Finance Research Letters 22 (2017) 81–89
Contents lists available at ScienceDirect
Finance Research Letters
journal homepage: www.elsevier.com/locate/frl
Why do microfinance institutions fail socially? A global
empirical examination
Gregor Dorfleitner a,b,∗, Christopher Priberny a,c, Michaela Röhe a
a
Department of Finance, University of Regensburg, 93040 Regensburg, Germany
CERMi (Centre for European Research in Microfinance), Belgium
c
Deutsche Bundesbank University of Applied Sciences, Germany
b
a r t i c l e
i n f o
Article history:
Received 21 October 2016
Accepted 31 December 2016
Available online 5 January 2017
JEL Classification:
G21
G23
L31
M14
a b s t r a c t
We empirically study social failures of microfinance institutions (MFIs). Besides various
measures for the financial performance and outreach, we consider the relationship between several institutional variables and social failure. Regarding the relationship with the
financial performance, we identify MFIs with good portfolio quality as being less prone
to social failure. Also, MFIs with better measures for the quality of outreach appear to be
less likely to fail socially. Finally, MFIs with a higher fraction of donations and regulated
institutions exhibit a lower probability of social failure, while fast growing MFIs appear to
show a positive correlation.
© 2017 Elsevier Inc. All rights reserved.
Keywords:
Microfinance
Microcredit
MFI social failure
Regulation
Gender
Risk
1. Introduction
In this study, we empirically analyze social failures of microfinance institutions (MFIs) by employing a worldwide data
set of socially rated institutions. MFIs provide financial and non-financial services in developing countries, often to the
economically active poor. Many MFIs pursue a double bottom line approach (see Allet, 2014). For these MFIs not only social
goals are important, but financial self-sufficiency is also found to be relevant. In this context, mission drift is a major concern
for MFIs’ stakeholders such as clients, investors, regulators, etc. The term ‘mission drift’ is frequently used as a synonym for
the possible trade-off between the social and financial performance (for example, by Kar, 2013), a notion which has evolved
as a consequence of the commercialization process within the microfinance industry.
However, empirical analyses on the trade-off show ambiguous results. Supporting evidence for such a trade-off is, for example, identified by Hermes et al. (2011) and Hartarska et al. (2013). In contrast, Quayes (2012, 2015) and Abdullah and
Quayes (2016) find evidence of a positive relationship between the financial and social performance of MFIs. Similarly,
Louis et al. (2013) identify a significant positive interrelation between these two sustainability dimensions.
∗
Corresponding author.
E-mail address: gregor.dorfleitner@ur.de (G. Dorfleitner).
http://dx.doi.org/10.1016/j.frl.2016.12.027
1544-6123/© 2017 Elsevier Inc. All rights reserved.
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G. Dorfleitner et al. / Finance Research Letters 22 (2017) 81–89
A weak social performance caused by the trade-off between the financial performance and the outreach of an MFI could
eventually result in mission drift. In this study, we define an MFI as having socially failed if it does not achieve its social
mission sufficiently, specifically if it is not able to reach a certain minimum requirement regarding its social rating grade.
The main contribution of this paper is to identify a possible relationship between the financial performance and outreach of
an MFI and its likelihood of failing socially. In addition to measures for the financial performance (in terms of profitability
and portfolio quality) and the depth and quality of outreach, we observe the correlation between social failure and several
institutional variables.
Based on a global data set of 215 MFIs, we find a positive relationship between social failure and the write-off ratio,
which measures financial performance and captures the quality of the loan portfolio. We also find MFIs with good measures for the outreach (i.e. low average outstanding balances, a high percentage of female loan officers and high personnel
expenses) to be less likely to fail socially. No significant relationship is identified for the percentage of female borrowers.
Furthermore, regulated MFIs exhibit a lower probability of social failure, and a higher percentage of donations appears to be
negatively related to social failure. Finally, we identify a positive relationship between the growth ratio (regarding number
of active borrowers) and social failure.
The remainder of this paper is organized as follows. First, we provide an overview of the related literature and develop a
set of hypotheses. Then we describe the data and methodology. Next, the results are presented. The final section concludes.
