Description
The purpose of this paper is to present a statistical examination of the factors affecting the
performance of microfinance institutions (MFIs) operating in Eastern Europe and Central Asia.
Journal of Financial Economic Policy
Microfinance institution costs: effects of gender, subsidies and technology
Steven B. Caudill Daniel M. Gropper Valentina Hartarska
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Steven B. Caudill Daniel M. Gropper Valentina Hartarska, (2012),"Microfinance institution costs: effects of
gender, subsidies and technology", J ournal of Financial Economic Policy, Vol. 4 Iss 4 pp. 292 - 304
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Micro?nance institution costs:
effects of gender, subsidies
and technology
Steven B. Caudill
Department of Economics, Rhodes College, Memphis, Tennessee, USA
Daniel M. Gropper
Department of Finance, Auburn University, Auburn, Alabama, USA, and
Valentina Hartarska
Department of Agricultural Economics and Rural Sociology, Auburn University,
Auburn, Alabama, USA
Abstract
Purpose – The purpose of this paper is to present a statistical examination of the factors affecting the
performance of micro?nance institutions (MFIs) operating in Eastern Europe and Central Asia.
Design/methodology/approach – Data on MFIs operating in Eastern Europe and Central Asia
during the period 1999-2004 were used in this study. A statistical analysis of the performance of these
MFIs was conducted utilizing a cost function approach, which was estimated using seemingly
unrelated regressions.
Findings – During the study time period, MFIs involved in the provision of group loans and with a
higher percentage of loans to women had lower costs. The presence of subsidies is also found to be
associated with higher MFI costs.
Social implications – Providing ?nancial services to women, and use of group loans was associated
with lower costs in Eastern Europe and central Asian micro?nance institutions in the early 2000s.
Originality/value – This study focuses exclusively on ef?ciency of MFIs operating in Eastern
Europe and Central Asia, and the ?rst to explicitly measure outreach ef?ciency when output is
measured by number of active clients, rather than the value of the overall MFI lending portfolio.
Keywords Micro?nance institutions, Cost ef?ciency, Technical change, Lending outreach, Loans,
Gender, Eastern Europe, Central Asia
Paper type Research paper
1. Introduction
Micro?nance institutions (MFIs) are important, particularly in developing countries,
because they expand the frontier of ?nancial intermediation by providing loans to those
traditionally excluded from the formal ?nancial markets. The contribution of MFIs to
poverty alleviationhas attractedsigni?cant attentioninrecent years. The UnitedNations
declared 2005 to be the International Year of Microcredit. Although usually small, MFIs
control substantial resources and serve signi?cant numbers of borrowers. For example,
in Eastern Europe and Central Asia (ECA) alone, these organizations have an asset base
of about $1 billion and serve about 500,000 active borrowers (Foster et al., 2003).
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1757-6385.htm
JEL classi?cation – G21, D24
The authors thank the Micro?nance Centre for Central and Eastern Europe and the Newly
Independent States for providing the data for this work.
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Journal of Financial Economic Policy
Vol. 4 No. 4, 2012
pp. 292-304
qEmerald Group Publishing Limited
1757-6385
DOI 10.1108/17576381211279271
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Despite the growing importance of MFIs there are only a few studies of MFI
performance inthe ECAregion(Hartarska andNadolnyak, 2008; Hartarska andMersland,
2012). There has been a substantial prior literature on the cost structure of European
banks (Altunbas et al., 2001; Altunbas and Molyneux, 1996; Pastor, 2002; Pastor et al.,
1997) but only recently have there been systematic studies of bank performance in the
ECA region (Fries and Taci, 2005; Hasan and Marton, 2003; Bonin et al., 2005a, b).
While there has been little systematic empirical work to date on the performance
of MFIs, there have been a few empirical studies in the related areas of relationship
lending and community banking (Berger and Udell, 1995, 2002; Berger et al., 2003, 2004).
This paper adds to the literature bypresenting a systematic statistical examination of
the factors affecting the performance of MFIs operating in ECA. Using data for a sample
of MFIs from the region from 1999 to 2004, we estimate a cost function, incorporating
characteristics which are likely to in?uence productivity (Caudill et al., 2009).
Theoretical work suggests that group lending methodology decreases the costs of
serving marginal clientele both by mitigating problems of adverse selection (Ghatak,
1999; Armendariz de Aghion and Collier, 2000) and moral hazard (Stiglitz, 1990; Laffont
and Rey, 2003; Rai and Sjostromy, 2004). The empirical evidence for non-ECA MFIs
shows that group lending is associated with higher repayment rates (Gomez and Santor,
2003). Armendariz de Aghion and Morduch (2000) argue, however, that individual
lending contracts with dynamic incentives may be more cost-effective in countries in
the ECA region. This paper provides empirical evidence on the cost implications of
group lending.
To ful?ll their poverty alleviation mission MFIs may target women because the
majority of the poor in the areas they serve are female. Because women have less access
to capital, the return to capital may be, on average, higher than for men and therefore,
endowing women with capital may be growth-enhancing. The limited labor mobility
of women can decrease monitoring costs for MFIs and thus reduce the incidence of
strategic default. To date, empirical studies have not focused on the cost consequences
of targeted lending to women in ECA region; this paper provides evidence of the impact
of this practice on the cost structure of MFIs.
Another unusual aspect of MFI operations is the presence of subsidies. Although
ultimately the goal of many MFIs is to become ?nancially self-sustainable, in practice
most receive direct and indirect subsidies. The empirical impact of these subsidies on
ef?ciency is not well understood (Armendariz de Aghion and Morduch, 2005).
We present empirical evidence on how subsidies affect MFI productivity.
To examine and measure these various effects, we estimate a cost function using the
number of borrowers to whom loans are made as the measure of output for the MFI,
which is consistent with the outreach objective of these institutions. We ?nd that the
presence of subsidies is associated with higher MFI costs. This result is consistent across
several different measures of subsidy. We also ?nd that MFIs involved in the provision
of group loans and with a higher percentage of loans to women have lower costs.
2. Micro?nance institutions
Micro?nance has been de?ned as “a collection of banking practices built around
providing small loans (typically without collateral) and accepting tiny savings deposits”
(Armendariz de Aghion and Morduch, 2005, p. 1). MFIs provide ?nancial services to the
entrepreneurial poor who generally do not have access to traditional banking services.
Micro?nance
institution costs
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MFIs pursue a double bottom line of outreach and sustainability. On one hand, MFIs
ful?ll an outreach mission by providing ?nancial services to the poor; the more poor
people served, the greater the output of outreach services. On the other hand, MFIs must
operate like other ?nancial institutions, lending to creditworthy clients and earning
positive returns on their loan portfolios in order to sustain and expand their operations
(sustainability). Because sustainability is an important goal of these organizations, we
assume that MFIs strive to minimize costs of operation for any given level of operations.
Since poor customers generally have no credit history and little collateral, MFIs
must use innovative lending practices to reduce risks associated with asymmetric
information between lender and borrower. In fact, several studies have focused on
understanding the mechanisms of lending practices such as group loans, a type of
joint-liability loan, whereby the MFI delegates screening, monitoring, and contract
enforcement costs to a group, and individual uncollateralized loans, whereby repayment
is “secured” with a promise of access to larger loans in the future conditional on current
loan repayment (Conning, 1999; Navajas et al., 2000). Other studies have focused on the
impact that MFIs have on borrowers (Brau and Woller, 2004).
