Remer and Kattilakoski International Journal of Corporate Social Responsibility

(2021) 6:5
International Journal of
https://doi.org/10.1186/s40991-021-00059-5 Corporate Social Responsibility

ORIGINAL ARTICLE Open Access

Microfinance institutions’ operational self-

sufficiency in sub-Saharan Africa: empirical
evidence
Laxmi Remer* and Hanna Kattilakoski

Abstract
The topic of financial sustainability in microfinance institutions has become more important as an increasing
number of Microfinance Institutions (MFIs) seek operational self-sufficiency, which translates into financial
sustainability. This study aims to identify factors that drive operational self-sufficiency in microfinance institutions. To
accomplish this, 416 MFIs in sub-Saharan Africa are studied and several drivers for operational self-sufficiency are
empirically analyzed. Results indicate that these drivers are return on assets, and the ratios total expenses/assets and
financial revenues/assets. The results imply that MFIs should encourage cost-management measures. They also
reveal that there may not be a significant tradeoff in self-sufficiency and outreach. These findings will enable
microfinance institutions worldwide to sharpen their institutional capabilities to achieve operational self-sufficiency
and also provide policymakers with more focused tools to assist industry development.
Keywords: MFI, Microfinance institution, Operational self-sufficiency, Financial sustainability, Outreach, Financial
inclusion, Africa

Introduction By definition, an MFI has a dual objective: to cover its

Microfinance institutions (MFIs) provide small scale costs (self-sufficiency) and to reach many poor borrowers
loans to poor, low income people and communities, who (outreach) (Hartarska & Nadolnyak, 2007). Financial
are considered un-bankable. Microfinance, often referred sustainability in microfinance organizations is paramount
to as microcredit, has made deep inroads into sustainable because it enables them to achieve, both, their long-term
finance since Muhammed Yunus set up the Grameen and short-term goals. According to Bayar (2013), the total
Bank in 1983 to bank the un-bankable. With about 130 demand in microfinance markets is about 500 million
million clients, the microfinance market is already fairly people, indicating a large unmet demand and potential for
established and is predicted to grow at an estimated com- further growth within the microfinance industry. Overall,
pound annual growth rate (CAGR) of more than 15% by penetration rates are low, ranging from 0.5% in Eastern
2020 (Technavio, 2016). However, although many MFIs Europe and Central Asia to about 2.5% in South Asia
have shown great success in outreach, “millions of low (Gonzalez & Rosenberg, 2006). To meet this need, MFIs
income individuals in developing countries still lack access are promoting financial inclusion through provision of
to financial services” (Bogan, 2012). Since they are finan- financial services to the poor (Chikalipah, 2017). Unfortu-
cially unsustainable, many MFIs are financed mostly nately, the industry situation is such that there is an over-
through donations or subsidies which provide funds that whelming number of unprofitable MFIs, which serve
allow them to continue operating (Quayes, 2012). about 56% of all micro-borrowers. This number does not
even include the extremely small MFIs that do not report
financial information and are likely to be unprofitable
* Correspondence: l.remer@cbs.de (Gonzalez & Rosenberg, 2006).
CBS International Business School, Cologne, Germany

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Remer and Kattilakoski International Journal of Corporate Social Responsibility (2021) 6:5 Page 2 of 12