2. Related literature and hypotheses development
2.1. Related literature
In traditional banking, the term “failure” typically describes the closure (or in some cases merger) of an institution, due
to bankruptcy or financial distress. While there is a broad body of literature on traditional bank failures (e.g., Thomson,
1991; Whalen, 1991; Wheelock and Wilson, 20 0 0; Koetter et al., 2007; Männasoo and Mayes, 2009; Jin et al., 2011; Kerstein
and Kozberg, 2013), only few studies examine MFI failures. Literature analyzing failures directly constitutes case studies with
regard to certain failed MFIs (e.g., Rozas, 2009, 2011) and regression analyses (Dorfleitner et al., 2014).
In contrast to financial failures, social failures of MFIs have not been previously investigated. However, the majority of
studies on the performance of MFIs analyzes the financial performance as well as the social performance. If a trade-off
between the financial and social performance leads to a weakening of the social performance, mission drift and possibly
social failure can ultimately follow. Hence studies on factors that are possibly associated with the social performance of
MFIs, also referred to as outreach, represent a relevant strand of related literature.
Mersland and Strøm (2009) investigate the relationship between variables measuring the governance of an institution
and its outreach. Hartarska et al. (2014) investigate the influence of the gender of CEOs in MFIs on the financial performance
and outreach. Their results indicate that institutions with female CEOs appear to show improved financial performance and
outreach. Tchakoute-Tchuigoua (2010) points out that banks and non-bank financial institutions (NBFIs) exhibit a better
breadth of outreach and finds no indications for mission drift. Studies analyzing the performance and outreach of MFIs beyond institutional characteristics comprise Ahlin et al. (2011), who investigate the interrelation of macro-economic variables
with financial performance and outreach, and Assefa et al. (2013), who focus on the effect of competition (measured by the
Lerner-index) on the financial performance in terms of portfolio quality and outreach.
In the microfinance industry, specialized rating agencies, such as M-CRIL, MicroFinanza Rating, MicroRate, and Planet
Rating, provide assessments of the performance of MFIs that are willing and able to undergo the rating process. Microfinance institutional ratings mainly account for factors related to the financial performance, although certain social aspects that
could have an influence on the financial performance, e.g. client protection, are also considered (see Clark and Sinha, 2013).
Beisland and Mersland (2012) emphasize that institutional ratings could facilitate the access to international funding.
In contrast, social ratings evaluate whether or not MFIs achieve their social mission. According to Clark and Sinha (2013),
categories that are considered in a social rating are the country context, social performance management, social responsibility, depth of outreach, quality of services, and outcomes. Empirical analyses of the relationship between microfinance ratings
and MFI-specific variables are restricted to institutional ratings (e.g. Gutiérrez-Nieto and Serrano-Cinca, 2007; Beisland and
Mersland, 2012). In order to study social failures of MFIs, we base our analysis on social rating grades.
2.2. Hypotheses
Regarding the relationship between the financial performance and social failures, there are—on the one hand—studies in
favor of a possible trade-off between measures for the financial and the social performance of an MFI (e.g. Hermes et al.,
2011; Hartarska et al., 2013). On the other hand, for example, Quayes (2012, 2015) shows a positive relationship between the
financial and social performance. Regarding the relationship between the financial performance and the probability of social
failure, the ambiguous results of previous empirical studies do not lead to a clear conclusion with respect to the expected
sign of the relationship. If a trade-off exists and MFIs focus too greatly on their financial performance and too little on the
social one, we can expect a negative relationship between the financial performance and social failure, as we assume that a
bad social performance can precipitate social failure. Based on this background we state the first hypothesis.
G. Dorfleitner et al. / Finance Research Letters 22 (2017) 81–89
83
Hypothesis 1 (H1). The financial performance of an MFI affects its likelihood of social failure.
To measure the relationship between the likelihood of social failure and the outreach, we include measures for the depth
and the quality of outreach (see Schreiner, 20 02; Hishigsuren, 20 07). As variables for the depth of outreach, we employ the
percentage of female borrowers and the average outstanding balance, which is the lower the higher the poverty level of an
MFI’s clients. Furthermore, the percentage of female loan officers and the personnel expense divided by average assets are
considered as measures of the quality of outreach.
In their study on the relationship between governance and the performance of MFIs, Mersland and Strøm (2009) argue
that female CEOs could reduce information asymmetry and may be more aware of the preferences of female clients regarding products and terms. Hence, we additionally analyze the percentage of female loan officers. We assume that female loan
officers interact with more insight during encounters with clients. A similar rationale could be true for the relative personnel expenses. We expect variables indicating a good outreach in terms of depth and quality of outreach of the MFI to be
negatively related to the probability of social failure.