One way in which MFIs differ greatly from other ?nancial institutions is that many
aspects of MFI operations are characterized by subsidies. For example, the MFI equity
base used to begin operations is typically contributed by an international donor. These
donors include governments in developed countries, international organizations such
as the World Bank, or intermediaries and international networks such as Opportunity
International and FINCA International. If additional funds are required, donors may
offer outright grants or loans at either subsidized or commercial rates, with the recent
trend toward providing loans rather than grants.
In addition, MFIs may receive a variety of in-kind transfers and subsidies in the
form of technical assistance (TA) and/or free physical capital. These subsidies affect
the prices of labor and capital. In-kind subsidies can come in the form of outside funds
for salaries of senior management or outside funds for personnel training. Subsidies of
this kind are provided via TA contracts paid for by either the TA agency or a donor.
In addition to these subsidies, local governments and TA agencies may provide cars,
buildings, or other facilities to MFIs.
2.1 Micro?nance in the ECA region
At the beginning of the transition period, banks naturally focused on lending to larger
and state-owned enterprises. Soon there were severe banking crises in almost every
transition country. Two factors contributed to these crises. One factor was lax bank
licensing policies. A second factor was the failure of banks to impose hard budget
constraints because they correctly assumed that the government would bail them out
(Perotti, 1998). A lingering consequence of these bank crises, both here and elsewhere,
is changes to the legal and regulatory environment in this sector (Amri et al., 2011).
This regulatory environment may make provision of ?nancial services to the poor
even more dif?cult. Systematic reviews of government regulatory practices and
?nancial system performance across countries are provided by Barth et al. (2004) and
Demirgu¨c¸-Kunt et al. (2004).
MFIs emerged as one of the efforts to ?ll the gap in the ?nancial services industry.
In early transition, there was considerable interest in providing credit to small
(,100 employees) and medium (,500 employees) enterprises (SMEs) but not
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to micro-entrepreneurs and self-employed individuals. For example, in the ?rst report
on the state of microcredit in the region, the OECD indicated that out of the 20 SME
programs operating in Poland, the Czech Republic, and Hungary only four reached
microenterprises withsmall amounts of credit while onlythree included microenterprises
as part of their portfolio (Microcredit in Transitional Economies, OECD, 1996).
The ?rst micro?nance initiatives date back to 1992. Initially, large micro?nance
networks such as Opportunity International, CARE International, FINCA, and religious
charities such as Catholic Relief sent missions to various countries to start micro?nance
activities. Some of these initiatives later grew into independent institutions. In addition,
international donors such as the World Bank, United States Agency for International
Development (USAID), and the German Development Agency (GTZ) provided grants
and technical expertise resulting in the creation of new MFIs.
Many international donors have increasingly come to prefer loans to grants for the
establishment of micro?nance activity. In fact, many of the loans to MFIs in ECA
are simply commercial bank loans at the market rate of interest. As a consequence, the
relatively young MFIs in ECA were established relying more heavily on loans than
grants, and so perhaps avoided developing a subsidy-dependent organizational culture.
MFIs in this region operated in an environment unlike that of MFIs in other developing
countries. These differences are manifested in the customers served, the products
offered, and the funding sources available.
2.2 MFI clients and services
Micro?nance initiatives in ECA emerged in an environment with considerable
suspicion of and inexperience with entrepreneurship, as well as lack of experience with
charity and ?nancial services. The widespread mistrust of the entrepreneur gave rise
to a tendency on the part of governments to over-regulate entrepreneurial activity
rather than create an enabling environment. For MFIs entering the market this meant
that there were additional challenges because their clients faced not only ?nancing
constraints but also signi?cant institutional constraints. These disadvantages were
partially offset by the higher educational and skill levels of displaced workers in the
ECA region seeking to become entrepreneurs.
MFIs in the region serve a wide range of clients but cater mostly to the poor and
especially poor women. Foster et al. (2003) report that at one end of the spectrum of MFI
clients are women displaced or widowed by wars who require smaller loans (less than
$1,000) and for whom group loans may be appropriate. At the other end of the MFI
spectrum are loans of up to $2,500 offered to established microenterprises typically
employing one or two people (such as mechanics, hairdressers and in some cases even
doctors and dentists). On rare occasions the larger microenterprises take loans in the
$5,000-10,000 range.
MFIs in the ECA region have adapted their traditional lending technologies and
management practices to accommodate the challenges of the environment and to take
advantage of the opportunities that countries in the ECA region offer (Hartarska, 2005).
For example, even MFIs that once focused on group loans, such as FINCAInternational,
introduced innovative individual lending techniques more appropriate to the new type
of clients (Armendariz de Aghion and Morduch, 2000).
Compared to MFIs in other world regions, MFIs in ECA are among the youngest in
the micro?nance industry but already have showed ?nancial results that are among
Micro?nance
institution costs
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the best in the industry (Benchmarking Micro?nance in Eastern Europe and
Central Asia, Micro?nance Center, 2004). For example, the Micro Banking Bulletin
(MBB) No. 9 shows that in 2003 the average MFI in the ECA region was ?ve years old,
and had a gross portfolio yield of 35 percent (in real terms), and operational
self-sustainability of 131 percent, while the average MFI in the industry was nine years
old with a portfolio yield of 29 percent and operational self-sustainability of 123 percent.
Micro?nance services in the ECA region are offered by four groups of organizations:
(1) non-governmental organizations (NGOs) or ?nancial companies exclusively
devoted to micro?nance;
(2) micro?nance banks, which are chartered commercial banks devoted to
micro?nance services;
(3) commercial banks which offer some microloans usually through a separate unit
within the bank dedicated to MFI activities (called downscaling); and
(4) credit unions.
Only ten credit unions are included in the analysis because most credit unions are not
heavily involved in micro?nance activities. Commercial bank departments engaged in
micro?nance but not operating as independent organizations do not have comparable
data and are excluded from this analysis.
As we state previously, much of what had been written about MFI costs and
productivity is without the bene?t of much empirical support. This problemis a largely a
consequence of the limited availability of MFI data. Using high-quality MFI data, we
specify and estimate cost functions in order to empirically examine several hypotheses
about MFI operations.
3. Methodology and data
3.1 Model
The transcendental logarithmic (translog) form is used for all of the cost estimations.
While there are limitations to the translog form, it has a long history of use in this area
(Ferrier and Lovell, 1990; Altunbas and Molyneux, 1996; DeYoung and Hasan, 1998).
The translog functional form is:
ln C ¼ a
0
þ
X
a
j
ln q
j
þ
X
b
k
ln p
k
þ ð1=2Þ
XX
a
ij
ln q
i
ln q
j
þ ð1=2Þ
XX
b
lk
ln p
l
ln p
k
þ
XX
d
jk
ln q
j
ln p
k
; ð1Þ
where C is total cost, and the q’s and p’s are output quantities and input prices,
respectively. Homogeneity in input prices is required, and is imposed in the estimation
by normalizing (dividing) all input prices and total cost by the price of capital (PCAP).
Also, data are mean-scaled (divided by their means) in order to facilitate calculation of
scale economies. In order to improve ef?ciency of the estimation, we also estimate the
cost share equations:
s
k
¼ b
k
þb
lk
ln p
l
þ · · · þb
jk
ln q
j
; ð2Þ
with cross equation parameter constraints imposed. The translog cost function, along with
the share equations, is estimated using the seemingly unrelated regressions (SUR) method.
