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This paper investigates the relations between female leadership, firm performance, and corporate governance in a global panel of 329 Microfinance Institutions (MFIs) in 73 countries covering the years 1998–2008. The microfinance industry is particularly suited for studying the impact of female leadership on governance and performance because of its mission orientation, its entrepreneurial nature, diverse institutional conditions, and high percentage of female leaders. We find female leadership to be significantly associated with larger boards, younger firms, a non-commercial legal status, and more female clientele. Furthermore, we find that a female chief executive officer and a female chairman of the board are positively related to MFI performance, but this result is not driven by improved governance.
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Female leadership, performance, and governance in microfinance
institutions
Reidar Øystein Strøm
a
, Bert D’Espallier
b
, Roy Mersland
c,
a
Faculty of Social Sciences, Oslo and Akershus University College, Norway
b
Faculty of Economics and Management, Hogeschool-Universiteit Brussel, Belgium
c
School of Business and Law, University of Agder, Norway
article info
Article history:
Received 3 May 2012
Accepted 17 January 2014
Available online 29 January 2014
JEL classification:
G32
G34
Keywords:
Female leadership
Financial performance
Microfinance
MFIs
Cross-country panel data
abstract
This paper investigates the relations between female leadership, firm performance, and corporate gover-
nance in a global panel of 329 Microfinance Institutions (MFIs) in 73 countries covering the years 1998–
2008. The microfinance industry is particularly suited for studying the impact of female leadership on
governance and performance because of its mission orientation, its entrepreneurial nature, diverse insti-
tutional conditions, and high percentage of female leaders. We find female leadership to be significantly
associated with larger boards, younger firms, a non-commercial legal status, and more female clientele.
Furthermore, we find that a female chief executive officer and a female chairman of the board are posi-
tively related to MFI performance, but this result is not driven by improved governance.
Ó2014 Elsevier B.V. All rights reserved.
1. Introduction
The microfinance institution’s (MFI) purpose or mission is to
provide access to financial services to poor families and small busi-
nesses situated mostly in developing and newly industrialized
countries. Microfinance is to a large extent a women’s business. Fe-
male borrowers are the MFIs’ largest market, and lending to wo-
men is considered one of the main reasons for microfinance’s
success (Armendáriz and Morduch, 2010). But microfinance is
not only a business for women it is to a large extent also a business
by women. Interestingly, beside Nobel laureate Muhammad Yunus,
several women are industry icons: for example, Pilar Ramirez of
Banco FIE in Bolivia and Ingrid Munro in Jamii Bora in Kenya. The
female proportion of top executives and directors in MFIs is high.
In our sample the CEO is female in 27% of MFIs, the chair is female
in 23%, and 29% of all board seats are held by women. These pro-
portions are much higher than corresponding figures in traditional
firms. For instance, for their very large sample of U.S. companies,
Adams and Ferreira (2009) report that only 8.8% of directors are fe-
male. In this paper, we investigate whether female leadership im-
proves governance and financial performance in MFIs.
Unlike studies in high-income countries (Smith et al., 2006;
Adams and Ferreira, 2009) that often consider only the role of
directors, we address these questions for the chief executive officer
(CEO), the chair, as well as board directors. Moreover, our study is
novel because it surveys entrepreneurial firms (MFIs) in emerging
markets. Our data tells us that the MFIs’ median time in operation
is eight years. In eight years, the weight of tradition has not settled
in a firm, so that a masculine culture has not yet become ingrained,
and the male network has not had time to become established.
Thus, the ‘‘glass ceiling’’ (Kanter, 1977) between men at the top
and women in jobs below has not had time to set. This creates
opportunities for able women to rise in the MFI’s leadership hier-
archy. Microfinance is also typically a mission-driven organization
(Randøy et al., 2014). Thus, we are able to tell if the leadership’s
gender matters for governance and performance in circumstances
that are different from those usually studied.
The female orientation is often a stated goal in many MFIs. The
MFIs in our sample have indicated whether they prefer to lend to
women. 44% state that they have such a female bias. This female
attention is evident in supporting international organizations for
microfinance as well. The objective of the Microcredit Summit
Campaign, which plays a central role in the promotion of microfi-
nance, is ‘‘to ensure that 175 million of the world’s poorest fami-
lies, especially women, receive credit for self-employment and
http://dx.doi.org/10.1016/j.jbankfin.2014.01.014
0378-4266/Ó2014 Elsevier B.V. All rights reserved.