Hypothesis 2 (H2). The depth and quality of outreach of an MFI is negatively related to its likelihood of social failure.
The majority of MFIs integrate donations in their capital structure (see, for example D’Espallier et al., 2013). The donors
providing these funds can be assumed to be in pursuit of certain social goals. In particular, they could encourage MFIs to
enhance their social performance. Furthermore, donors could also provide technical assistance regarding the social performance management of an MFI. D’Espallier et al. (2013) explore differences in the social performance between subsidized
(with donations) and unsubsidized MFIs and summarize that unsubsidized institutions exhibit worse social performance.
Hence, we expect MFIs receiving donations to be less likely to fail socially.
Hypothesis 3 (H3). Donations are negatively related to the likelihood of social failure.
Regulated MFIs can offer a broader range of services to their customers (see, e.g., Arun, 2005). Providing clients with
more types of products is often perceived as being more social. For example, Churchill et al. (2014) argue that clients of
MFIs that offer insurance could benefit from better health and welfare and, therefore, these institutions could exhibit a
better social (and financial) performance. Overall, we expect regulated institutions to be less likely to fail socially.
Hypothesis 4 (H4). Regulation is negatively related to the likelihood of social failure.
Besides the lack of regulation, a rapid growth can be positively related to social failure. Rozas (2011) identifies growth
that happens too quickly as being one of the risk factors for financial failures of MFIs. He argues that the focus on growth
(and profitability) could lead to the neglect of financial risk. With regard to social failures, fast growing MFIs may also set
high interest rates in order to be able to retain earnings or attract international commercial funding. Furthermore, a rapid
growth could also promote over-indebtedness. Therefore, we aim to survey this allegation by investigating the relationship
between the growth rate of an MFI and the growth in terms of the number of borrowers and expect a positive correlation.
Hypothesis 5 (H5). The growth rate of an MFI in terms of the number of borrowers is positively related to the probability
of failure.
3. Data and methodology
3.1. Data
Data sources. Our analysis is based on a unique data set which is derived from five different data sources. Whether or not
an MFI is subject to social failure is derived from social rating reports of the three rating agencies Planet Rating, MicroRate,
and MicroFinanza Rating. More details on the identification process are shown in the next paragraph. Other MFI-specific
data were obtained from the information platform MIX Market1 in April 2016. Because the data from Mix Market are based
on self-reported information provided by MFIs, data inconsistencies can apply. Therefore, we carefully check the data for
unrealistic values, e.g. a share of female borrowers larger than one. Furthermore, we consider macroeconomic indicators,
obtained from the World Bank in April 2016. All five sources combined result in an unbalanced panel consisting of annual
data for the period between 1995 and 20142 including 2776 MFIs. Note that social rating reports were only available for
256 MFIs. The final data set resulted in 215 institutions, as not all MFIs were reporting to MIX Market in the period under
consideration. As our study is based on information provided by rating agencies as well as data obtained from MIX Market,
it is likely to suffer from selection bias, which is a limitation of the data set.
1
2
Cf. www.mixmarket.org.
Note that the reporting for 2015 and 2016 has not been finished. Therefore, we exclude those observations to avoid biased data.
84
G. Dorfleitner et al. / Finance Research Letters 22 (2017) 81–89
Table 1
Number of failed and non-failed MFIs across rating agencies.
Social failure
Rating agency
No
Yes
Total
MicroRate
Microfinanza Rating
Planet Rating
Total
56
68
77
201
1
11
2
14
57
79
79
215
Identifying failures. In the context of this paper, social failure constitutes an extreme form of mission drift. To measure these
behaviors of MFIs, we use social rating grades provided by the specialized microfinance rating agencies Planet Rating, MicroRate, and MicroFinanza Rating. The information on rated institutions and the respective rating grades was obtained from
the websites of the rating agencies in February, 2016. Data on rating grades have been adjusted by eliminating duplicates.
Note, that only the most recent rating grade is considered. For each rating scale, a critical grade is determined that divides
our data set into socially failed and non-failed MFIs.
The rating grades employed by the observed rating agencies are not directly comparable. Therefore, we define social
failure for each rating agency according to the description of the rating categories. Until March 2012, MicroFinanza Rating
used a rating scale with eight categories ranging from S AAA (best rating) to S D (worst rating). For the respective rating
reports, we classify all MFIs rated S C or worse as having socially failed, as the latter represents a “limited likelihood to
achieve social goals” (MicroFinanza Rating, 2012, p. 2). For rating reports after March 2012, MicroFinanza adopted an updated
rating scale comprising six categories (S AA–S D). Previously S C-MFIs, are rated S B, hence we adjust our definition of social
failure for rating reports from April 2012 by including institutions with a rating of S B or worse in our sample of socially
failed MFIs.