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3.2 Data
Lack of high quality data has been the major obstacle to studying MFI costs and
productivity. The micro?nance industry has become more transparent recently as a
result of increased competition among MFIs for donations, subsidies, and loans because
donors have become more selective of the projects they fund. As a consequence,
industry-wide data have become available only in recent years. An important advantage
of the data used here is that these are high-quality data which have been subjected to
several levels of professional review. Simply using ?nancial statements from various
MFIs makes comparisons problematic because many MFIs are non-regulated and
organizationally diverse. Their ?nancial statements might not include all subsidies and
might not be in?ation adjusted. Auditing of ?nancial statements is not required of all
organizational types. Moreover, differences in cross-country accounting standards
complicate the comparison of ?nancial statements across countries.
To correct for such problems, the MBB has developed standards that facilitate
comparisons of MFI ?nancial statements across countries. Individual MFIs from across
the world submit their ?nancial data which is checked and corrected by the MBB staff
or a regional partner. In the case of ECA, MFI data is checked and corrected by the
Micro?nance Center for Central and Eastern Europe (CEE) and the Newly Independent
States (NIS). Staff members of the Micro?nance Center for CEE and NIS carefully
examine each individual ?nancial statement, performing numerous adjustments and
checks, and, when necessary, engage in follow-up data collection to ensure that data
across MFIs is comparable.
The data used in the study have been provided by the Micro?nance Center for CEE
and NIS. The data set contains high quality ?nancial information on MFIs operating in
ECAin for the years 1999-2004. As Table I indicates, the largest numbers of observations
in the sample come from Bosnia and Herzegovina (43), Russia (20), and Georgia (18).
Our selection and speci?cation of regression variables follows prior articles
(Caudill et al., 1995, 2009). The objective function of MFIs is to extend small loans to as
many credit-worthy persons as they can reach, as mentioned earlier in the discussion of
the outreach function of MFIs. Thus, MFIs are most appropriately modeled using the
production approach to estimating ?nancial institution cost functions. In other work,
we explore both the “intermediation approach” and the “production approach” to
modeling MFI costs, as well as a combination of the two (Hartarska et al., 2006).
All ?nancial variables are denominated in USD and adjusted for country-speci?c
in?ation. Lending output is measured as the number of borrowers to whom loans have
been made. Many of the input prices faced by MFIs in the sample are subsidized.
We use the subsidized input prices in our cost function because these are the input
prices actually faced by the MFI managers. In addition, other variables believed to be
related to MFI costs are also explored. A discussion of the construction of each of the
variables used in this study follows.
Labor. The price of labor is calculated as actual personnel expense (unadjusted for
in-kind subsidies such as salary of senior manager paid in by TA or TA money),
divided by the number of employees.
Physical capital. The price of physical capital is calculated as actual operating expense
(unadjusted for in-kind subsidy such as free rent) minus actual personnel expense
divided by the net ?xed assets (that is, ?xed assets net of accumulated depreciation
and adjusted for in?ation to account for appreciation of the physical assets).
Micro?nance
institution costs
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Financial capital. The price of ?nancial capital is actual expense on?nancial capital divided
by the stock of ?nancial capital. Financial expense is calculated as the sum of interest
and fees on borrowing, net of in?ation adjustment expense (calculated as the difference
between in?ation adjustment expense due to in?ation eroding the portfolio and in?ation
revenue resulting from the increased value of ?xed assets) plus other ?nancial expense,
which includes exchange rate related expense. We believe exchange rate expense should
be included in ?nancial capital because many MFIs obtain loans in hard currency (USD or
Euro) but extend loans in local currency and thus face substantial exchange rate risk.
Output. Our measure of lending output (NLoans) is the number of borrowers to
whom loans have been made. Given the typical MFI clientele in this data, it is unlikely
that individuals have more than one loan so we expect a close correspondence between
the number of borrowers and the number of loans.
Total cost. Total cost is the sum of input quantities times input prices – the sum of
?nancial expense, adjusted for in?ation but not for subsidies, plus actual operating
expense.
Estimation of the statistical cost function provides a solid theoretical framework
in which to evaluate a variety of factors related to MFI performance. To do so we
incorporate several exogenous variables directly into the cost function. We include Age,
PctWomen, Group, and several subsidy measures. A brief discussion of each follows.
Age. We include the age of the institution. We expect that learning occurs over the life
of the MFI as managers gain experience in that institution and environment. We expect
older MFIs to be more ef?cient producers, such that costs are lower for a given quantity
of output.
PctWomen. We include the percentage of loans made to women. There are some
?ndings that indicate that loans to women may be less expensive because women have
Country Number of observations
Albania 9
Armenia 9
Azerbaijan 7
Bosnia and Herzegovina 43
Bulgaria 6
Croatia 7
Georgia 18
Kazakhstan 4
Kosovo 6
Kyrgyzstan 8
Macedonia 1
Moldova 1
Mongolia 6
Montenegro 3
Poland 1
Romania 10
Russia 20
Tajikistan 3
Ukraine 3
Uzbekistan 2
Yugoslavia 4
Table I.
Geographic distribution
of sample of micro?nance
institutions
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better repayment rates (Khandker et al., 1995; Hulme, 1991; Gobbons and Kasim, 1991).
These results come from MFIs in non-ECA regions so the precise effect for MFIs in
ECA remains an empirical question.
Group. We also examine the effect of the practice of making group loans by the MFI.
We de?ne this variable to be equal to one if the MFI made loans to either solidarity
groups or village groups. We do not have prior expectations about the sign of this
variable because although evidence fromnon-ECAregions suggests that group lending
has lower costs, other researchers have suggested that innovative individual lending
may work as well and be even less costly in the ECA region (Gomez and Santor, 2003;
Armendariz de Aghion and Morduch, 2000). Indeed, MFIs in the region have come up
with some innovative individual lending practices. For example, in Albania tangible
assets with negligible resale value but high private value are effectively used as
collateral. In Russia, a visit to the home is as important as a look at the ?nancial
statements to ensure better screening. In rural Albania, elements of peer screening are
introduced in individual lending because villagers must obtain a loan guarantee and
a character evaluation by a local village credit committee in order to get a loan.
Subsidy. Our constructed subsidy variable is the sum of two components. The ?rst
component accounts for in-kind payments that subsidize the costs of labor and physical
capital, and is calculated as the difference between adjusted and unadjusted operating
expense. The second component is the cost of subsidized ?nancial capital calculated
as the deposit rate times the average equity, which is the sumof beginning of the year and
end of year equity (which includes current year direct subsidies) divided by two.
We expect that MFIs with subsidies and thus softer budget constraints will have
higher costs.
Sub/NLoan. Sub/NLoan is the amount of subsidy per loan. For the reasons
discussed previously, we expect Sub/NLoan to be positively related to costs.
Subsidy dependence index. The subsidy dependence index (SDI) is another measure
of subsidy that is widely used in the development literature. The SDI, developed by
Yaron (1992), is calculated as subsidy divided by revenue fromlending, where subsidy is
the average cost of capital calculated as the deposit rate times the beginning-of-the-year
equity (the sum of paid in capital, donated equity and retained earnings in years prior
to the current year) plus the deposit rate times one-half of the additions to equity in the
current year. From this amount the value of net income after taxes but before donations
is excluded because this is the revenue generated in the current year. Net income
unadjusted for in?ation is used to construct the SDI. This index is narrowly de?ned to
answer the question by how much a lender must increase its revenues in order to cover
its costs if the lender had no access to subsidized resources.
The variable de?nitions, means, and standard deviations of all variables used in our
analyses are given in Table II. Our original data set contained 171 observations, but
some observations were deleted due to missing or unreliable values. In particular, in the
?nal analysis we include only MFIs with positive values of TC, NLoan, P
L
, P
K
, and P
CAP
.
Ultimately, our regression results are based on a sample of 155 observations.