MFIs remain subsidized because of their importance MFI rather than taking an investor’s perspective. Finally, it
to poverty alleviation. There is a well acknowledged uses the most exhaustive and large database from 2000 to
tradeoff in the industry between sustainability and 2017, from sub-Saharan Africa (SSA).
outreach. The tradeoff assumes that if an MFI focuses The rest of the paper is structured as follows: section
on financial sustainability, their outreach will be com- two is literature review leading into theoretical frame-
promised, since they will likely have to increase interest work and hypotheses, section three discusses data and
rates to compensate for higher operating expenses. Like- methodology used which in-turn is followed by sections
wise, if an MFI focuses solely on financial inclusion and 4 and 5 with results and conclusions respectively.
outreach, they are likely to be unprofitable because they
are unable to cover the excessive costs of reaching the Literature review
extremely poor. Nature of lending in MFIs
However, financial sustainability has recently become Microfinance has emerged as a feasible financial alterna-
more important for the microfinance industry with the tive for poor people with no access to credit from formal
MFIs seeing improving profitability over time. With this financial institutions. Its objectives include poverty alle-
increasing profitability within the microfinance industry, viation by fostering small scale entrepreneurship through
new players are emerging and entering the market. The simple access to credit. It distinguishes itself from formal
expansion of the microfinance industry is likely to credit by disbursing small loans to the poor, using vari-
continue to meet the demand for about 250 million cus- ous innovative non-traditional loan configurations such
tomers in the future (Bayar, 2013). as loans without collateral, group lending, progressive
In this paper, operational self-sufficiency (OSS) is used loan structure, immediate repayment arrangements,
to proxy for financial sustainability and is at times used regular repayment schedules and collateral substitutes
interchangeably. OSS would allow an MFI freedom from (Quayes, 2012).
subsidies and the opportunity to continue outreach to The concept of small-scale lending extends beyond
populations denied financial services whilst being profit- just typical institutions, and it includes several types of
able. Financial sustainability or OSS is crucially import- lending, many that are mostly through informal organi-
ant for the long-term self-sufficiency of an MFI thereby zations. Additionally, informal groups create a more
aiding poverty alleviation. competitive landscape for microfinance institutions.
The aim of this paper to draw attention to the impacts Other types of microfinance lending are essentially cen-
and consequences of efficient operations and cost man- tered around group lending and group saving.
agement measures in the context of MFIs. This in turn, Group lending is a common type of microfinance loan
will allow them to achieve operational and financial self- where the group represents a borrower (Zuru, Hashim,
sufficiency. An added aim of this paper is also to provide & Arshad, 2016). The loan is disbursed to a group and
policymakers with more focused tools to assist in indus- members of the group, usually four to ten individuals,
try development. are responsible for the repayment of the loan (Chetty,
A detailed and in-depth study of the existing literature 2017) Members of such groups mostly include farmers,
has identified several un-researched gaps, and based on labourers, tenants and other rural workers. For a loan
these, this paper offers multiple contributions to research. provider, this often minimizes risk of lending, as the
It uses OSS, a less studied, key indicator of financial sus- basic idea is that individual risks are overcome by the
tainability to proxy for long-term financial sustainability. collective responsibility and security granted by a group
OSS is the MFI’s ability to cover its costs through operat- (Grameen Bank, 2018). Group lending focuses on social
ing income (MIX Market, 2018). The only study that capital, which promotes social interaction, information
comes close in the recent years is by Chikalipah (2017), sharing and trust. These factors are all foundations of
where the dependent variable is explicitly financial sus- group lending methodology (Kamukama & Natamba,
tainability and not OSS. Moreover, Chikalipah (2017) uses 2013). Mostly, these formation types do not require
GMM methodology to deal with endogeneity of data, financial administration. A challenge faced by group
which is most often an ‘unfixable’ limitation. This paper lending is personal preferences in lending credit. Group
also establishes significant causal drivers of MFI OSS lending can also be referred to as joint liability groups.
using regression analysis whilst attempting to shed light Many of the informal groups are extremely similar in
on the tradeoff theory. According to Hermes & Lensink, their practices. However, the interesting feature in all of
2007, “although this issue is the subject of a heated debate, them is that they focus heavily on groups. Additionally,
there is a lack of systematic empirical analyses on the most groups have self-selected members, which means
nature and determinants of the trade-off.” This paper also that members are admitted into the groups based on
establishes key differences between the various types of their relationships with peers. Individuals are unlikely to
MFIs and focuses on the long-term self-sufficiency of an recommend someone for a group if they know that the
Remer and Kattilakoski International Journal of Corporate Social Responsibility (2021) 6:5 Page 3 of 12

individual is unlikely to pay back their loans or if they use a wider array of financial resources to conduct busi-
know the individual is dishonest or immoral. Thus, these ness, such as borrowing from banks or through capital
groups utilize their knowledge of a person’s character to markets (Gibbons & Meehan, 1999). Financial sustain-
make decisions on admittance into a group as a form of ability through OSS has recently become centerstage for
risk management. the microfinance industry and MFIs have seen improv-
ing profitability over time.
Different types of MFIs However, meeting the needs of an underserved market
Microfinance institutions have a range of different legal is rather expensive. Especially in the rural areas of SSA,
standings. There is a variety in the types of formal and where operating costs can run high and capital con-
semiformal institutions within the market including coop- straints limit outreach (Bogan, 2012). Without financial
eratives, credit unions, non-governmental organizations sustainability, reaching the long-term goal of aiding pov-
(NGOs), non-banking financial institutions (NBFIs), rural erty alleviation is more difficult since MFIs depend on
banks, postal banks, and commercial banks (Daher & Le third parties for funding (Daher & Le Saout, 2013;
Saout, 2013). The legal statuses identified by the MIX Otero, 1999).
Market, the data source for this paper, include NGO, Third party funding, which can take the form of
NBFI, Banks, Rural Banks and Credit Unions/Coopera- subsidies, is common practice within the microfinance
tives as well as “other.” Research has shown that NGOs industry. Subsidies help cover the cost of funds and
account for less than a quarter of total borrowers; most administration, which in-turn increase the outreach an
microfinance is provided by governments, such as state- organization can have (Hudon & Traca, 2010). Subsidies
owned institutions or self-help groups that are financed by are important in that they allow an MFI to conduct busi-
state banks. About a sixth of borrowers are served by ness regardless of whether it is financially self-sufficient
private banks and finance companies (Gonzalez & or not. Additionally, MFIs can then offer borrowers
Rosenberg, 2006). more affordable lower interest rates. Subsidies are par-
The different forms of institutions operate in diverse ticularly important in more remote areas, where individ-
ways. For example, NGOs tend to make smaller loans, uals are harder to reach, and thus, administrative costs
which are substantially costlier per dollar lent, and thus of loans and doing business are higher.
require higher interest rates, than microfinance providers However, research has found that the (higher) inten-
chartered as banks or NBFIs. NGO microfinance institu- sity of subsidies is associated with (lower) sustainability
tions also lend substantially higher shares of their portfo- (Hudon & Traca, 2010). As MFIs receive more funding,
lios to women (Cull, Demirguc-Kunt, & Morduch, 2016). they are less dependent on the success of their own op-
Many MFIs are not only involved in lending, but they erations. Moreover, although MFIs may claim that they
offer additional services such as bank accounts and in- are profitable, they may still use subsidies to cover costs
surance products, whilst also providing financial and (De Aghion & Morduch, 2004; Hudon & Traca, 2010).
business literacy. Some might offer additional sources According to Quayes (2012), initially, it was expected
such as savings accounts, insurance, health care and that MFIs would wean themselves off donor subsidies
personal development, making the scope of MFI’s work and achieve self-sufficiency as the rate of recovery of
go beyond only financial matters (Jha, 2016). In principle, loans increased, but other research contends that the
MFIs try to build a unique atmosphere of financial inclu- high rate of recovery in the microcredit industry has
sion intertwined with a sustainable livelihood aimed at failed to transform the donor-dependent MFIs into inde-
empowering poor communities. Many MFIs are also in- pendent self-sustaining organizations. Interestingly,
volved in several social development initiatives such as Nawaz (2010) states that financial performance of MFIs
capacity-building, education, financial literacy, water and is seen to significantly decline without the use of subsid-
sanitation, livelihood promotion, preventative healthcare ies. It is clear that MFIs need to, both, sustain them-
and training (Jha, 2016). This is in line with the MFI goal selves and increase their outreach to the poor and
of reducing poverty by giving poor the resources needed unbanked population.
for them to become self-sufficient whilst remaining finan-
cially self-sufficient themselves. Theoretical framework
The tradeoff theory
OSS in the context of MFIs As mentioned before, given an MFI’s dual objective of
MFIs are critical in meeting the needs of an underserved self-sufficiency and outreach (Hartarska & Nadolnyak,
market. OSS in an MFI, not unlike any other business, is 2007), the “trade-off” theory states that financial inclu-
important because it allows the MFI to sustain itself sion (outreach) keeps the interest rates of MFIs low
both in the short and long run whilst delivering on its given scale impacts. Most literature is in agreement that
commitment. Financially self-sufficient MFIs are able to there are two extremes: the poverty/outreach approach
Remer and Kattilakoski International Journal of Corporate Social Responsibility (2021) 6:5 Page 4 of 12