Regarding MicroRate, which follows a half-star rating system, MFIs can achieve a rating between 1 star (worst rating) and
5 stars (best rating). According to the description of the rating categories, we consider MFIs with 2 stars or less as having
socially failed, as these institutions exhibit a poor social performance, if any at all. The rating scale of Planet Rating ranges
between 0 (worst rating) and 5 (best rating). For this rating agency, we consider MFIs with a rating of 2 or worse as socially
failed.3
In case of a failure, the reference year is the year in which the failure rating is issued. In case we do not observe a
social failure for an MFI, the reference year is set according to the last rating report available. Table 1 summarizes failed and
non-failed MFIs across rating agencies.
Cross-sectional indicators. Although panel data are available in general, performing a panel analysis is not suitable in our case
because we observe only very few social failures. Therefore, we transform the panel data set into cross-sectional indicators
by utilizing the average value of all available observations in the three-year period preceding the reference year. Additionally,
we construct a growth indicator, namely NBdev, as the average annual growth rate of the number of borrowers calculated
on the basis of the same period. By neglecting observations in the reference year we prevent homemade endogeneity in the
sense that the explanatory variables are clearly observed before the event or non-event of a social failure.
3.2. Estimation technique and explanatory variables
To study the relationship between social failure and variables measuring the financial performance, outreach, and institutional characteristics according to H1–H5, we estimate probit models with Eicker–Huber–White heteroskedastic-consistent
standard errors. An overview of the variables used can be found in Table 2. The dependent variable is a dummy variable
indicating whether or not the respective MFI has failed socially. Note that for several variables that are required for the
analysis, not all data are available. As the number of failed MFIs is already very limited, we impute missing values with
their means.
To measure the financial performance in terms of profitability and portfolio risk, we employ two commonly used variables, namely the return on assets (Mersland and Strøm, 2009; Tchakoute-Tchuigoua, 2010) and the write-off ratio (see e.g.,
D’Espallier et al., 2011). In the empirical microfinance literature, the depth of outreach is often measured by proxies for the
poverty of microfinance clients. For example, borrowers receiving smaller loan amounts are assumed to be poorer. Various
versions of average loan size measures have been used to assess the social sustainability of MFIs (see, for example, Mersland
and Strøm, 2009; Cull et al., 2011). Further ratios indicating the poverty of the clients of an institution are the percentage
of female borrowers (employed, e.g., by Cull et al., 2007, 2011) and the percentage of clients living in rural areas (included,
e.g., by Mersland and Urgeghe, 2013). The measures we include for the depth of outreach are the average outstanding bal-
3
Note, that for Planet Rating we do not include MFIs with a rating of 2+ in the socially failed data sample as the respective category can be interpreted slightly better than MicroFinanza Rating’s category S B (and especially S C, before the update of the rating scale) according to the descriptions of the
categories.
G. Dorfleitner et al. / Finance Research Letters 22 (2017) 81–89
85
Table 2
Definition of variables.
Variable
Description
ROA
WOR
Return on assets. Net operating income less taxes divided by average assets. Source: MIX Market.
Write-off ratio. Obtained by dividing the value of non-collectable loans by the average gross loan
portfolio. Source: MIX Market.
Gross loan portfolio divided by the number of loans outstanding.
Women ratio. Share of MFI borrowers that are female. Source: MIX Market.
Share of loan officers that are female. Source: MIX Market.
Personnel expense divided by average assets. Source: MIX Market.
Indicates whether an MFI is subject to the supervision of a regulatory authority. Dummy variable.
Source: MIX Market.
Donations in USD divided by the average total assets. Source: Derived from MIX income statements.
Number of active borrowers. Source: MIX Market.
NB growth rate. NBdev is the average discrete rate of increase of an MFI’s number of active borrowers.
Source: Derived from MIX Market.
Average gross loan portfolio (mn USD). Average value of the gross loan portfolio at the start and end
of the reporting period in mn USD. Source: Derived from MIX Market.
Deposits in USD divided by the gross loan portfolio. Source: Derived from MIX Market.
Type of institution. Legal status of the MFI: Credit union, bank, non-bank financial institution (NBFI),
non-governmental organization (NGO), and other. Dummy variables. Source: MIX Market.