4. Estimation results
Initially, we considered estimating a cost function with three outputs: loans, short and
long-term?nancial assets, and deposits. We elected not to pursue this speci?cation because
few of the ECA MFIs are involved, to a signi?cant degree, in anything other than loans.
Micro?nance
institution costs
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Several models are estimated to uncover aspects of MFI operations. These estimation
results are contained in Table III, which presents the SUR estimation results. Three
models are estimated incorporating different subsidy measures. Column 2 of Table III
presents the estimation results including our calculated subsidy variable. The model
exhibits an excellent ?t. The system-weighted R
2
is 0.81 and 13 of the 14 regression
coef?cients are statistically signi?cant at the a ¼ 0.10 level or better. The signs and
magnitudes of the coef?cients of NLoan, P
L
, and P
K
are all in accord with theory. The
coef?cient of NLoan is the output elasticity. The value of 0.67 is consistent with the
average MFI operating under conditions of increasing returns to scale. However, this
result must be interpreted with caution because some MFIs do produce other outputs.
Our main interest is in the signs and magnitudes of the added variables: Age,
PctWomen, Group, andSubsidy. Coef?cients of all four of these variables are statistically
signi?cant at the a ¼ 0.10 level or better. The coef?cient of age indicates that costs rise
over time as the MFI continues to operate. This may occur due to rent-seeking behavior
Variable Means (SD)
TC (USD) (total cost) 1,122,827 (1,000,645)
NLoan (number of loans) 5,469.7 (5,971)
P
L
(price of labour in $) 8,792.04 (5,347.11)
P
K
(price of capital) 4.724 (9.84)
P
CAP
(price of ?nancial capital (%)) 0.071 (0.09)
PctWomen (proportion of loans made to women) 0.605 (0.26)
Group (dummy variable indicating that the MFI makes loans to either
groups or villages) 0.077 (0.27)
Subsidy (USD thousands) (the subsidies given to the MFI) 178.32 (201.87)
SDI (subsidy dependence index) 20.008 (0.483)
Table II.
Summary statistics
Variable Model 1 Model 2 Model 3
Intercept 20.462 (3.40) 20.354 (2.91) 20.261 (2.05)
NLoan 0.666 (13.4) 0.781 (17.56) 0.750 (16.04)
P
L
0.437 (49.84) 0.438 (49.89) 0.436 (49.59)
P
K
0.326 (37.12) 0.325 (37.09) 0.329 (37.38)
NLoan
*
NLoan 0.114 (3.94) 0.112 (4.01) 0.141 (4.35)
P
L*
P
L
0.071 (13.24) 0.073 (13.68) 0.073 (14.02)
P
K*
P
K
0.060 (11.96) 0.061 (12.04) 0.062 (12.65)
P
L*
P
K
20.026 (26.68) 20.027 (6.91) 20.028 (7.40)
P
L*
NLoan 0.021 (3.93) 0.021 (4.03) 0.021 (3.97)
P
K*
NLoan 20.006 (1.10) 20.006 (1.03) 20.006 (1.05)
Age 0.028 (1.77) 0.026 (1.72) 0.035 (2.18)
PctWomen 20.458 (3.49) 20.122 (4.06) 20.597 (4.74)
Group 20.229 (1.93) 20.209 (1.81) 20.197 (1.62)
Subsidy 0.001 (3.80) – –
Sub/Nloan – 0.002 (5.51) –
SDI – – 0.023 (0.31)
System-weighted R
2
0.81 0.82 0.81
Note: Numbers in parentheses are absolute values of t-ratios
Table III.
SUR estimation
results – production
approach
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of MFI managers or, possibly, an increase in the size of the loans being offered.
The coef?cient of PctWomen indicates that costs fall as the percentage of loans made to
women increases. This ?nding is consistent with assertions in the development
literature. The coef?cient of the Group variable indicates that those MFIs offering group
loans have lower costs. This ?nding is also consistent with assertions in the development
literature. The positive coef?cient of subsidy indicates that larger subsidies are
associated with higher costs. This result is consistent with our expectations about
inef?ciencies created by the presence of subsidies.
Column 3 of Table III presents the SUR estimation results from a similar cost model
with the only change being that the Subsidy variable is replaced by Sub/NLoan, or
subsidy per loan. The results are very similar to those discussed above for the model
in column 2. The coef?cient of the new variable, subsidy per loan, is positive and
signi?cant indicating, again, that subsidies are associated with increased costs.
In our ?nal estimation results, the subsidy dependence index (SDI) is our measure
of subsidy. These SUR estimation results are shown in column 4 of Table III.
The system-weighted R
2
is 0.81 and 11 of the 14 regression coef?cients are statistically
signi?cant at the a ¼ 0.10 level or better. The output elasticity is estimated to be 0.75.
The signs and magnitudes of the coef?cients of NLoans, P
L
, and P
K
are, again, all in
accord with theory. However, only two coef?cients of the extra variables, Age and
PctWomen, are signi?cantly differently from zero. The coef?cient of age indicates,
again, that costs rise with age. The coef?cient of PctWomen is negative and signi?cant
indicating that making a higher percentage of loans to women reduces costs. The
measure of subsidy in this formulation, SDI, is not signi?cantly different from zero.
5. Conclusions
There are literally hundreds of studies examining the cost structure of banks, savings
and loans, and other ?nancial institutions. Our study estimates a cost function as a
mechanism to examine several productivity and cost issues for MFIs. We examine
MFIs in ECA using 1999-2004 data. When considering our results as a whole some
conclusions about MFI operations appear clear. Supporting the contentions in some of
the development literature, we ?nd that MFIs with a higher percentage of their lending
to women have lower costs. We also ?nd that providing loans using the group lending
approach during this time period reduced costs. Finally, we ?nd that the presence of
subsidies is associated with increased costs. This conclusion holds across several
alternative ways to measure subsidy. Whether those subsidies insulate the MFI
managers from some of the pressures to operate more ef?ciently, offset costs for those
institutions which face higher costs due to some other circumstance, or some other
mechanism is at work, we cannot state de?nitively. However, the correlation between
subsidies and higher costs is fairly robust. While MFIs have typically been established
with subsidies, some evidence (Caudill et al., 2009) suggests that some MFIs can
become more ef?cient over time, and perhaps even survive without subsidies.
The need to achieve an outreach mission distinguishes MFIs from other ?nancial
intermediaries. The evidence found here suggests that MFIs, in their attempt to reach
as many clients as possible, are able to do so at lower costs in the ECAregion by offering
group loans and serving more women. Banks and other traditional ?nancial ?rms
may not be able to compete effectively with MFIs in reaching some of these clients.
However, if the goal is to provide access to substantial amounts of ?nancial capital
Micro?nance
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to the entrepreneurial poor, that is, provide access to larger loans in a cost-effective
manner, traditional bank lending methodologies may be suf?cient for the task.
MFIs have a role to play in extending credit to those who have traditionally been
excluded from formal ?nancial markets, particularly in emerging economies. The
effectiveness with which they perform that role is important in the short term, as they
attempt to reach the greatest number of clients, as well as for the long-term growth and
development of formal ?nancial markets in these regions.
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About the authors
Steven B. Caudill is the Robert D. McCallum Distinguished Professor of Economics at Rhodes
College.
Daniel M. Gropper is the David and Meredith Luck Professor of Finance in the College of
Business at Auburn University. Daniel M. Gropper is the corresponding author and can be
contacted at: [email protected]
Valentina Hartarska is an Associate Professor of Agricultural Economics at Auburn
University.
