recognizing that this estimate is a lower bound on the true value.

12where O jt is the outcome of group j in the four-month interval period t; CVBjt , CSGjt , and
CINDjt are competition to group j in time t from VB, SG, and individual lenders, respectively;

α t is a time fixed effect, f j a group-level fixed effect, and A jt the cycle number (or age) of

the group. The use of α t guarantees that the error term is orthogonal to the time dimensionof the competition variable, and similarly f j guarantees that the error term is orthogonal to
the group dimension of the competition variable. The assumption needed for the estimatesof γ to be unbiased, then, is that C jt ⊥ ε jt , or that unexplained changes in outcomes be
unrelated to changes in competition which differ across groups other than through a causal
impact. This assumption would be violated by a process of dynamic endogenous placementin which ∆C jt = g (∆O jt −1 ) , or by any shocks which are experienced differentially across space
and which affect both C jt and O jt .

It is robust to static endogenous placement in which

C jt = g (O j ) t = 0 , or to temporal shocks that are commonly experienced by all groups. The

different group outcomes that we consider are measures of movement in clientele (drop-out
and new client enrolment rates), measures of performance in repayment (percentages ofmembers with excellent and with very poor repayment rates), and measures of group activity(average loan size and saving level among individual group members).
Partial results are reported in Table 5, showing the estimates for γ VB , γ SG , and γ IND
for the six outcomes O, and for the three measures of competition C (presence ofcompetitors, number of competitors, and proximity of the closest competitor).
The main results are the following:Competition is not changing the rate at which clients drop out from and enrol into
the lending groups. This indicates that there are no large-scale defections, defaults,
or expulsions, and that the market does not appear to be ‘saturated’ as a whole, in
13the sense that lenders are competing in a zero-sum game for clients. These results
reject the “enforcement costs” (H1) and the “reputation” (H2 & H3a) hypotheses.


While drop out does not increase, competition from individual and SG lenders
pushes down repayment performance. This suggests that there is double-dippingtaking place by FINCA clients with these two kinds of lenders, and FINCA
repayment is suffering as a result (H3b). However, this result does not carrythrough to other VB lenders. It may be the case that the frequent meetings of VB
lenders make double-dipping difficult, or that FINCA is the top-priority creditor
among VB lenders, or that since all VB lenders use similar social collateral on loansthat FINCA is less affected by double-dipping to these institutions. One way or theother, there appears to be some beneficial impact on repayment of having numerous
local VB lenders (H4), lending credence to the idea that this is a general equilibrium
effect through financial deepening.

Non-response of loan size to competition indicates either that FINCA is the lowestcost provider of credit, or that clients are still credit-constrained in the presence ofcompetition.



Savings are, in the short run, a zero-sum item, and so competing deposit institutions
engage in a division of spoils. As results show, average individual savings declinewith increasing competition from VB and individual lenders.

In summary, the results lead us to reject the “enforcement cost” (H1) and the“reputation” (H2 & H3a) hypotheses regarding the impact of increasing competition on
repayment and dropout for the incumbent Village Bank, as neither are responsive to risingcompetition. The “double-dipping” (H3b) hypothesis is not rejected for competition from14

SG and individual lenders on repayment and dropout, while it is rejected for VB
competition. Double-dipping induced by competition from SG and individual lenders thus
worsens repayment for the incumbent Village Bank. Finally, the “financial deepening” (H4)hypothesis of competition on repayment and dropout for the incumbent Village Bank isrejected for SG and individual lenders, but not for VB, suggesting positive generalequilibrium from Village Banking at the local level.

6.2. Differential Effect of Competition across Local Characteristics
While the previous regression eliminates all cross-sectional identification, we suspectthat it will be the case that the effects of competition on FINCA clients depend on thecharacteristics of the district in which the competition takes place. To analyze the role oflocal characteristics, we retain our group-level fixed-effects but now introduce district-level
characteristics interacted with the presence of competition. Because the fixed effects absorb
all cross-sectional heterogeneity, there is no need to include the non-interacted district-level
characteristics. The econometrics can be specified as:
O jt = α t + f j + β A jt + θ A2jt + βVB CVBjt + β SG CSGjt + β IND CINDjt+ CVBjt Z j γ VB + CSGjt Z j γ SG + CINDjt Z j γ IND + ε jt

where Z j is a vector of group or district-level exogenous characteristics for each group andthe interaction terms CKjt Z j allow to identify how the effect of competition from lender typeK varies across characteristics Z j . These include the location of the group (urban versus
rural) and the district-level average years of education, MFI penetration, and unmet credit
demand.
Results are reported in Tables 6 and 7. In Table 6, competition is measured by thepresence of a competitor, while, in Table 7, competition is measured by the proximity