Corresponding author. Tel.: +47 92434333.
E-mail address: roy.mersland@uia.no (R. Mersland).
Journal of Banking & Finance 42 (2014) 60–75
Contents lists available at ScienceDirect
Journal of Banking & Finance
journal homepage: www.elsevier.com/locate/jbf
Author's personal copy
other financial and business services’’ [our emphasis]
(www.microcreditsummit.org).
When many MFIs are gender biased, it becomes interesting to
study how well female leaders perform. Does a woman bring better
governance and better financial performance to the MFIs they run?
Mersland and Strøm (2009) argue that because a female CEO is bet-
ter able to tap into the local, often female information network, fe-
male leaders may design product and procedures that better meet
the female users’ needs. We follow this line of argument here, and
extend the analysis to the chair and the board of directors. Our
main hypothesis is that female leadership has beneficial conse-
quences for the MFI’s governance as well as its financial perfor-
mance. In gender biased MFIs, female leaders could be better
matched to the challenges and opportunities that female custom-
ers face.
But female leadership is possibly endogenous, that is, specific
MFIs may attract female leaders. If these are also good performers,
we cannot attribute good financial performance to female leader-
ship. At most we can state there is a correlation. We attempt to dis-
entangle the reverse causality problem by following the Heckman
(1978) dummy endogenous variable method. We also test for sam-
ple selectivity bias by the inverse Mill’s ratio test.
The research on female leadership is scant in microfinance.
Armendáriz and Morduch (2010) argue that female targeting and
financial sustainability are perfectly compatible, since female tar-
geting within microfinance has often been attributed to increased
efficiency due to higher repayment rates among female borrowers.
D’Espallier et al. (2011) confirm that the targeting of women leads
to higher repayment rates in MFIs. Both deal with the customer as-
pect. However, our study investigates whether female leadership
has an impact upon the MFI’s governance and its financial perfor-
mance in an industry that to a large extent caters to female
customers.
In the general governance literature, only Adams and Ferreira
(2009) address both the governance and performance issues re-
lated to female management, but limit the study to directors. They
find that female directors are ‘‘tougher’’ monitors than men, but
also that a positive effect of female directors on performance is
only detectable for firms with weak governance structures when
instrumental variables (IV) methodology is employed. Further-
more, Smith et al. (2006), and Francoeur et al. (2008) investigate
the relationships between a female CEO and female directors on
the one hand and financial performance on the other. These studies
do not look into corporate governance issues. All studies cited here
use data from diverse industries in Western countries. In contrast,
we investigate the effects of three leadership types (CEO, Chair and
Directors) upon both corporate governance and financial perfor-
mance in an homogeneous industry in many developing countries.
The sample consists of 329 MFIs in 73 countries from 1998 to
2008. The data are from rating agencies and cover up to six years
of data per individual MFI. The sample is drawn from the same
industry, where MFIs largely follow the same mode of operation,
focusing on loans to poor people and small enterprises, granting
small loans with a short maturity, and demanding frequent repay-
ments (Helms, 2006). Borrowers often have little or no collateral or
credit history. Frequent repayments enable the MFI to quickly as-
sess the borrower’s repayment ability. Nevertheless, heterogeneity
may arise due to different firm and country characteristics, in par-
ticular attitudes to women. We control for heterogeneity by firm
and country background variables. Firm controls include the MFI’s
business practice, differences in institutional background, and
commonly used controls such as MFI size, age, and risk. The coun-
try controls encompass a set of country variables, and also include
world regional dummies. It turns out that despite cultural diver-
sity, the fraction of female leadership positions is remarkably sim-
ilar across countries. By controlling for country differences, our
findings are relevant for corporate governance in other than
emerging markets (Aguilera and Jackson, 2010).
We find that female leadership is negatively related to such
governance measures as the number of board meetings, internal
audits, and the separation of the CEO’s and chair’s roles, but posi-
tively related to MFI financial performance. This is contrary to
what Adams and Ferreira (2009) find for female directors. Thus,
the quality of an MFI’s CEO and chair seems to be more important
for the MFI’s success than general corporate governance. Country
specific variables complement these findings, as they are corre-
lated with corporate governance but not financial performance.
The results are robust to variations in estimating methodology,
variable definition, and regression specification.
This article proceeds as follows. Section 2develops hypotheses
from former literature on female top executives’ and board mem-
bers’ influence on firm performance and corporate governance.
Section 3gives a brief introduction to the microfinance industry
and its special focus on women together with data descriptives.