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This article has been cited by:
1. Alexander Pinz, Bernd Helmig. 2015. Success Factors of Microfinance Institutions: State of the Art
and Research Agenda. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations 26,
488-509. [CrossRef]
2. V. Hartarska, D. Nadolnyak, R. Mersland. 2014. Are Women Better Bankers to the Poor? Evidence from
Rural Microfinance Institutions. American Journal of Agricultural Economics 96, 1291-1306. [CrossRef]
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doc_734510689.pdf
The purpose of this paper is to present a statistical examination of the factors affecting the
performance of microfinance institutions (MFIs) operating in Eastern Europe and Central Asia.
Journal of Financial Economic Policy
Microfinance institution costs: effects of gender, subsidies and technology
Steven B. Caudill Daniel M. Gropper Valentina Hartarska
Article information:
To cite this document:
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gender, subsidies and technology", J ournal of Financial Economic Policy, Vol. 4 Iss 4 pp. 292 - 304
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Micro?nance institution costs:
effects of gender, subsidies
and technology
Steven B. Caudill
Department of Economics, Rhodes College, Memphis, Tennessee, USA
Daniel M. Gropper
Department of Finance, Auburn University, Auburn, Alabama, USA, and
Valentina Hartarska
Department of Agricultural Economics and Rural Sociology, Auburn University,
Auburn, Alabama, USA
Abstract
Purpose – The purpose of this paper is to present a statistical examination of the factors affecting the
performance of micro?nance institutions (MFIs) operating in Eastern Europe and Central Asia.
Design/methodology/approach – Data on MFIs operating in Eastern Europe and Central Asia
during the period 1999-2004 were used in this study. A statistical analysis of the performance of these
MFIs was conducted utilizing a cost function approach, which was estimated using seemingly
unrelated regressions.
Findings – During the study time period, MFIs involved in the provision of group loans and with a
higher percentage of loans to women had lower costs. The presence of subsidies is also found to be
associated with higher MFI costs.
Social implications – Providing ?nancial services to women, and use of group loans was associated
with lower costs in Eastern Europe and central Asian micro?nance institutions in the early 2000s.
Originality/value – This study focuses exclusively on ef?ciency of MFIs operating in Eastern
Europe and Central Asia, and the ?rst to explicitly measure outreach ef?ciency when output is
measured by number of active clients, rather than the value of the overall MFI lending portfolio.
Keywords Micro?nance institutions, Cost ef?ciency, Technical change, Lending outreach, Loans,
Gender, Eastern Europe, Central Asia
Paper type Research paper
1. Introduction
Micro?nance institutions (MFIs) are important, particularly in developing countries,
because they expand the frontier of ?nancial intermediation by providing loans to those
traditionally excluded from the formal ?nancial markets. The contribution of MFIs to
poverty alleviationhas attractedsigni?cant attentioninrecent years. The UnitedNations
declared 2005 to be the International Year of Microcredit. Although usually small, MFIs
control substantial resources and serve signi?cant numbers of borrowers. For example,
in Eastern Europe and Central Asia (ECA) alone, these organizations have an asset base
of about $1 billion and serve about 500,000 active borrowers (Foster et al., 2003).
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1757-6385.htm
JEL classi?cation – G21, D24
The authors thank the Micro?nance Centre for Central and Eastern Europe and the Newly
Independent States for providing the data for this work.
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Journal of Financial Economic Policy
Vol. 4 No. 4, 2012
pp. 292-304
qEmerald Group Publishing Limited
1757-6385
DOI 10.1108/17576381211279271
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Despite the growing importance of MFIs there are only a few studies of MFI
performance inthe ECAregion(Hartarska andNadolnyak, 2008; Hartarska andMersland,
2012). There has been a substantial prior literature on the cost structure of European
banks (Altunbas et al., 2001; Altunbas and Molyneux, 1996; Pastor, 2002; Pastor et al.,
1997) but only recently have there been systematic studies of bank performance in the
ECA region (Fries and Taci, 2005; Hasan and Marton, 2003; Bonin et al., 2005a, b).
While there has been little systematic empirical work to date on the performance
of MFIs, there have been a few empirical studies in the related areas of relationship
lending and community banking (Berger and Udell, 1995, 2002; Berger et al., 2003, 2004).
This paper adds to the literature bypresenting a systematic statistical examination of
the factors affecting the performance of MFIs operating in ECA. Using data for a sample
of MFIs from the region from 1999 to 2004, we estimate a cost function, incorporating
characteristics which are likely to in?uence productivity (Caudill et al., 2009).
Theoretical work suggests that group lending methodology decreases the costs of
serving marginal clientele both by mitigating problems of adverse selection (Ghatak,
1999; Armendariz de Aghion and Collier, 2000) and moral hazard (Stiglitz, 1990; Laffont
and Rey, 2003; Rai and Sjostromy, 2004). The empirical evidence for non-ECA MFIs
shows that group lending is associated with higher repayment rates (Gomez and Santor,
2003). Armendariz de Aghion and Morduch (2000) argue, however, that individual
lending contracts with dynamic incentives may be more cost-effective in countries in
the ECA region. This paper provides empirical evidence on the cost implications of
group lending.
To ful?ll their poverty alleviation mission MFIs may target women because the
majority of the poor in the areas they serve are female. Because women have less access
to capital, the return to capital may be, on average, higher than for men and therefore,
endowing women with capital may be growth-enhancing. The limited labor mobility
of women can decrease monitoring costs for MFIs and thus reduce the incidence of
strategic default. To date, empirical studies have not focused on the cost consequences
of targeted lending to women in ECA region; this paper provides evidence of the impact
of this practice on the cost structure of MFIs.
Another unusual aspect of MFI operations is the presence of subsidies. Although
ultimately the goal of many MFIs is to become ?nancially self-sustainable, in practice
most receive direct and indirect subsidies. The empirical impact of these subsidies on
ef?ciency is not well understood (Armendariz de Aghion and Morduch, 2005).
We present empirical evidence on how subsidies affect MFI productivity.
To examine and measure these various effects, we estimate a cost function using the
number of borrowers to whom loans are made as the measure of output for the MFI,
which is consistent with the outreach objective of these institutions. We ?nd that the
presence of subsidies is associated with higher MFI costs. This result is consistent across
several different measures of subsidy. We also ?nd that MFIs involved in the provision
of group loans and with a higher percentage of loans to women have lower costs.
2. Micro?nance institutions
Micro?nance has been de?ned as “a collection of banking practices built around
providing small loans (typically without collateral) and accepting tiny savings deposits”
(Armendariz de Aghion and Morduch, 2005, p. 1). MFIs provide ?nancial services to the
entrepreneurial poor who generally do not have access to traditional banking services.
Micro?nance
institution costs
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MFIs pursue a double bottom line of outreach and sustainability. On one hand, MFIs
ful?ll an outreach mission by providing ?nancial services to the poor; the more poor
people served, the greater the output of outreach services. On the other hand, MFIs must
operate like other ?nancial institutions, lending to creditworthy clients and earning
positive returns on their loan portfolios in order to sustain and expand their operations
(sustainability). Because sustainability is an important goal of these organizations, we
assume that MFIs strive to minimize costs of operation for any given level of operations.
Since poor customers generally have no credit history and little collateral, MFIs
must use innovative lending practices to reduce risks associated with asymmetric
information between lender and borrower. In fact, several studies have focused on
understanding the mechanisms of lending practices such as group loans, a type of
joint-liability loan, whereby the MFI delegates screening, monitoring, and contract
enforcement costs to a group, and individual uncollateralized loans, whereby repayment
is “secured” with a promise of access to larger loans in the future conditional on current
loan repayment (Conning, 1999; Navajas et al., 2000). Other studies have focused on the
impact that MFIs have on borrowers (Brau and Woller, 2004).