15

indicator. While few parameters are statistically significant in each regression, the overall
pattern of influence leads to the following conclusions:

Role of education in the district. Clients in more highly educated districts behave as if
there were more information sharing. This can be seen in their higher levels of
repayment under individual (presence) and SG (presence and proximity)
competition, and their higher savings and lower loans under VB (presence)competition. This result has several candidate explanations: it may be the case that
more information is actually transmitted in highly educated districts, that educatedindividuals may consider the dynamic consequences of their actions and thus notdouble dip, or that the educated are able to engage in double-dipping without
repayment problems.



Role of MFI penetration in the district. Higher SG competition has stronger adverse
effects on FINCA clientele (increase in dropout (proximity) and decrease in newclient enrolment (presence and proximity)) if they compete in environments alreadysaturated with lenders.



Role of unmet credit demand in the district. High unmet credit demand in the districtleads to lower repayment performance under individual (presence and proximity)and SG (presence) competition. This points to multiple loan-taking (H3b), andagain provides evidence for the fact that double-dipping is more prevalent with adifferent kind of institution than it is with another VB.

Role of rural location of group. While several pieces of evidence have been presented toindicate that there is more multiple loan-taking from individualized lenders, these
institutions often are not present in the countryside. In Table 1 we saw that SGlenders are more than twice as common in cities as in rural areas, while there is no16

significant difference in the prevalence of VB lenders. Hence, the results that we
see from VB competition in Table 7 are compatible with double-dipping in the
countryside occurring mostly from other VB institutions. Dropout is lower in the
countryside (proximity) where clients take multiple VB loans, and consequently theirsavings in FINCA are driven down (proximity) as they make the minimum savings
contributions in multiple institutions. We conclude that rural clients are most likely
to double-dip from other VBs where different lending products are unavailable andmarkets are poorly saturated.

7. Impact of Competition on Individuals
Who are the individual borrowers most likely to drop out from the FINCA clientele
as a consequence of increased competition? In answering this question, we deal with theendogenous placement problem by using group-level fixed effects. If placement decisions
are made on the basis of observable and unobservable characteristics of the region or thegroups, the direct effect of these characteristics as well as of competition based on thesecharacteristics will pass into the fixed effects. By interacting the competition intensity fromour three types of lenders with individual characteristics, we are able to identify how client
traits increase or decrease their likelihood of leaving under competition relative to the groupmean. Since we do not have data from the other lenders, we cannot tell whether clients areleaving FINCA to go to a specific competitor; rather we infer this by patterns of increased
dropout that coincide with increased competition. The validity of this analysis relies on theassumption that no other unobserved determinants of dropout that vary across individual
characteristics are correlated with the presence of competition.

17

Because of the inconsistency of fixed-effect probit regressions (Chamberlain 1980),
we use a linear probability regression with robust (Hubert-White corrected) standard errors
to estimate the model. The model was tested using the linear probability, probit, and logit
specifications; marginal effects were similar in all three but the robust standard errors in thelinear probability model are substantially larger. The estimated equation is the following:Pr( Dropoutij = 1) = f j + β X i + CVBj X iδVB + CSGj X iδ SG + CINDj X iδ IND + ε iwhere X i represents individual characteristics. We do not include the competition variables
directly, since their average effects are subsumed into the fixed effects.
How should asset holdings affect the probability of dropout under competition? To
draw a direct parallel to Navajas et al. (2002), FINCA, like Bancosol in Bolivia, offers a ‘One
for All’ contract and lets the joint liability mechanism conduct its screening.

Hence,

competing SG and individual lenders should be expected to leverage their abilities in the useof ‘imperfect collateral’ assets that are of high consumption value to the borrower. We
therefore expect that the possession of physical assets will increase the likelihood of acontract being offered by individualized lenders, with particular focus on business size
(stock) for SG lenders, and possibly land for individual lenders.
Results are reported in Table 8. As discussed above, the sample of observations is
reduced to 6517 clients from 470 groups, out of the original 780 groups. This sub-sample ofgroups for which we have individual data does not differ from the overall sample in terms ofcompetition intensity.