and the self-sustainability approach (Schreiner, 2002). borrowers are more susceptible and less capable of
The poverty/outreach approach is directed towards dealing with economic volatility (Quayes, 2012). MFIs
improving the standard of living of poor individuals and that reach the poorest clients have the highest costs and
focuses on the impact of the MFI on individuals within a a smaller volume of operations (Lafourcade, Isern,
community. The success of an MFI with the poverty Mwangi, & Brown, 2005). As a result, increased depth of
approach is measured based on how well it fulfills the outreach comes at a higher service and administrative
needs of the poorest individuals in short-term. Mostly, cost, which results in worsening financial performance.
donations fund these typically non-profit MFIs and there There is however, statistical evidence which shows that
is a significant dependence on financial help from third financially self-sufficient MFIs have better outreach than
parties. MFIs that are not self-sufficient (Quayes, 2012). This in-
On the other hand, the self-sustainability approach formation is significant in that, financial sustainability
focuses more on a formal financial system where success should be a prerequisite for all MFIs in order for them
is measured through profitability. In this approach, to have the best outreach within communities. Though
donations cover start-up costs and fund innovation most research agrees that there is a tradeoff between fi-
experiments (Schreiner, 2002). Given these innovations, nancial self-sustainability and outreach, this information
in the long-term, client revenue covers costs. More re- proves that the tradeoff can be overcome. An operation-
cently, MFIs have adopted a for-profit business model ally and therefore financially sustainable microfinance
instead of a non-profit (see changes in Grameen Bank, organization should ideally be able to maintain self-
2018 and Chikalipah, 2017). However, there is also the sufficiency while keeping interest rates and operating
apprehension about financial self-sufficiency, in that, it costs low to successfully cater to the poor, especially in
may adversely affect the social outreach mission of SSA.
accessing credit for the poor (Quayes, 2012) giving rise
to the “tradeoff” between outreach and self-sufficiency. Microfinance in sub-Saharan Africa (SSA)
Despite this, there seems to be a trend towards im- The need for microfinance as a means for poverty allevi-
proving financial sustainability as evidenced by data ation is evident in SSA, where the number of poor has
from 2001 and 2004 which shows that based on the increased from 280 million to 330 million since 1990 to
borrowers served, profitability has increased from 53% 2012 (The World Bank, 2016). Extreme poverty remains a
to 64% respectively (Gonzalez & Rosenberg, 2006). The challenge especially within the SSA region. As a perspec-
emphasis on financial performance is also important tive, the world average for the population in poverty is
because donor agencies have a vested interest in the 10.7% and in Sub-Saharan Africa, the average is 43% (The
efficient utilization of funds allocated. World Bank, 2016). Three quarters of the adult population
In order for an MFI to have wider outreach and to here lack access to formal banking services. In 2014, only
maintain financial sustainability, they must charge higher 16% of adults had any type of formal savings and only 6%
interest and incur higher costs of disbursing loans participated in formal borrowing (The World Bank, 2014)
(Quayes, 2012). Unlike traditional development banks, as compared to almost 89% adults with accounts in formal
MFIs use many innovative lending methods and charge banks in high income economies (Bayar, 2013).
market-based interest rates to compensate for the higher Microfinance in this region faces its own, unique chal-
costs associated with conducting this type of business lenges. Very low population densities throughout Africa
(Hartarska & Nadolnyak, 2007). The interest rates cover only increase the already high operating expenses. Deliv-
the cost of screening, monitoring and enforcing loans. ering microfinance services to extremely rural areas is
According to Bogan (2012), as protection from default, very expensive because the distance between clients is
MFIs have charged nominal interest rates of 30% to 60%. physically vast and financial transactions are likely to be
Ayayi and Sene (2010) show that MFIs with the highest impractically small.
interest rates are the best performers, the most efficient The overall financial infrastructure in Africa is also
and the most financially sustainable organizations. lacking, and a shortage of strong managers has caused
However, while these interest rates may cover operating major sub-regions in Africa to agree that staff shortages
expenses, they are not ideal for expanding outreach, are holding back their growth and service improvements
since the financial services being offered become rela- (Ashcroft, 2008). Africa’s small private sector is domi-
tively unaffordable to the poor. nated by small enterprises that engage in largely infor-
Typically, by extending more credit services to the mal activities, their growth hampered by limited access
poor, a larger number of small loans will be adminis- to formal financial services, such as deposit, credit facil-
tered which could translate as higher cost per loan. Also, ities and other financial services. Only about 15% of
there is an increased risk in this sort of an outreach small-medium enterprises in Africa have access to these
because there is a higher chance of default since poor services (United Nations OSAA, n.d.).
Remer and Kattilakoski International Journal of Corporate Social Responsibility (2021) 6:5 Page 5 of 12