One way in which MFIs differ greatly from other ?nancial institutions is that many
aspects of MFI operations are characterized by subsidies. For example, the MFI equity
base used to begin operations is typically contributed by an international donor. These
donors include governments in developed countries, international organizations such
as the World Bank, or intermediaries and international networks such as Opportunity
International and FINCA International. If additional funds are required, donors may
offer outright grants or loans at either subsidized or commercial rates, with the recent
trend toward providing loans rather than grants.
In addition, MFIs may receive a variety of in-kind transfers and subsidies in the
form of technical assistance (TA) and/or free physical capital. These subsidies affect
the prices of labor and capital. In-kind subsidies can come in the form of outside funds
for salaries of senior management or outside funds for personnel training. Subsidies of
this kind are provided via TA contracts paid for by either the TA agency or a donor.
In addition to these subsidies, local governments and TA agencies may provide cars,
buildings, or other facilities to MFIs.
2.1 Micro?nance in the ECA region
At the beginning of the transition period, banks naturally focused on lending to larger
and state-owned enterprises. Soon there were severe banking crises in almost every
transition country. Two factors contributed to these crises. One factor was lax bank
licensing policies. A second factor was the failure of banks to impose hard budget
constraints because they correctly assumed that the government would bail them out
(Perotti, 1998). A lingering consequence of these bank crises, both here and elsewhere,
is changes to the legal and regulatory environment in this sector (Amri et al., 2011).
This regulatory environment may make provision of ?nancial services to the poor
even more dif?cult. Systematic reviews of government regulatory practices and
?nancial system performance across countries are provided by Barth et al. (2004) and
Demirgu¨c¸-Kunt et al. (2004).
MFIs emerged as one of the efforts to ?ll the gap in the ?nancial services industry.
In early transition, there was considerable interest in providing credit to small
(,100 employees) and medium (,500 employees) enterprises (SMEs) but not
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to micro-entrepreneurs and self-employed individuals. For example, in the ?rst report
on the state of microcredit in the region, the OECD indicated that out of the 20 SME
programs operating in Poland, the Czech Republic, and Hungary only four reached
microenterprises withsmall amounts of credit while onlythree included microenterprises
as part of their portfolio (Microcredit in Transitional Economies, OECD, 1996).
The ?rst micro?nance initiatives date back to 1992. Initially, large micro?nance
networks such as Opportunity International, CARE International, FINCA, and religious
charities such as Catholic Relief sent missions to various countries to start micro?nance
activities. Some of these initiatives later grew into independent institutions. In addition,
international donors such as the World Bank, United States Agency for International
Development (USAID), and the German Development Agency (GTZ) provided grants
and technical expertise resulting in the creation of new MFIs.
Many international donors have increasingly come to prefer loans to grants for the
establishment of micro?nance activity. In fact, many of the loans to MFIs in ECA
are simply commercial bank loans at the market rate of interest. As a consequence, the
relatively young MFIs in ECA were established relying more heavily on loans than
grants, and so perhaps avoided developing a subsidy-dependent organizational culture.
MFIs in this region operated in an environment unlike that of MFIs in other developing
countries. These differences are manifested in the customers served, the products
offered, and the funding sources available.
2.2 MFI clients and services
Micro?nance initiatives in ECA emerged in an environment with considerable
suspicion of and inexperience with entrepreneurship, as well as lack of experience with
charity and ?nancial services. The widespread mistrust of the entrepreneur gave rise
to a tendency on the part of governments to over-regulate entrepreneurial activity
rather than create an enabling environment. For MFIs entering the market this meant
that there were additional challenges because their clients faced not only ?nancing
constraints but also signi?cant institutional constraints. These disadvantages were
partially offset by the higher educational and skill levels of displaced workers in the
ECA region seeking to become entrepreneurs.
MFIs in the region serve a wide range of clients but cater mostly to the poor and
especially poor women. Foster et al. (2003) report that at one end of the spectrum of MFI
clients are women displaced or widowed by wars who require smaller loans (less than
$1,000) and for whom group loans may be appropriate. At the other end of the MFI
spectrum are loans of up to $2,500 offered to established microenterprises typically
employing one or two people (such as mechanics, hairdressers and in some cases even
doctors and dentists). On rare occasions the larger microenterprises take loans in the
$5,000-10,000 range.
MFIs in the ECA region have adapted their traditional lending technologies and
management practices to accommodate the challenges of the environment and to take
advantage of the opportunities that countries in the ECA region offer (Hartarska, 2005).
For example, even MFIs that once focused on group loans, such as FINCAInternational,
introduced innovative individual lending techniques more appropriate to the new type
of clients (Armendariz de Aghion and Morduch, 2000).
Compared to MFIs in other world regions, MFIs in ECA are among the youngest in
the micro?nance industry but already have showed ?nancial results that are among
Micro?nance
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the best in the industry (Benchmarking Micro?nance in Eastern Europe and
Central Asia, Micro?nance Center, 2004). For example, the Micro Banking Bulletin
(MBB) No. 9 shows that in 2003 the average MFI in the ECA region was ?ve years old,
and had a gross portfolio yield of 35 percent (in real terms), and operational
self-sustainability of 131 percent, while the average MFI in the industry was nine years
old with a portfolio yield of 29 percent and operational self-sustainability of 123 percent.
Micro?nance services in the ECA region are offered by four groups of organizations:
(1) non-governmental organizations (NGOs) or ?nancial companies exclusively
devoted to micro?nance;
(2) micro?nance banks, which are chartered commercial banks devoted to
micro?nance services;
(3) commercial banks which offer some microloans usually through a separate unit
within the bank dedicated to MFI activities (called downscaling); and
(4) credit unions.
Only ten credit unions are included in the analysis because most credit unions are not
heavily involved in micro?nance activities. Commercial bank departments engaged in
micro?nance but not operating as independent organizations do not have comparable
data and are excluded from this analysis.
As we state previously, much of what had been written about MFI costs and
productivity is without the bene?t of much empirical support. This problemis a largely a
consequence of the limited availability of MFI data. Using high-quality MFI data, we
specify and estimate cost functions in order to empirically examine several hypotheses
about MFI operations.
3. Methodology and data
3.1 Model
The transcendental logarithmic (translog) form is used for all of the cost estimations.
While there are limitations to the translog form, it has a long history of use in this area
(Ferrier and Lovell, 1990; Altunbas and Molyneux, 1996; DeYoung and Hasan, 1998).
The translog functional form is:
ln C ¼ a
0
þ
X
a
j
ln q
j
þ
X
b
k
ln p
k
þ ð1=2Þ
XX
a
ij
ln q
i
ln q
j
þ ð1=2Þ
XX
b
lk
ln p
l
ln p
k
þ
XX
d
jk
ln q
j
ln p
k
; ð1Þ
where C is total cost, and the q’s and p’s are output quantities and input prices,
respectively. Homogeneity in input prices is required, and is imposed in the estimation
by normalizing (dividing) all input prices and total cost by the price of capital (PCAP).
Also, data are mean-scaled (divided by their means) in order to facilitate calculation of
scale economies. In order to improve ef?ciency of the estimation, we also estimate the
cost share equations:
s
k
¼ b
k
þb
lk
ln p
l
þ · · · þb
jk
ln q
j
; ð2Þ
with cross equation parameter constraints imposed. The translog cost function, along with
the share equations, is estimated using the seemingly unrelated regressions (SUR) method.