Further, even if the intensity of competition for groups withindividual surveys is not representative of the larger population, as long as the selection ofgroups is orthogonal to the interaction between individual characteristics and competition
relative to the group mean, our results are unbiased. The competition interactions are18

calculated as deviations from the mean, so the uninteracted coefficients represent marginal
effects at the average level of competition.
The sole clear result from this analysis is the greatly increased probability of dropout
from the incumbent Village Bank clientele for clients managing large businesses (measured
by value of business stocks) in the face of SG competition. This result is consistent withtheory on the supply side (for the reasons given above) and on the demand side (because
larger businesses require the larger loan sizes and more flexible repayment terms of SGloans). This result is extremely strong and robust to specification. The increase in theprobability of dropout among clients with high business sales under individual lendercompetition could be explained on similar grounds of supply (cash flow as a determinant inindividual loans) and demand (the need for larger loans), but this result is more sensitive to
specification. Land and home ownership are not significant, perhaps because our definitionof ‘individual’ lenders does not include true banks. VB competition appears to exert asimilar pull on clients regardless of their asset holdings.

8. Conclusion
While MFIs were initially established as regional monopolies, competition has been
rising rapidly among lenders as the industry comes of age. The paper analyzes the impactthat this rising competition has on the incumbent lender. Despite the fact that the data usedin this paper are relatively noisy, we have used robust methodologies and the reported results
are those that have been found not to rely on specification. We find that while credit
saturation is lower in rural areas, entrance to the market in the countryside during the period
analyzed is more intense than in cities. In addition, lenders tend to cluster in their location

19

decisions, district-level characteristics strongly determine these decisions, and group-based
lenders increased operations more rapidly than individual lenders.
We find that the entrance of competing lenders induces a deterioration in repayment
performance and a decrease in savings deposits among borrowers of the incumbent lender.
These phenomena are consistent with a model of competition whereby clients do notabandon the incumbent lender but rather take multiple loans, thus damaging their
repayments to the incumbent. Because mandatory savings and minimum savings balances
are standard among MFIs, double-dipping clients are forced to share their scarce savings
amongst the institutions from whom they borrow, reducing their level of savings with theincumbent.
Strikingly, we find no change in the dropout rate or the client enrolment rate whencompetitors of any kind entered the market. Similarly, loan volumes did not change undercompetition. These facts are likely indicative of a market which has yet to be fully saturated,leaving open the possibility that some of the more draconian impacts of competition may yet
be observed as the pool of unmet demand is drained. They also, however, appear todisqualify theories that predict rapid movement of clients from one lender to another when
multiple lenders are present, whether due to the improvement or the destruction of dynamic
incentives. Despite the absence of an aggregate effect on dropout, we find heterogeneity inimpact among individual clients: borrowers with large businesses and high cash flows are themost likely to leave the incumbent as new lenders enter.

While some part of thisheterogeneity likely comes from the demand side, it also implies that entering lenders areable to identify the most promising existing clients.
In many ways, testing for the implications of competition between social-capital
based lenders is completely equivalent to testing for the degree of information sharing

20

prevalent in the market. The observed absence of increase in drop out among clients of theincumbent lender indicates that, although Uganda has no formal mechanisms for the sharing
of information, credit officers and joint-liability groups are able to identify deadbeat
borrowers coming from other lending institutions. This is not altogether surprising, as credit
officers from different organizations are known to meet informally to discuss theperformance of clients in their neighbourhoods.

Particularly in rural areas, clients are

extremely unlikely to be able to default in one joint-liability network without acquiring a
reputation that would preclude their joining another.
The evidence of double-dipping, however, implies that existing informal information
sharing networks are not able to overcome the problem of identifying a borrower’s total
outstanding indebtedness.

Interestingly, our results indicate that clients in districts withhigher education levels behave as if local information sharing were better. Asymmetricpositive information, then, may present a problem that requires a more formal mechanism
for information sharing than now exists in the country, such as a credit bureau.
In conclusion, the results of this paper show that increased competition among MFIlenders calls upon institutional innovations for information sharing on client indebtedness
levels if declining repayment rates are to be avoided. This is necessary to allow proper
assessment of risk under multiple loan-taking. The general equilibrium effect of deeper
credit markets combined with information sharing through credit bureaus should thus allow
to transform increasing competition among lenders into a gain in repayment performanceand downward pressure on interest rates to the benefit of MFI clients.