The geographical regions are Latin America and the Caribbean (LAC), the Middle East and North Africa,
Africa, South Asia, Eastern Europe and Central Asia, and East Asia and the Pacific Area. Source: MIX
Market.
Gross domestic product per capita. USD value of gross domestic product of the country, in which the
MFI mainly operates, divided by its midyear population. Source: World Bank data.
Measure for the operating efficiency of an MFI. Financial revenue divided by the sum of financial
expense, impairment loss, and operating expense. For values below 1, MFIs are not self-sufficient.
Source: Derived from MIX Market.
Indicates if an MFI operates for profit or not (non-profit). The non-profit category includes MFIs with
no information. Dummy variables. Source: MIX Market.
Average outstanding balance
WB
Percent of female loan officers
Personnel expense/assets
Regulated (dummy)
Donations/assets
NB
NBdev
Average GLP (mn USD)
Deposits/loans
Type
Region
GDPpc
Operational self-sufficiency
Profit status (dummy)
ance and the women ratio (WB). Furthermore, we include measures for the quality of outreach (see Hishigsuren, 2007), the
percentage of female loan officers, and the personnel expense relative to an MFI’s assets.
We also include three institutional variables in order to test H3–H5. First, the ratio of donations to average assets (H3)
is observed. Second, a dummy variable indicating whether or not an MFI is regulated (H4) is employed. Finally, we consider
NBdev, the growth rate of the number of borrowers of an MFI (H5).
To control for MFI-specific and macro-economic effects, we incorporate a set of additional variables. Besides control
variables for the size of the institution (log(avgGLP) and log(avgGLP)2 ), we account for the type of MFI. Due to a limited
sample of socially failed MFIs, the consideration of organizational type dummies does not appear to be reasonable. Instead,
other variables are included to specify the type of institution. On the one hand, the deposits to loans ratio measures whether
and to which extent an MFI uses deposits to cover its lending activities. On the other hand, a for-profit dummy that indicates
the profit status of an institution is included. Macro-economic control variables comprise a dummy variable indicating if an
MFI operates in the region Latin America and the Caribbean, the gross domestic product per capita, and the Gini index of
the country in which the MFI mainly operates.
4. Results
4.1. Descriptive analysis
Table 3 presents frequency statistics of the categorical variables. A closer look reveals that not all of the different MFI
types are represented among the failed MFIs as banks and MFIs of the type Other demonstrate no failures. The same holds
true regarding the regions, which comprise no failures in South Asia and in Middle East and North Africa. This issue is
addressed in the regression analysis below as we do not use the MFI type at all but instead consider the deposits to
loans ratio and the dummy representing the information on whether it is a for-profit institution. The regional information is reduced to whether the MFI is located in Latin America and the Caribbean or the rest of the world below. Considering the regional distribution, one can state that our sample is surely not representative for the global population of
MFIs as South Asia is under-represented. However, concerning all other categorical variables, the sample appears to be
well-balanced.
Table 4 provides descriptive statistics of the metric variables. While Panel A displays the statistics for the entire sample,
Panel B provides information on the sub-sample of failed MFIs. Generally, the whole sample reveals a natural distribution
of the variable values, comparable with that of other studies on global MFIs (see e.g. Dorfleitner et al., 2013). However, the
differences to the failed sample are remarkable and provide a first indication that the evidence may generally be in favor of
the hypotheses.
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G. Dorfleitner et al. / Finance Research Letters 22 (2017) 81–89
Table 3
Frequencies for categorical variables based on the unimputed data sets. The variables are
defined in Table 2.
Social Failure
No
Obs.
Type
Bank
Credit union
NBFI
NGO
Other
Yes
%
Obs.
Total
%
Obs.