Many MFIs in SSA underperform and struggle to re- increases in the number of borrowers, higher interest
main in business (Chikalipah, 2017). In a study covering rates and higher return on assets. He also shows that
Africa, East Asia, Eastern Europe, Latin America, the increases in deposits decrease financial sustainability
Middle East and South Asia for the years 2003 and 2006, and macroeconomic variables apart from GDP growth
Africa had the highest percentage of unsustainable MFIs (significant at 10%) do not impact financial sustain-
(38.02%), the highest percentage of portfolio at risk ability of the MFIs. We, therefore, do not include
(7.03%), and the lowest average return on assets (0.38%) macroeconomic variables in our analysis to preserve
(Bogan, 2012). In a region where poverty is prominent the brevity of the model.
and omnipresent, financially sustainable MFIs are espe- Bogan (2012) shows that the size of an MFI’s assets
cially important to continue to help the poor. and an MFI’s capital structure are associated with per-
All of the above makes SSA an apt region to focus on. formance, both in terms of sustainability and outreach.
Additionally, a study by Hartarska and Nadolnyak (2007)
Hypotheses showed that less leveraged MFIs have better OSS show-
Based on the evidence presented above and according to ing donors’ willingness to provide equity to MFIs that
the concept of social entrepreneurship, a business should do well and extend loans to those that slack off – a clas-
be able to be profitable and serve a social need (Martin sic phenomenon witnessed in regular corporate finance.
& Osberg, 2007). Despite the tradeoff theory, which Quayes (2012) showed that financial performance has a
implies that there is a tradeoff between financial self- positive impact on the depth of outreach which increases
sufficiency and outreach, higher outreach also implies the probability of attaining financial sustainability. Add-
scale effects thereby reducing cost per borrower. It is itionally, “the depth of outreach is positively affected by
therefore likely to be a strategy followed by for-profit financial sustainability, and firms which are OSS have a
MFIs giving rise to our first and second hypotheses, smaller average loan size than firms which are not”
namely, (Quayes, 2012).
H1: For-profit MFIs have higher outreach as compared Schäfer and Fukasawa (2011) suggest that an MFI can
to non-profit MFIs and in the same vein. expand its outreach and serve more customers which
H2: Higher OSS results in increased outreach. results in higher financial stability and OSS. The more
For-profit MFIs are likely to be more focused and effi- borrowers an MFI has, the more they can leverage econ-
cient in terms of their operations as compared to the omies of scale and scope, thereby reducing total cost per
subsidized non-profit MFIs, thereby giving us the third borrower. They also found that less revenue is associated
and fourth hypotheses, namely, with reduced OSS.
H3: For-profit and non-profit MFI’s OSS is driven by Given the above, this paper uses the following
dissimilar factors. variables (some as obvious ratios) to test the aforemen-
and tioned hypotheses. The definitions are sourced from
H4: OSS and Non-OSS MFIs are driven by dissimilar MIX Market (2018):
factors. OSS (Non-OSS): is an indicator of financial perform-
Dissimilar factors here capture the differences in the ance of an MFI (Hartarska & Nadolnyak, 2007), and
operational functioning of “for-” and “non-profit” MFIs. measures its ability to (not) cover its costs through oper-
We distinguish between for- and non-profit MFIs and also ating income (MIX Market, 2018). The widely accepted
between operationally self-sufficient and non-operationally formula, which this paper also uses, is:
self-sufficient MFIs. This is done so as to have clearer
results and also to establish the logic that operational self- Financial Revenue
OSS ¼
sufficiency promotes financial self-sufficiency and outreach. ðFinancial expense þ Net impairment þ Operating ExpensesÞ

Data and methodology where financial expense is the expense on funding liabil-
Data is collected from MIX Market created by the Microfi- ities and net impairment is the loss on gross loan portfo-
nance Information Exchange. A web-based platform, MIX lio (MIX Market, 2018). OSS is a percentage, whereby
Market is widely used because of their extensive standard- over 100% indicates self-sufficiency (less than 100% indi-
ized financial and outreach information on MFIs. The data cates no operational self-sufficiency).
gathered by the MIX is standardized according to the ROA: return on assets is the ratio of net operating in-
microfinance industry reporting standards, which are come (less taxes) and average assets, a classic measure of
aligned with the International Financial Reporting Stan- profitability.
dards (Global Impact Investing Network, 2018). ROE: return on equity is the ratio of net operating in-
According to Chikalipah (2017), factors that improve come (less taxes) and average equity, showing investor
financial sustainability include, decreased costs/borrower, perspective.
Remer and Kattilakoski International Journal of Corporate Social Responsibility (2021) 6:5 Page 6 of 12