21

References
Besley, T. and Coate, S. (1995). ‘Group Lending, Repayment Incentives and Social Collateral.’
Journal of Development Economics, Vol. 46, pp. 1-18.
Chamberlain, G. (1980). ‘Analysis of Covariance with Qualitative Data’, The Review of Economic
Studies, Vol. 47, Issue 1, pp. 225-238.
Conning, J. (1999). ‘Outreach, Sustainability and Leverage in Monitored and Peer-Monitored
Lending’, Journal of Development Economics, Vol. 60, pp. 51-77.
Greenwood, J. and Jovanovic, B. (1990). ‘Financial Development, Growth and the Distribution ofIncome’, Journal of Political Economy, Volume 98, No. 5, pp. 1076-1107.
Hoff, K. and Stiglitz, J. (1998). ‘Moneylenders and Bankers: Price-Increasing Subsidies in a
Monopolistically Competitive Market’, Journal of Development Economics, Vol. 55, pp. 485-518.
King, R. and Levine, R. (1993). ‘Finance, Entrepreneurship and Growth: Theory and Evidence’,
Journal of Monetary Economics, Vol. 32, Issue 3, pp. 513-542.
Kletzer, K. (1984). ‘Asymmetries of Information and LDC Borrowing with Sovereign Risk’, THE
ECONOMIC JOURNAL, Vol. 94, Issue 374, pp. 287-307.
Marquez, R. (2002). ‘Competition, Asymmetric Information, and Information Dispersion in the
Banking Industry’, Review of Financial Studies, Vol. 15, No. 3, pp. 901-926.
McIntosh, C. and Wydick, B. (2004). ‘Competition and Microfinance.’ Working paper,
http://www.usfca.edu/fac-staff/wydick/comp&mf.doc
Navajas, S., Conning, J. and Gonzales-Vega, C. (2003). ‘Lending Technologies, Competition, andConsolidation in the Market for Microfinance in Bolivia’, Journal of International Development, Vol
15, pp. 747-770.
Pagano, M. (1993). ‘Financial Markets and Growth: An Overview’, European Economic Review, Vol.
37, Issue 2, pp. 613-622.
Petersen, M. and Rajan, R. (1995). ‘The Effect of Credit Market Competition on Lending
Relationships’, The Quarterly Journal of Economics, Vol. 110, Issue 2, pp. 407-443.

22

Table 1. Hypotheses on the impact of competition according to the degree of information sharing
Predicted impact ofincreasing competition on
Degree of information sharing
Hypothesis
Repayment
Dropout
No information sharing

H1: Enforcement costs

Worsens

Increases

H2: Reputation

Improves

Increases

Only negative information sharing

H3a: Absence of bad reputation
H3b: Double-Dipping

Improves
Worsens

Weakly increases
Unchanged

With or without information sharing

H4: Financial Deepening

Improves

Unchanged

Perfect positive and negative
information sharing

Table 2. Competition to the incumbent FINCA groups

Averages

Urban groups
2001a
1997

Rural groups
2001a
1997

391

389

Number of observations

t-test of ruralurban difference
1997
2001

Village Banks
Presence
Number
Proximity

0.569
0.780
0.090

0.657**
1.081**
0.117*

0.383
0.478
0.108

0.615**
0.972**
0.158*

Solidarity Groups
Presence
Number
Proximity

0.583
0.733
0.094

0.771**
1.077**
0.116

0.161
0.193
0.021

0.365**
0.438**
0.050********

Individual lenders
Presence
Number
Proximity

0.286
0.307
0.042

0.373
0.426
0.056

0.085
0.087
0.022

0.311
0.357*
0.067

**
**

**
**
**

*=95% significance, **=99% significance.
t-test of differences between 1997 and 2001a23

Table 3. Explaining changes in competition intensity using pre-test variables
Dependent variable: Change in the number of competitors between 1998 and 2001 (742 observations)
Mean value Individual lenderof variables Coeff. p-value
Group-level characteristics
Urban = 1
Presence of commercial bank = 1
District-level characteristics
Education (years of schooling)
MFI penetration1 (percent)Unmet credit demand2 ($ '000)
Distance to bank (km)Population with bank account (percent)Population (1000)
Urban (percent)Intercept