%
21
19
82
76
3
10.45
9.45
40.80
37.81
1.49
0
3
9
2
0
0.00
21.43
64.29
14.29
0.00
21
22
91
78
3
9.77
10.23
42.33
36.28
1.40
Region
Africa
East Asia and the Pacific
Eastern Europe and Central Asia
Latin America and The Caribbean
Middle East and North Africa
South Asia
34
22
27
102
13
3
16.92
10.95
13.43
50.75
6.47
1.49
7
1
4
2
0
0
50.00
7.14
28.57
14.29
0.00
0.00
41
23
31
104
13
3
19.07
10.70
14.42
48.37
6.05
1.40
Regulated
No
Yes
No information
67
133
1
33.33
66.17
0.50
5
8
1
35.71
57.14
7.14
72
141
2
33.49
65.58
0.93
Operational self-sufficiency
No
Yes
No information
34
164
3
16.92
81.59
1.49
1
12
1
7.14
85.71
7.14
35
176
4
16.28
81.86
1.86
Profit status
Non-profit
For-profit
No information
118
82
1
58.71
40.80
0.50
6
8
0
42.86
57.14
0.00
124
90
1
57.67
41.86
0.47
Deposits
No
Yes
No information
94
104
3
46.77
51.74
1.49
3
11
0
21.43
78.57
0.00
97
115
3
45.12
53.49
1.40
Donations
No
Yes
No information
Total
93
96
12
201
46.27
47.76
5.97
6
5
3
14
42.86
35.71
21.43
99
101
15
215
46.05
46.98
6.98
The mean of the ROA and also of the WOR is higher in the failed sample, while—unsurprisingly—all of the variables
measuring the outreach, on average, are worse there. Additionally, we see less donations and a higher growth of the number
of borrowers (NBdev) in the sub-sample of failed MFIs. However, these observations do not allow final statistically sound
conclusions as we do not test for the significance of the differences and, of course, the influence of all variables is to be
measured simultaneously. To address this issue, we perform a regression analysis in the next step.
4.2. Regression analysis
Table 5 shows the coefficients of probit regressions with Eicker–Huber–White heteroskedastic-consistent standard errors.
The dependent variable is the social failure dummy. Model specification (1) includes financial performance measures (H1),
model (2) observes outreach measures (H2), and model specification (3) focusses on institutional variables relating to H3–
H5. Model specification (4) employs financial performance, outreach and institutional variables. All regressions are estimated
considering the MFI-specific and macro-economic control variables described above.
The findings of the regressions are surprisingly clear. Concerning hypothesis H1, which relates to the influence of the
financial performance on the failure, the return on assets at first sight (model (1)) appears to be positively related, indicating
mission drift. However, in the full regression including all explanatory variables, the coefficient is not significant anymore.
Interestingly, the WOR ratio has a significant positive coefficient, implying that high write-off ratios, which are an aspect of
a bad financial performance, can predict social failures. A possible interpretation of this finding is the phenomenon of overindebtedness among the borrowers. If an MFI encourages less creditworthy borrowers into taking one or several loans then
this can lead to over-indebtedness of the borrowers, which is, on the one hand, assessed negatively by microfinance rating
agencies and is an indication of a social failure. On the other hand, it also does harm to the MFI itself as it results in high
write-offs on granted loans, which is the variable we measure. Summarizing, there are no indications that high ROA values
G. Dorfleitner et al. / Finance Research Letters 22 (2017) 81–89
87
Table 4
Descriptive statistics for metric variables based on the unimputed data set. The variables are defined in Table 2.
Quantiles
Variable
n
Mean
S.D.
Panel A: Full Sample
ROA
Personnel expense/assets
WOR
Average outstanding balance
WB
Percent of female loan officers
Average GLP (mn USD)
Donations/assets
NBdev
NB (in mn)
Deposits/loans
GDPpc (in thousand USD)
Gini index
208
197
204
205
201
122
215
201
203
211
212
215
170
0.026
0.111
0.018
1078.885
0.634
0.358
47.686
0.028
0.200
0.056
0.241
3.477
44.461
0.064
0.076
0.022
1193.233
0.233
0.272
119.240
0.092
0.284
0.180
0.358
2.783
7.735
Min
−0.379
0.009
0.0 0 0
40.667
0.087
0.0 0 0
0.239
−0.002
−0.391
0.0 0 0
0.0 0 0
0.233
16.640
0.25
0.011
0.059
0.003
288.667
0.474
0.113
3.368
0.0 0 0
0.043
0.007
0.0 0 0
1.152
40.280
Mdn
0.026
0.091
0.011
608.0 0 0
0.594
0.331
8.334
0.0 0 0
0.141
0.016
0.019
2.745
46.163
0.75
0.054
0.134
0.026
1496.333
0.846
0.550
37.975
0.010
0.285
0.055
0.424
4.989
49.867
Max
0.214
0.544
0.132
6368.0 0 0
1.0 0 0
1.0 0 0
994.999
0.786
1.525
2.439
2.220
12.983
59.370
Quantiles
Variable
Panel B: Failed MFIs
ROA
Personnel expense/assets
WOR
Average outstanding balance
WB
Percent of female loan officers
Average GLP (mn USD)
Donations/assets
NBdev
NB (in mn)
Deposits/loans
GDPpc (in thousand USD)
Gini index
n
13
12
12
12
11
8
14
11
12
13
14
14
9
Mean
S.D.