Assets: is the total value of resources controlled by the To ensure data reliability, only the MFIs with diamond
MFI as a result of past events and from which future levels above 3 (inclusive) and with no missing informa-
economic benefits are expected to flow to the institution. tion were selected for further analysis giving us a final
For calculation purposes, assets are the sum of each in- 416 unique MFIs with 1703 rows of data.
dividual asset account listed. These MFIs were then sorted into categories: for-
Active borrowers: is the number of individuals who profit or non-profit and OSS, NOSS (non-OSS), or
currently have an outstanding loan balance with the UPNOSS (underperforming NOSS) MFIs. The OSS,
MFI. NOSS and UPNOSS categories are based on the OSS ra-
Average loan balance: is the ratio of gross loan portfo- tio, namely, OSS > 100% is operationally self-sufficient,
lio to number of active borrowers. OSS ratio < 100% is non-operationally self-sufficient,
Cost per borrower: is the ratio of operating expenses to UPNOSS is the subset of MFIs with OSS ratio below
average number of active borrowers. 80% and for- and non-profit MFIs is based on the legal
Loan portfolio: includes (average) gross loan portfolio, status declared by the MFI. Once again, we distinguish
number of loans outstanding, number of loans dis- between the for- and non-profit MFIs, because based on
bursed, fees and commission income on loan portfolio, theory, a non-profit organization should be more fo-
interest income on loan portfolio. cused on outreach and less on financial sustainability.
Financial revenue: is revenue from the loan portfolio OLS regression method is then applied to the data to
and other financial assets. identify causal factors for OSS in SSA.
Total Expense: The sum of financial expenses, impair-
ment loss and operating expenses. Results
Outreach: is number of people an MFI extends credit Figure 1 below shows that, of 416 MFIs, 57% were OSS,
to, or number of borrowers over a specific period of while 43% were NOSS. This is very interesting because
time (Quayes, 2012). The MIX Market classifies out- there is a higher percentage of self-sufficient MFIs than
reach as small, medium or large, where: would have been expected as suggested by literature.

 Small: Less than 10,000 borrowers Pie chart based on own analysis; data - mix market 2018
 Medium: borrowers between 10,000 and 30,000 Moreover, from Table 1 below, when comparing the
 Large: Greater than 30,000 borrowers outreach of MFIs, the OSS organizations actually report
a larger outreach than the NOSS organizations. I.e. 26%
Methodology of the OSS MFIs reported larger outreach as compared
All available information on the above-mentioned vari- to 11% of the NOSS MFIs. In fact, 51% of OSS MFIs
ables for the SSA MFIs, for the period from 2000 to report medium to large outreach as compared to 30% by
2017, were downloaded from the MIX Market (2018). NOSS MFIs. This implies that OSS MFIs, contrary to
This resulted in 856 unique individual MFIs with 4124 the tradeoff theory, show relatively higher outreach than
rows of data. Given the voluntary nature of disclosure, NOSS.
some MFIs have more information than others.
Each MFI is assigned a diamond rating by the MIX
Market (2017). This rating system represents the level of
disclosure; the higher the level of disclosure, the higher
the number of diamonds. The currency is US dollar:

 Level 1: General information.

 Level 2: Level 1 and outreach data (at minimum,
data for two consecutive years).
 Level 3: Levels 1–2 and financial data (at minimum,
data for two consecutive years).
 Level 4: Levels 1–3 and audited financial statements
(at minimum, audited financial statements including
auditors’ opinion and notes for at least two
consecutive years).
 Level 5: Levels 1–4 and rating or other due diligence
report (at minimum, ratings/evaluation, due
diligence and other benchmarking assessment
Fig. 1 Comparison of OSS
reports or studies for one of the 2 years reported).
Remer and Kattilakoski International Journal of Corporate Social Responsibility (2021) 6:5 Page 7 of 12

Table 1 Comparison of Outreach amongst different types of correlation coefficients are above 0.70 (Baltagi, 2008;
MFIs Hsiao, 2014).
OUTREACH All Data OSS NOSS UPOSS For-Profit Non-Profit
Small 55% 49% 65% 72% 49% 60%
Correlation matrix indicating no multicollinearity for
Medium 25% 25% 25% 22% 29% 22% independent variables for 416 SSA MFIs. Pearson’s
Large 20% 26% 11% 6% 22% 18% probability in parenthesis
Figures based on own analysis; data - Mix Market, 2018. OSS Operational Self- Table 3 below presents the descriptive statistics for the
sufficiency, NOSS Non-OSS, or UPNOSS Underperforming NOSS. Each variables that proxy as drivers for OSS, for the 416 MFIs.
percentage is a ratio of the number of MFIs with a specific outreach to the
total number of MFIs. For e.g., in NOSS MFIs column, the number of “small” The data spans 18 years wherein the average OSS ratio is
outreach organizations was totaled and divided by the total number of MFIs 106% with a minimum of 2% and a maximum of 1938%.
giving 65%. Outreach: Small- Less than 10,000 borrowers, Medium- Number of
borrowers is between 10,000 and 30,000, Large- Greater than ROA averages at a − 3% and ROE at − 63%, both indicat-
30,000 borrowers ing issues with profitability. The average loan balance
per borrower has been US$598 whilst cost/borrower has
averaged at US$188. The average number of active bor-
From Fig. 2 below, it is clear that a relatively higher
rowers for this period has been 29,299 borrowers with a
percentage, i.e. 65% of for-profit MFIs show OSS whilst
minimum of 2 and maximum of 801,809 borrowers.
52% of the non-profit MFIs show OSS. This is in line
Table 3 also shows that the max and min are really ex-
with the theory that non-profit MFIs, which are either
treme implying a high degree of volatility in this industry
credit unions/co-operatives or NGOs and are heavily
– a fact also reiterated by rather high standard
subsidized, are more focused on other aspects of micro-
deviations.
finance than OSS (Quayes, 2012).