Solidarity Group
Coeff. p-value

Village Bank
Coeff p-value

0.49
0.38

0.02
0.10

0.53
0.00

0.05
0.06

0.22
0.12

-0.19
0.12

0.00
0.02

5.1
0.008
0.6
2.6
1.8
635
20.4

-0.12
37.6
0.10
-0.16
0.63
-0.26
-0.004
-0.12

0.00
0.00
0.55
0.00
0.16
0.00
0.00
0.90

0.04
48.8
1.14
-0.08
3.35
0.01
-0.008
-6.77

0.37
0.00
0.00
0.06
0.00
0.91
0.00
0.00

0.06
42.1
1.97
-0.31
5.18
-0.22
-0.010
-9.63

0.33
0.00
0.00
0.00
0.00
0.05
0.00
0.00

Mean value of dependent variable
R-squared

0.144
0.137

0.312
0.114

0.427
0.181

1

Percent of the surveyed population reporting taking an MFI loan.
Size of hypothetical loan desired in thousand U.S. dollars.
All independent variables are measured in 1998, except district population and urban share, measured in 1996.

2

Table 4. Group outcomes during the December 1998 – August 2002 period

Dropout (percent of clients)
New client enrollment (percent of clients)
Excellent repayment (percent of clients)
Very poor repayment (percent of clients)
Average individual loan size (US$)
Average individual savings (US$)

Number ofobservations

Mean

Standard
deviation

4794
4374
4352
4171
4782
4153

5.6
4.2
10.6
2.1
135
53.5

3.9
3.8
7.7
3.2
72
44.2

Minimum Maximum
0
0
0
0
0
0

33
47
40
36
1633
1182

24

Table 5. Fixed-effect estimates of the impact of competition on various group outcomes
Presence
Dependent variables
Independent variables Coefficientp-value
Dropout (percent of clients)
Village bank
Solidarity group
Individual lender

0.07
0.03
-0.42

Measure of competition intensity
Number
Proximity
Coefficientp-value
Coefficientp-value

0.84
0.92
0.42

-0.17
0.01
-0.27

0.32
0.96
0.62

0.04
-0.20
1.55

0.95
0.87
0.31

New client enrollment (percent of clients)
Village bank
0.09
Solidarity group
0.01
Individual lender
0.68

0.78
0.96
0.20

0.19
0.10
0.49

0.29
0.67
0.38

0.24
0.69
2.67

0.73
0.59
0.08

Excellent repayment (percent of clients)
Village bank
-0.16
Solidarity group-1.07
Individual lender
-1.45

0.78
0.03
0.13

0.74
-0.19
-2.12

0.02
0.63
0.03

-1.42
1.07
-9.33

0.25
0.62
0.00

Very poor repayment (percent of clients)
Village bank
-0.11
Solidarity group
0.22
Individual lender
-0.13

0.69
0.36
0.78

-0.12
-0.06
0.03

0.38
0.75
0.95

0.77
1.17
0.14

0.18
0.25
0.91

Average individual loan size (US$)
Village bank
4.4
Solidarity group-3.2
Individual lender
-3.1

0.36
0.46
0.70

0.1
2.0
-3.8

0.97
0.55
0.65

0.5
-22.6
-13.7

0.97
0.23
0.55

Average individual savings (US$)
Village bank
-0.8
Solidarity group-2.3
Individual lender
-24.2

0.84
0.51
0.00

-1.9
-3.4
-21.5

0.41
0.21
0.00

-24.0
-9.8
-27.4

0.01
0.52
0.10

All regressions include year and group fixed effects, and cycle number and its square.
4000 to 4800 observations, depending on the outcome and competition measure, on 766 groups.
Numbers in bold are significant at the 10% level.

25

Table 6. Role of district-level variation on the effects of presence of competition for the incumbent
Partial results
Dropout rate
New client enrollment Excellent repayment
Coefficient p-value Coefficient p-value Coefficient p-value

Average loan size
Coefficient p-value

Village bank interactions (γVB)
District education
Urban group
District MFI penetration
District unmet credit demand

-0.41
-0.72
-106.2
0.003

0.50
0.36
0.47
0.57

0.98
-0.14
35.1
-0.004

0.11
0.86
0.81
0.43

0.00
0.00
0.0
0.00

0.00
0.00
0.00
0.00

-22.08
18.97
-2016
0.145

0.02
0.11
0.37
0.04

Solidarity group interactions (γSG)
District education
Urban group
District MFI penetration
District unmet credit demand