Min
0.047
0.072
0.026
1360.917
0.532
0.163
5.946
0.010
0.391
0.017
0.501
1.930
42.682
0.064
0.028
0.025
1541.945
0.195
0.137
6.453
0.016
0.462
0.034
0.634
2.125
7.189
−0.018
0.028
0.0 0 0
120.667
0.199
0.0 0 0
0.239
0.0 0 0
0.048
0.0 0 0
0.0 0 0
0.233
33.480
0.25
0.011
0.049
0.006
329.833
0.408
0.0 0 0
1.878
0.0 0 0
0.056
0.002
0.009
0.703
36.963
Mdn
0.020
0.076
0.015
568.0 0 0
0.551
0.227
3.518
0.0 0 0
0.158
0.005
0.329
1.329
41.855
0.75
0.071
0.090
0.048
2351.750
0.611
0.274
6.661
0.015
0.764
0.017
0.691
2.277
48.130
Max
0.190
0.119
0.068
4295.500
0.845
0.301
24.741
0.053
1.463
0.128
2.220
8.738
56.063
predict a higher likelihood for social failures, while high write-offs on loans indeed do. Therefore, there is weak evidence in
favor of H1, but not in the obviously expectable manner. However, it should be stated that the fact that an MFI acts on a
for-profit basis, no matter how profitable the MFI really is, positively interrelates with the probability of a failure.
Regarding H2 (depth and quality of outreach), we also find weak evidence supporting this hypothesis, as the percentage
of female loan officers and the personnel expenses per assets, which both proxy friendliness and care with respect to the
customers, are negatively related with the probability of failure. However, the coefficients of the depth of outreach, namely
the percentage of female borrowers and the average outstanding loan balance, which is an inverse measure as smaller loans
are considered more social, are not or only significant to a little extent. However, as the rating agencies will, amongst other
assessment procedures, also have a look at these variables, this finding is not particularly surprising since these variables
can be assumed to be positively autocorrelated over time.
The hypothesis on donations (H3) is confirmed. However, the first impression of a strong significance from model (3) is
relativized by model (4) as the coefficient here is only significant on the 10% level. Even if the causation can be twofold,
either the notion of donors forcing MFIs to be more social or the notion of social MFIs attracting more donors, the implications are important as this means that from the presence of donations one can predict a lower likelihood of failure.
Also, the evidence on H4 (regulation) and H5 (growth) is clearly in favor of the hypotheses, as both coefficients are
highly significant in model (3) and in model (4). Concerning regulation, a reverse causality is to be excluded as regulation is
independent of the possible threat of a social failure. Thus, this provides mild evidence supporting the fact that regulation
can help prevent social failures. Also, the finding on the coefficient of NBdev is highly relevant as it shows that the mere
increase of the number of borrowers predicts social failure. The causation is, however, not so clear, as it could be the
abandonment of the social goals that has led to the growth strategy in order to increase profits as well as wild growth
rates leading to a significantly worsening social performance. In any case, regulators, donors and investors need to examine
quickly growing MFIs very closely.
Regarding the control variables, we can derive some interesting additional effects. The deposits variable is significantly
positive, implying that the more deposits an MFI has, the more likely it is to fail socially. This, however, could also be an
effect of the MFI type, which we do not consider in the analysis directly, as those MFIs that are not allowed to take deposits
have a zero value in this variable. The significantly positive coefficient of the Gini index can be interpreted as being a sign
that in those countries with high inequality, MFIs are more threatened to fail. Whether this is an effect that has to do with
88
G. Dorfleitner et al. / Finance Research Letters 22 (2017) 81–89
Table 5
Coefficients of the probit models. The dependent variable is the social failure dummy. The regression analysis is performed using Eicker–Huber–White heteroskedastic-consistent errors. Standard errors are in parentheses. The symbols ∗ , ∗ ∗ , and ∗ ∗ ∗ express significance at the 10%, 5%, and 1% level, respectively. The variables are defined in Table 2.