Pie chart based on own analysis; data from mix market 2018 Data from MIX market 2018 for 416 SSA MFIs
When comparing outreach for the for-profit and non- Table 4 below presents OLS regression results con-
profit MFIs, the results indicate that for-profit MFIs ducted on the OSS and NOSS MFIs to determine causal
have a slightly higher outreach, as indicated in Table 1 factors. Model 1 includes all data for the 416 MFIs and
above. We see that 22% of for-profit MFIs have a larger is provided only for comparative basis. Model 2 includes
outreach as compared to 18% in non-profit MFIs. This data only for OSS institutions (OSS > 100%), while
also therefore challenges the theory that for-profit MFIs Model 3 includes data only for NOSS institutions (OSS <
are not as focused on reaching poor clients. In summary, 100%). Model 4 for UPNOSS MFIs is where OSS < 80%,
for- and non-profit MFIs have dissimilar OSS and because this isolates organizations that are truly NOSS.
outreach. Models 5 and 6 are grouped based on their declared
profit orientation. For sake of brevity, whilst we present
Regression analysis the conventional significance levels of 1% (highly signifi-
Before beginning with regression analysis, we check for cant), 5% (significant) and 10%, we only discuss the
the presence of multicollinearity. Table 2 below shows significances of 1% and 5%.
that there is no serious multicollinearity in the chosen A key finding is the set of shared commonalities
variables, (barring Return on Equity), since none of the amongst the different types of MFIs, namely:

Fig. 2 OSS in For-Profit vs Non-Profit MFIs

Remer and Kattilakoski International Journal of Corporate Social Responsibility (2021) 6:5 Page 8 of 12

Table 2 Pairwise correlations

Return on Return on Average loan Cost per Total expense Number of Number Financial
assets equity balance per borrower / assets Active Borrowers/ of active Revenue/
borrower Financial Revenue borrowers Assets
Return on assets 1
Return on equity 0.07 (0.008) 1
Average loan balance 0.08 (0.000) 0.012 (0.611) 1
per borrower
Cost per borrower −0.21 (0.000) 0.03 (0.264) 0.39 (0.000) 1
Total expense / assets −0.63 (0.000) −0.07 (0.004) − 0.17 (0.000) 0.13 (0.000) 1
Number of Active −0.08 (0.001) −0.0003 (0.99) − 0.11 (0.000) −0.09 (0.000) − 0.04 (0.110) 1
Borrowers/ Financial
Revenue
Number of active 0.14 (0.000) 0.01 (0.672) −0.03 (0.297) −0.06 (0.010) − 0.11 (0.000) −0.003 (0.902) 1
borrowers
Financial Revenue/ 0.10 (0.000) −0.01 (0.782) −0.08 (0.001) − 0.03 (0.307) 0.41 (0.000) − 0.08 (0.002) −0.01 (0.765) 1
Assets

1. The intercept is positive and highly significant in all are relatively better at OSS as compared to non-profit
the models. The coefficients are higher for OSS and MFIs.
for-profit MFIs relative to NOSS, UPNOSS and
non-profit MFIs. ROA
2. ROA is positive and consistently highly significant The results reveal that, for all the MFIs, irrespective of
in all the models as a causal driver for OSS. The their distinct constructive differences, ROA is the most
coefficient is the biggest for OSS MFIs. significant driver for OSS. Our finding supports that of
3. ROE is not significant in any of the models. Chikalipah (2017) who found that ROA is the major
4. Ratio Financial Revenue/Assets is positive and determinant of financial sustainability in the SSA MFIs.
consistently significant in all models, where it is The implication is that MFIs, in general, efficiently
highly significant for OSS MFIs. generate a higher return from their asset portfolios (the
loans they give out), which translates into increased OSS
which in-turn translates into financial sustainability. The
Discussion
coefficient for OSS MFIs is the biggest once again reiter-
Our results confirm findings of previous work whilst
ating efficient return generation.
establishing new results.
Financial revenue/assets
Intercept The OSS MFIs have a highly significant positive Finan-
Here, whilst the result is as expected for OSS and NOSS cial Revenue/Assets ratio which ties in neatly with the
MFIs, (given their definition and construction), in case significant ROA. This also implies that despite using a
of for- and non-profit MFIs, the larger coefficient for the subset of all income by focusing only on financial reve-
former relative to the latter implies that for-profit MFIs nues, OSS MFIs are able to efficiently generate higher

Table 3 Descriptive statistics

Variable Mean Standard Deviation Minimum Maximum
Operational self-sufficiency 106% 65% 2% 1,94%
Return on assets −3% 14% −97% 83%
Return on equity −63% 2581% − 105,87% 8,66%
Average loan balance/borrower 598 1101 9 22,25
Cost/borrower 188 456 0 12,185
Total expense/assets 28% 19% 3% 156%
Number of active borrowers/financial revenue 2% 8% 0% 249%
Number of active borrowers 29,299 79,785 3 801,81
Financial revenue/assets 24% 26% 1% 663%
Remer and Kattilakoski International Journal of Corporate Social Responsibility (2021) 6:5 Page 9 of 12