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3.2 Data
Lack of high quality data has been the major obstacle to studying MFI costs and
productivity. The micro?nance industry has become more transparent recently as a
result of increased competition among MFIs for donations, subsidies, and loans because
donors have become more selective of the projects they fund. As a consequence,
industry-wide data have become available only in recent years. An important advantage
of the data used here is that these are high-quality data which have been subjected to
several levels of professional review. Simply using ?nancial statements from various
MFIs makes comparisons problematic because many MFIs are non-regulated and
organizationally diverse. Their ?nancial statements might not include all subsidies and
might not be in?ation adjusted. Auditing of ?nancial statements is not required of all
organizational types. Moreover, differences in cross-country accounting standards
complicate the comparison of ?nancial statements across countries.
To correct for such problems, the MBB has developed standards that facilitate
comparisons of MFI ?nancial statements across countries. Individual MFIs from across
the world submit their ?nancial data which is checked and corrected by the MBB staff
or a regional partner. In the case of ECA, MFI data is checked and corrected by the
Micro?nance Center for Central and Eastern Europe (CEE) and the Newly Independent
States (NIS). Staff members of the Micro?nance Center for CEE and NIS carefully
examine each individual ?nancial statement, performing numerous adjustments and
checks, and, when necessary, engage in follow-up data collection to ensure that data
across MFIs is comparable.
The data used in the study have been provided by the Micro?nance Center for CEE
and NIS. The data set contains high quality ?nancial information on MFIs operating in
ECAin for the years 1999-2004. As Table I indicates, the largest numbers of observations
in the sample come from Bosnia and Herzegovina (43), Russia (20), and Georgia (18).
Our selection and speci?cation of regression variables follows prior articles
(Caudill et al., 1995, 2009). The objective function of MFIs is to extend small loans to as
many credit-worthy persons as they can reach, as mentioned earlier in the discussion of
the outreach function of MFIs. Thus, MFIs are most appropriately modeled using the
production approach to estimating ?nancial institution cost functions. In other work,
we explore both the “intermediation approach” and the “production approach” to
modeling MFI costs, as well as a combination of the two (Hartarska et al., 2006).
All ?nancial variables are denominated in USD and adjusted for country-speci?c
in?ation. Lending output is measured as the number of borrowers to whom loans have
been made. Many of the input prices faced by MFIs in the sample are subsidized.
We use the subsidized input prices in our cost function because these are the input
prices actually faced by the MFI managers. In addition, other variables believed to be
related to MFI costs are also explored. A discussion of the construction of each of the
variables used in this study follows.
Labor. The price of labor is calculated as actual personnel expense (unadjusted for
in-kind subsidies such as salary of senior manager paid in by TA or TA money),
divided by the number of employees.
Physical capital. The price of physical capital is calculated as actual operating expense
(unadjusted for in-kind subsidy such as free rent) minus actual personnel expense
divided by the net ?xed assets (that is, ?xed assets net of accumulated depreciation
and adjusted for in?ation to account for appreciation of the physical assets).
Micro?nance
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Financial capital. The price of ?nancial capital is actual expense on?nancial capital divided
by the stock of ?nancial capital. Financial expense is calculated as the sum of interest
and fees on borrowing, net of in?ation adjustment expense (calculated as the difference
between in?ation adjustment expense due to in?ation eroding the portfolio and in?ation
revenue resulting from the increased value of ?xed assets) plus other ?nancial expense,
which includes exchange rate related expense. We believe exchange rate expense should
be included in ?nancial capital because many MFIs obtain loans in hard currency (USD or
Euro) but extend loans in local currency and thus face substantial exchange rate risk.
Output. Our measure of lending output (NLoans) is the number of borrowers to
whom loans have been made. Given the typical MFI clientele in this data, it is unlikely
that individuals have more than one loan so we expect a close correspondence between
the number of borrowers and the number of loans.
Total cost. Total cost is the sum of input quantities times input prices – the sum of
?nancial expense, adjusted for in?ation but not for subsidies, plus actual operating
expense.
Estimation of the statistical cost function provides a solid theoretical framework
in which to evaluate a variety of factors related to MFI performance. To do so we
incorporate several exogenous variables directly into the cost function. We include Age,
PctWomen, Group, and several subsidy measures. A brief discussion of each follows.
Age. We include the age of the institution. We expect that learning occurs over the life
of the MFI as managers gain experience in that institution and environment. We expect
older MFIs to be more ef?cient producers, such that costs are lower for a given quantity
of output.
PctWomen. We include the percentage of loans made to women. There are some
?ndings that indicate that loans to women may be less expensive because women have
Country Number of observations
Albania 9
Armenia 9
Azerbaijan 7
Bosnia and Herzegovina 43
Bulgaria 6
Croatia 7
Georgia 18
Kazakhstan 4
Kosovo 6
Kyrgyzstan 8
Macedonia 1
Moldova 1
Mongolia 6
Montenegro 3
Poland 1
Romania 10
Russia 20
Tajikistan 3
Ukraine 3
Uzbekistan 2
Yugoslavia 4
Table I.
Geographic distribution
of sample of micro?nance
institutions
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better repayment rates (Khandker et al., 1995; Hulme, 1991; Gobbons and Kasim, 1991).
These results come from MFIs in non-ECA regions so the precise effect for MFIs in
ECA remains an empirical question.
Group. We also examine the effect of the practice of making group loans by the MFI.
We de?ne this variable to be equal to one if the MFI made loans to either solidarity
groups or village groups. We do not have prior expectations about the sign of this
variable because although evidence fromnon-ECAregions suggests that group lending
has lower costs, other researchers have suggested that innovative individual lending
may work as well and be even less costly in the ECA region (Gomez and Santor, 2003;
Armendariz de Aghion and Morduch, 2000). Indeed, MFIs in the region have come up
with some innovative individual lending practices. For example, in Albania tangible
assets with negligible resale value but high private value are effectively used as
collateral. In Russia, a visit to the home is as important as a look at the ?nancial
statements to ensure better screening. In rural Albania, elements of peer screening are
introduced in individual lending because villagers must obtain a loan guarantee and
a character evaluation by a local village credit committee in order to get a loan.
Subsidy. Our constructed subsidy variable is the sum of two components. The ?rst
component accounts for in-kind payments that subsidize the costs of labor and physical
capital, and is calculated as the difference between adjusted and unadjusted operating
expense. The second component is the cost of subsidized ?nancial capital calculated
as the deposit rate times the average equity, which is the sumof beginning of the year and
end of year equity (which includes current year direct subsidies) divided by two.
We expect that MFIs with subsidies and thus softer budget constraints will have
higher costs.
Sub/NLoan. Sub/NLoan is the amount of subsidy per loan. For the reasons
discussed previously, we expect Sub/NLoan to be positively related to costs.
Subsidy dependence index. The subsidy dependence index (SDI) is another measure
of subsidy that is widely used in the development literature. The SDI, developed by
Yaron (1992), is calculated as subsidy divided by revenue fromlending, where subsidy is
the average cost of capital calculated as the deposit rate times the beginning-of-the-year
equity (the sum of paid in capital, donated equity and retained earnings in years prior
to the current year) plus the deposit rate times one-half of the additions to equity in the
current year. From this amount the value of net income after taxes but before donations
is excluded because this is the revenue generated in the current year. Net income
unadjusted for in?ation is used to construct the SDI. This index is narrowly de?ned to
answer the question by how much a lender must increase its revenues in order to cover
its costs if the lender had no access to subsidized resources.
The variable de?nitions, means, and standard deviations of all variables used in our
analyses are given in Table II. Our original data set contained 171 observations, but
some observations were deleted due to missing or unreliable values. In particular, in the
?nal analysis we include only MFIs with positive values of TC, NLoan, P
L
, P
K
, and P
CAP
.