0.89
0.58
132.1
-0.009

0.16
0.38
0.23
0.01

1.14
-1.77
-284.7
0.001

0.07
0.01
0.01
0.78

2.38
0.38
76.7
-0.010

0.03
0.75
0.70
0.12

9.14
8.23
-358.5
0.012

0.34
0.42
0.83
0.83

Individual lender interactions (γind)
District education
Urban group
District MFI penetration
District unmet credit demand

0.32
0.21
230.3
-0.004

0.69
0.80
0.17
0.34

-0.22
-0.46
23.3
0.002

0.79
0.57
0.89
0.69

3.74
1.67
-259.4-0.022

0.01
0.24
0.38
0.00

9.22
-8.80
-2020
-0.011

0.46
0.48
0.43
0.86

4010 to 4616 observations, depending on the outcome variable, on 736 groups
All regressions include year and group fixed effects, cycle number and its square, and the presence of the three types of competition
Numbers in bold are significant at the 10% level.

Table 7. Role of district-level variation on the effects of proximity of competition for the incumbent
Partial results
Dropout rate
New client enrollment Excellent repayment
Coefficient p-value Coefficient p-value Coefficient p-value

Average savings
Coefficient p-value

Village bank interactions (γVB)
District education
Urban group
District MFI penetration
District unmet credit demand

0.29
9.88
-61
-0.016

0.86
0.00
0.87
0.20

0.49
2.31
-26
-0.002

0.77
0.43
0.95
0.90

1.07
-1.47
-469
0.019

0.72
0.78
0.48
0.41

127.63
79.40
16115
-0.94

0.00
0.02
0.00
0.00

Solidarity group interactions (γSG)
District education
Urban group
District MFI penetration
District unmet credit demand

-2.26
-1.79
1528
-0.011

0.52
0.70
0.02
0.58

3.58
-11.41
-1729
0.026

0.30
0.01
0.01
0.20

17.36
1.35
810
-0.086

0.01
0.87
0.48
0.02

-48.77-40.06
-2317
0.33

0.242
0.463
0.763
0.173

Individual lender interactions (γind)
District education
Urban group
District MFI penetration
District unmet credit demand

0.12
-1.04
112
0.002

0.96
0.65
0.78
0.85

-0.24
-3.36
-48
0.001

0.92
0.14
0.91
0.93

6.06
5.20
285
-0.061

0.17
0.20
0.69
0.00

5.26
34.18
1776
-0.17

0.85
0.187
0.706
0.167

3950 to 4547 observations, depending on the outcome variable, on 736 groups.
All regressions include year and group fixed effects, cycle number and its square, and the presence of the three types of competition
Numbers in bold are significant at the 10% level.

26

Table 8. Differential effects of competition on dropout of individual clients
Linear probability model. Dependent variable: dropout = 1/0
Mean value

Coefficientp-value

0.10
0.09
0.09
0.027
0.514
0.70
2.12
0.67
0.47

0.011
0.004
-0.027-0.024-0.015-0.010-0.015-0.007-0.021

0.47
0.83
0.08
0.14
0.03
0.10
0.01
0.52
0.08

Interaction with proximity of individual lenders (δ IND)
Business daily sales (US$ 1000)
Business total stock (US$ 1000)
Number of non-working adults
Client education level (1 to 4)
Household owns its home
Household owns land

0.412
0.000
-0.004-0.003
0.008
0.023

0.05
0.97
0.53
0.82
0.73
0.37

Interaction with proximity of solidarity groups (δ SG)
Business daily sales (US$ 1000)
Business total stock (US$ 1000)
Number of non-working adults
Client education level (1 to 4)
Household owns its home
Household owns land

-0.6580.032
0.040-0.015-0.091
0.016

0.15
0.00
0.13
0.69
0.15
0.81

0.151
-0.015-0.0130.024-0.008
0.087

0.63
0.34
0.50
0.33
0.86
0.08

Direct effects (β )SingleDivorced
Widow
Business daily sales (US$ 1000)
Business total stock (US$ 1000)
Number of non-working adults
Client education level (1 to 4)
Household owns its home
Household owns landInteraction with proximity of village banks (δ VB)
Business daily sales (US$ 1000)
Business total stock (US$ 1000)
Number of non-working adults
Client education level (1 to 4)
Household owns its home
Household owns land
Regression includes group fixed effects. Robust standard errors.
Number of observations: 6517 clients in 470 groups; R2 = .53
Numbers in bold are significant at the 10% level.

27