Table 4 Regression Analysis

Dependent Variable = Operational Self-Sufficiency (OSS) Period: 2000–2017
All Data OSS MFIs NOSS MFIs UP NOSS MFIs For-Profit MFIs Non-Profit MFIs
(Model 1) (Model 2) (Model 3) (Model 4) (Model 5) (Model 6)
R2 25.9% 18.7% 74.7% 73.9% 48.1% 21.3%
Independent Variables
Intercept 1.0877c (34.6150) 1.4782c (26.1593) 0.6841c (67.3453) 0.5654c (54.3554) 1.1927c (31.2353) 1.0566c (22.2142)
Return on Assets 2.1051c (14.5958) 4.1541c (11.0489) 1.5381c (24.7522) 1.3372c (17.7114) 1.9032c (12.2658) 2.2467c (10.5726)
Return on Equity −0.0002 (− 0.3891) − 0.013(− 0.2684) 6.1E-06 (0.0567) 1.34E-05 (0.1535) 0.0087(1.4216) − 0.0002 (− 0.2828)
Average loan balance 2.79E-06 (0.2009) −2.8E-05(− 1.2256) c
3.7E-05 (5.3438) 3.11E-06 (0.3005) −1.4E-06 (− 0.1120) 7.4E-06 (0.3114)
per borrower
Cost per borrower 1.5E-05 (0.4438) 4.21E-05 (0.3535) −1.5E-05b (− 1.9716) −5.5E-06 (− 0.8040) −1.4E-05 (− 0.4308) 1.88E-05 (0.2759)
Total expense/assets − 0.0789 (− 0.6821) −1.8014 (−8.5486) 0.4712 (8.9181)
c c c
0.5375 (8.1095) − 0.3655 (− 2.9646) 0.0668 (0.3934)
c

Number of Active − 0.3534a(− 1.9542) −0.5863 (− 0.6166) 1.04E-06c (− 5.9365) −0.1714c (− 5.4769) −1.4540c (− 2.9659) −0.3076 (− 1.4005)
Borrowers/Financial
Revenue
Number of active 6.67E-07c (3.8681) 3.02E-07 (1.4326) 1.04E-06c (4.9937) −8.68E-07c (2.9565) 7.21E-07c (6.6289) −1.5E-07 (−0.2129)
borrowers
Financial Revenue/ 0.1328b (2.0357) 0.2164c (2.6697) 0.1119b (2.2721) 0.1435b (2.1603) 0.1051a (1.7344) 0.1943a (1.9095)
Assets
Observations 1703 977 726 407 648 1055
t stats in parenthesis; a, b, c significant at 10%, 5% and 1% respectively.
The regression is split up both by OSS and by profit status. OSS (Operational Self-sufficiency), NOSS (non-OSS), or UPNOSS (underperforming NOSS). The OSS groups
(Models 2 to 4) are based on the MFI’s calculated OSS. Model 2 are MFIs with an OSS above 100%. Model 3 is NOSS, or MFIs with an OSS below 100%. UPNOSS group
(Model 4) are MFIs with an OSS below 80%. Models 5 and 6 are split by the declared profit status. NOSS- non-OSS MFIs, UPNOSS- underperforming OSS MFIs

returns from their loan portfolios. With a coefficient the coefficient is also indicative of the fact that OSS
nearly twice as that of NOSS and UPNOSS, the latter MFIs are much more efficient at reducing and managing
both being significant too, OSS MFIs are simply much their expenses even when compared to for-profit MFIs.
better at generating higher financial revenues per unit
asset. Average loan balance/borrower
Unlike Quayes (2012), this is not significant driver of
Cost/borrower OSS in any of the models but for NOSS MFIs. This
This finding does not support that of Chikalipah (2017) shows that when loan balance/borrower rises, NOSS
as it is not significant in any of the models except for rises which again confirms that NOSS MFIs are ineffi-
NOSS MFIs, where it carries a negative sign implying cient in cost management.
higher the cost/borrower, lower the NOSS which is
intuitive. However, it not being significant in any of the Number of active borrowers/financial revenue
other models suggests that it is not important for OSS. This ratio is not significant for OSS MFIs but is positive
This implies that outreach cannot be limited by geo- and highly significant for NOSS MFIs. This implies again
graphic remoteness which mostly tends to increase cost/ that when the ratio rises NOSS rises – which ties up
borrower. It also gives credence to common sense that with the cost management finding above that the NOSS
increasing outreach (all else remaining constant) despite MFIs are rather slack at managing their costs despite
geographic constraints increases scale which in-turn increasing number of active borrowers. The case of
lowers the impact of cost/borrower on OSS. UPNOSS MFIs is intuitive with the coefficient signaling
that these are barely surviving and are in desperate need
Total expenses/assets of overhaul of their business model. The negative signifi-
OSS and for-profit MFIs exhibit a negative Total Ex- cant sign on for-profit MFIs implies that the number of
penses/Assets ratio which means lower the ratio higher active borrowers is smaller as compared to financial rev-
the OSS. In other words, cost management measures enue thereby increasing OSS in for-profit MFIs. This in-
need to be undertaken to achieve OSS. This is also turn shows that for-profit MFIs are much more efficient
supported by the positive coefficient on the ratio for in terms of generating financial revenue from their active
NOSS and UPNOSS MFIs implying these MFIs do not borrowers. The fact that this variable is not significant at
control their expenses effectively. Moreover, the size of all for the OSS MFIs implies that they do not necessarily
Remer and Kattilakoski International Journal of Corporate Social Responsibility (2021) 6:5 Page 10 of 12