Ultimately, our regression results are based on a sample of 155 observations.
4. Estimation results
Initially, we considered estimating a cost function with three outputs: loans, short and
long-term?nancial assets, and deposits. We elected not to pursue this speci?cation because
few of the ECA MFIs are involved, to a signi?cant degree, in anything other than loans.
Micro?nance
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Several models are estimated to uncover aspects of MFI operations. These estimation
results are contained in Table III, which presents the SUR estimation results. Three
models are estimated incorporating different subsidy measures. Column 2 of Table III
presents the estimation results including our calculated subsidy variable. The model
exhibits an excellent ?t. The system-weighted R
2
is 0.81 and 13 of the 14 regression
coef?cients are statistically signi?cant at the a ¼ 0.10 level or better. The signs and
magnitudes of the coef?cients of NLoan, P
L
, and P
K
are all in accord with theory. The
coef?cient of NLoan is the output elasticity. The value of 0.67 is consistent with the
average MFI operating under conditions of increasing returns to scale. However, this
result must be interpreted with caution because some MFIs do produce other outputs.
Our main interest is in the signs and magnitudes of the added variables: Age,
PctWomen, Group, andSubsidy. Coef?cients of all four of these variables are statistically
signi?cant at the a ¼ 0.10 level or better. The coef?cient of age indicates that costs rise
over time as the MFI continues to operate. This may occur due to rent-seeking behavior
Variable Means (SD)
TC (USD) (total cost) 1,122,827 (1,000,645)
NLoan (number of loans) 5,469.7 (5,971)
P
L
(price of labour in $) 8,792.04 (5,347.11)
P
K
(price of capital) 4.724 (9.84)
P
CAP
(price of ?nancial capital (%)) 0.071 (0.09)
PctWomen (proportion of loans made to women) 0.605 (0.26)
Group (dummy variable indicating that the MFI makes loans to either
groups or villages) 0.077 (0.27)
Subsidy (USD thousands) (the subsidies given to the MFI) 178.32 (201.87)
SDI (subsidy dependence index) 20.008 (0.483)
Table II.
Summary statistics
Variable Model 1 Model 2 Model 3
Intercept 20.462 (3.40) 20.354 (2.91) 20.261 (2.05)
NLoan 0.666 (13.4) 0.781 (17.56) 0.750 (16.04)
P
L
0.437 (49.84) 0.438 (49.89) 0.436 (49.59)
P
K
0.326 (37.12) 0.325 (37.09) 0.329 (37.38)
NLoan
*
NLoan 0.114 (3.94) 0.112 (4.01) 0.141 (4.35)
P
L*
P
L
0.071 (13.24) 0.073 (13.68) 0.073 (14.02)
P
K*
P
K
0.060 (11.96) 0.061 (12.04) 0.062 (12.65)
P
L*
P
K
20.026 (26.68) 20.027 (6.91) 20.028 (7.40)
P
L*
NLoan 0.021 (3.93) 0.021 (4.03) 0.021 (3.97)
P
K*
NLoan 20.006 (1.10) 20.006 (1.03) 20.006 (1.05)
Age 0.028 (1.77) 0.026 (1.72) 0.035 (2.18)
PctWomen 20.458 (3.49) 20.122 (4.06) 20.597 (4.74)
Group 20.229 (1.93) 20.209 (1.81) 20.197 (1.62)
Subsidy 0.001 (3.80) – –
Sub/Nloan – 0.002 (5.51) –
SDI – – 0.023 (0.31)
System-weighted R
2
0.81 0.82 0.81
Note: Numbers in parentheses are absolute values of t-ratios
Table III.
SUR estimation
results – production
approach
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of MFI managers or, possibly, an increase in the size of the loans being offered.
The coef?cient of PctWomen indicates that costs fall as the percentage of loans made to
women increases. This ?nding is consistent with assertions in the development
literature. The coef?cient of the Group variable indicates that those MFIs offering group
loans have lower costs. This ?nding is also consistent with assertions in the development
literature. The positive coef?cient of subsidy indicates that larger subsidies are
associated with higher costs. This result is consistent with our expectations about
inef?ciencies created by the presence of subsidies.
Column 3 of Table III presents the SUR estimation results from a similar cost model
with the only change being that the Subsidy variable is replaced by Sub/NLoan, or
subsidy per loan. The results are very similar to those discussed above for the model
in column 2. The coef?cient of the new variable, subsidy per loan, is positive and
signi?cant indicating, again, that subsidies are associated with increased costs.
In our ?nal estimation results, the subsidy dependence index (SDI) is our measure
of subsidy. These SUR estimation results are shown in column 4 of Table III.
The system-weighted R
2
is 0.81 and 11 of the 14 regression coef?cients are statistically
signi?cant at the a ¼ 0.10 level or better. The output elasticity is estimated to be 0.75.
The signs and magnitudes of the coef?cients of NLoans, P
L
, and P
K
are, again, all in
accord with theory. However, only two coef?cients of the extra variables, Age and
PctWomen, are signi?cantly differently from zero. The coef?cient of age indicates,
again, that costs rise with age. The coef?cient of PctWomen is negative and signi?cant
indicating that making a higher percentage of loans to women reduces costs. The
measure of subsidy in this formulation, SDI, is not signi?cantly different from zero.
5. Conclusions
There are literally hundreds of studies examining the cost structure of banks, savings
and loans, and other ?nancial institutions. Our study estimates a cost function as a
mechanism to examine several productivity and cost issues for MFIs. We examine
MFIs in ECA using 1999-2004 data. When considering our results as a whole some
conclusions about MFI operations appear clear. Supporting the contentions in some of
the development literature, we ?nd that MFIs with a higher percentage of their lending
to women have lower costs. We also ?nd that providing loans using the group lending
approach during this time period reduced costs. Finally, we ?nd that the presence of
subsidies is associated with increased costs. This conclusion holds across several
alternative ways to measure subsidy. Whether those subsidies insulate the MFI
managers from some of the pressures to operate more ef?ciently, offset costs for those
institutions which face higher costs due to some other circumstance, or some other
mechanism is at work, we cannot state de?nitively. However, the correlation between
subsidies and higher costs is fairly robust. While MFIs have typically been established
with subsidies, some evidence (Caudill et al., 2009) suggests that some MFIs can
become more ef?cient over time, and perhaps even survive without subsidies.
The need to achieve an outreach mission distinguishes MFIs from other ?nancial
intermediaries. The evidence found here suggests that MFIs, in their attempt to reach
as many clients as possible, are able to do so at lower costs in the ECAregion by offering
group loans and serving more women. Banks and other traditional ?nancial ?rms
may not be able to compete effectively with MFIs in reaching some of these clients.
However, if the goal is to provide access to substantial amounts of ?nancial capital
Micro?nance
institution costs
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to the entrepreneurial poor, that is, provide access to larger loans in a cost-effective
manner, traditional bank lending methodologies may be suf?cient for the task.
MFIs have a role to play in extending credit to those who have traditionally been
excluded from formal ?nancial markets, particularly in emerging economies. The
effectiveness with which they perform that role is important in the short term, as they
attempt to reach the greatest number of clients, as well as for the long-term growth and
development of formal ?nancial markets in these regions.
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About the authors
Steven B. Caudill is the Robert D. McCallum Distinguished Professor of Economics at Rhodes
College.
Daniel M. Gropper is the David and Meredith Luck Professor of Finance in the College of
Business at Auburn University. Daniel M. Gropper is the corresponding author and can be
contacted at: [email protected]
Valentina Hartarska is an Associate Professor of Agricultural Economics at Auburn
University.
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