(1)
H1: Financial performance
ROA
WOR
(2)
5.634∗∗
(2.548)
11.659∗∗
(5.369)
H2: Depth and quality of outreach
Average outstanding balance (in thousand)
Percent of female loan officers
Personnel expense/assets
H3–H5: Institutional variables
Donations/assets
Regulation (dummy)
NBdev
log(avgGLP)2
Deposits/loans
For-profit (dummy)
Macro-economic control variables
Region_LAC (dummy)
GDPpc (in thousand USD)
Gini index
Constant
Observations
Pseudo R2
3.224
(2.224)
−0.114
(0.073)
1.012∗∗
(0.478)
0.855∗∗
(0.354)
5.270∗∗
(2.548)
−0.184∗ ∗
(0.084)
1.171∗∗
(0.552)
0.795∗
(0.410)
−1.058∗ ∗
(0.523)
−0.042
(0.091)
0.066∗∗
(0.029)
−27.513
(17.313)
−1.440∗ ∗ ∗
(0.547)
0.023
(0.150)
0.111∗∗∗
(0.037)
−43.603∗∗
(19.643)
215
0.317
(4)
2.679
(2.746)
22.667∗∗
(9.874)
0.382∗
(0.197)
0.997
(0.886)
−2.671∗ ∗ ∗
(0.993)
−6.664∗ ∗
(2.819)
WB
MFI-specific control variables
log(avgGLP)
(3)
215
0.389
0.399∗
(0.205)
0.276
(1.091)
−3.823∗ ∗ ∗
(1.467)
−10.825∗ ∗
(4.777)
−19.671∗ ∗ ∗
(6.353)
−1.454∗ ∗ ∗
(0.436)
1.138∗∗
(0.530)
−19.167∗
(10.112)
−1.507∗ ∗ ∗
(0.448)
1.635∗∗∗
(0.595)
2.022
(2.775)
−0.082
(0.092)
1.529∗∗∗
(0.538)
1.348∗∗∗
(0.455)
7.277
(5.911)
−0.257
(0.193)
1.741∗∗∗
(0.607)
1.266∗∗
(0.523)
−1.489∗ ∗
(0.660)
−0.072
(0.091)
0.066∗
(0.035)
−15.518
(21.222)
−2.122∗ ∗
(0.923)
−0.100
(0.138)
0.151∗∗∗
(0.053)
−57.539
(45.943)
215
0.423
215
0.567
the over-indebtedness phenomenon discussed above or with general unfairness in society or with other variables, cannot be
answered by using our data but would be an interesting question for future research.
5. Conclusion
In this article we consider the social failure of MFIs, which we define as being a fatal form of mission drift. As MFIs can
generally be assumed to pursue a social mission, which enables them to have better access to resources such as subsidized
debt, donations or volunteers who support the workforce, their steady pursuit of the social mission is of vivid interest to
stakeholders. The latter also holds true for the event of a social failure, which is something these stakeholders wish to
avoid in any case. It turns out that variables indicating a high likelihood of a subsequent social failure are a high writeoff ratio (related to the microloan portfolio) and a high growth rate of the MFI in terms of the number of borrowers.
Moreover, the percentage of female loan officers, the personnel expenses per assets, the volume of donations and the fact
that an MFI is supervised by a regulatory authority are negative predictors of the failure probability in the sense that the
higher the value the lower the probability. It needs to be stressed that the identified relations are not necessarily causal.
Nevertheless, as the results can be used as an early warning system, some practical applications and policy implications
are straight-forward. Special caution is advised if MFIs grow rapidly or have a high write-off ratio. Also, unregulated forprofit MFIs should be observed closely. Moreover, the extensive presence of donations or of female loan officers may play
a mitigating role with respect to the risk of a social failure. Even if our study provides measurable insights which may
serve as a starting point for more intensive studies, we have to point to the fact that it has its limitations. First, as we use
G. Dorfleitner et al. / Finance Research Letters 22 (2017) 81–89
89
only (socially) rated MFIs, the results may not be representative for non-rated MFIs. However, we observe that the ranges
our variables take are comparable with those that are typical for MFIs reporting to the MIX Market platform with one
exception, namely the under-representation of MFIs from South Asia. Thus, second, our results are also not representative
for this region. With these limitations, a further research agenda emerges quite naturally: One should try to generalize the
failure definition to non-rated MFIs (e.g. by drawing on variables that measure the social mission directly) and could then
explore our hypotheses in a much larger sample and challenge our findings. Additionally, it appears necessary to dig deeper
into the evidence regarding our hypotheses by establishing which causal relationships prevail.
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