depend on each current individual borrower given their Theoretical implications

very efficient dealings with cost management. This also im- Findings indicate that the focus of an MFI should be on
plies there is no real tradeoff between outreach (as proxied cost management and efficient revenue generation form
by number of active borrowers) and OSS. We have already active borrowers. The structure of the MFI should be
shown in Table 1 that OSS MFIs have a larger outreach similar to any other company, where revenues must out-
relative to NOSS and combined with the regression result, weigh expenses. The results are in support of the profit-
it is clear that there is no tradeoff between outreach and incentive theory and the financial systems approach.
operational self-sufficiency. According to Bogan (2012) Whilst a causal relationship between OSS and outreach
operating costs can run high and capital constraints limit could not been proven, there is a clear positive one
the outreach, but our results prove otherwise. between for-profit MFIs and outreach which proves our
first hypothesis and supports the findings of Schäfer and
Number of active borrowers Fukasawa (2011). For-profit and non-profit MFIs are
This proxies for outreach and confirms the findings also dissimilar both in terms of OSS and outreach.
immediately above. This variable is used here only as a Finally, we show that MFI OSS is driven by higher ROA,
robustness measure. lower Total Expenses/Assets and higher Financial Revenue/
Assets.
Roe There is no obvious tradeoff between outreach and
This is not significant for any model implying share- OSS, since OSS MFIs have larger outreach partially
holders do not really seem to interfere with OSS. OSS of proving our second hypothesis. This implies that finan-
MFIs, as the results seem to prove has to do with efficient cial self-sufficiency does not undermine the depth of
cost management rather than keeping shareholders happy outreach and should therefore not be a point of
with higher returns. This result by itself distinguishes the contention.
business structure of an MFI from a regular corporation. To be profitable, an organization can be any legal type
and non-profit or for-profit, as this does not seem to
Conclusion affect financial sustainability of an MFI.
In summary, ROA is consistently the driver of OSS in all In summary therefore, efficient utilization of oper-
the different types of MFIs we have analyzed. OSS MFIs ational capacities leads to better financial management
are far more efficient at managing their costs followed thereby leading into a more sustainable, effective and
by for-profit MFIs. The OSS model (Model 2) according long-term functioning of the MFIs which can then trans-
to the regression is represented as: late into higher degree of poverty alleviation. Whilst the
inherent functioning of an MFI is still rooted in subsi-
OSS = 1.478 + 4.154xROA–1.801xTotal Expenses/Assets + dised financing, the understanding that this can only
0.216xFinancial Revenue/Assets lead to short term and limited poverty alleviation will
enable the industry to explore different business models.
Practical implications Such business models could guarantee increased finan-
NOSS, UPNOSS and non-profit MFIs truly lack in terms cial and operational sustainability thereby increasing the
of effective cost management. This study shows that of actual impact and outreach that MFIs have on poverty
the 8 factors (barring the intercept) tested as drivers for alleviation.
OSS in the different types of MFIs, 7 factors are (highly)
significant in case of NOSS MFIs also evidenced by the Limitations and future outlook
high R2. This implies that the degree of OSS that allows This work has some limitations though. Since the data
NOSS MFIs to remain afloat as businesses, is driven by provided by MIX Market is standardized version of data
a combination of many factors together. This hints at an submitted voluntarily by MFIs, a likely bias cannot be
ongoing struggle with self-sufficiency. In case of OSS denied. Moreover, since our data analysis was limited
MFIs, it is only 3 highly significant factors that function only to diamond levels 3 and above, a survivorship bias
as drivers of OSS showing a much more focused and is likely.
lean strategy at self-sufficiency. These results also show The results from this study indicate that it is very pos-
a clear focused business model adopted by OSS and for- sible for microfinance institutions to be profitable. In
profit MFIs, the more chaotic fire-fighting one adopted fact, over 50% of them are operationally self-sufficient
by NOSS MFIs, and a seemingly relaxed attitude towards which still leaves plenty of room to change within the
OSS manifested by the non-profit MFIs. This proves our industry.
third and fourth hypotheses whereby for-profit MFIs Moreover, applying these findings to a larger, more
and non-profit MFIs are driven by dissimilar factors and recent dataset, might be a further avenue to explore.
OSS and NOSS MFIs are driven by dissimilar factors. Further evaluating effective ways to capitalise on ROA,
Remer and Kattilakoski International Journal of Corporate Social Responsibility (2021) 6:5 Page 11 of 12

and Financial Revenue, the two strongest drivers for Bayar, Y. (2013). Future of microfinance in the light of recent crises in major
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204440813X13778729134363.
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Abbreviations
commitment to serving the poorest families. Journal of Microfinance/ESR
CAGR: Compound Annual Growth Rate; GMM: Generalized Method of
Review, 1(1), 8.
Moments; MFI: Micro Finance Institution; MIX: Microfinance Information
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Operational Self-Sufficiency; ROA: Return on Assets; ROE: Return on Equity; (MIX) and Social Performance Task Force (SPTF). Retrieved fromhttps://iris.
SSA: Sub-Saharan Africa; UPNOSS: Under Performing Non- Operationally Self- thegiin.org/users/profile/microfinance-information-exchange-mix.
Sufficient Gonzalez, A., & Rosenberg, R. (2006). The State of Microcredit: Outreach,
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