Section 4lays out the estimating methodologies and also defines
variables. Section 5covers the conditions under which female lead-
ership tends to arise, and Section 6examines the relations between
female leadership and corporate governance. Section 7deals with
the relations between female leadership and the MFI’s financial
performance. In Section 8we perform a number of robustness
checks, and Section 9presents our conclusions.
2. Gender, governance and performance
Mersland and Strøm (2009) find that a female CEO induces a
higher financial performance in the MFI. They assume this is due
to the female CEO’s better understanding of the market in which
the MFI operates. This is a matching, or sorting, argument implying
that an MFI that is matched with a leadership that has the same
traits will perform better. In this case, the ‘‘same traits’’ refers to
gender, so that for instance an MFI favoring female clients is
matched with female leadership. The underlying theory for this
is the Becker (1973) model for the marriage market.
1
Thus, the
hypothesis is that female managers and directors will improve the
MFI’s governance and financial performance due to the better match
between the MFI’s leadership team and its market conditions. In
microfinance, Ghatak (2000) shows how the Becker model may be
applied to the matching of good borrowers in a group lending
scheme. In Thomas and Ramaswamy (1996) the matching of leaders
with specific traits and the firm’s strategy increases firm
performance.
The matching hypothesis of female leadership and the MFI con-
tains two sub-hypotheses. The first is that female leadership is
more likely to be found in MFIs with a bias towards female custom-
ers. The second is that an MFI’s governance and financial perfor-
mance improves with female managers and directors. But we
hereby encounter two potential endogeneity problems. The first
is the reverse causality case (Hermalin and Weisbach, 1998) when
the MFI performing financially well attracts a female CEO. We con-
trol for reverse causation of female leadership in financial perfor-
mance regressions by the Heckman (1978) model for an
endogenous dummy variable. The second endogeneity problem is
sample selectivity, that is, the selection of a female CEO, chair or
director might be related to the emphasized focus on female cus-
tomers i.e. MFIs hire a female leader because most clients are wo-
men, not because of their qualifications. We handle this second
endogeneity problem by the inverse Mill’s ratio (IMR) test. In the
1
Becker gives various examples of matching: ‘‘...the optimal sorting of more able
workers and more able firms, more ‘‘modern’’ farms and more able farmers, or more
informed customers and more honest shopkeepers’’.
R.Ø. Strøm et al. / Journal of Banking & Finance 42 (2014) 60–75 61
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gender literature, the Kanter (1977) ‘‘window-dressing’’ or ‘‘token-
ism’’ is an argument for the endogeneity of female leadership. She
proposes that a company’s election of female directors is done in
order to demonstrate commitment to gender-neutral policies.
The dominant approach in gender studies seems to be that both
governance and financial performance improves with more wo-
men in management and board. Teigen (2000) puts the positive
gender effect down to what may be called the underused resource
effect and the underused diversity resource effect. The total resource
pool of equally able men and women is underused when candi-
dates for management and director positions are pulled from the
sub-set of men only. The underused diversity resource means that
managers and directors are not optimally matched to the condi-
tions of their firm. This last effect could be especially important
in microfinance, where the majority of clients are female. Female
leaders should therefore increase governance quality and financial
performance in MFIs. Beneficial leadership is often seen as an
intrinsic quality among women. Thus, Shrader et al. (1997) sum-
marize this in an early investigation: ‘‘There is evidence that wo-
men are more oriented toward supporting and maintaining
relationships than men. ... Therefore, as more and more women
assume managerial positions, organizational learning, climate,
and performance should improve.’’ Bertrand and Schoar (2003)
conjecture that female managers and directors represent a new
management style. If this is true, it should only increase the effects
on governance and performance. In particular, in the Adams and
Ferreira (2007) model directors perform two functions, monitoring
and advice. Thus, female directors could be better advisors to the
CEO than male. This could be especially important in the young
and entrepreneurial MFIs, where growth rates are unusually high.
It appears that most empirical investigations start from the re-
source effects, but that the advantages of matching go unnoticed.
Furthermore, only Adams and Ferreira (2009) address governance
and financial performance, although predictions are for both is-
sues. They find that women on the board generate better gover-
nance, but better firm performance follows only in firms with
weak overall governance. Moreover, besides the above-mentioned
Shrader et al. (1997) study, we are aware of only Smith et al. (2006)
and Francoeur et al. (2008) who study different management and
director positions. These find that female managers (e.g. the female
CEO) improves firm performance, but that female directors are
only weakly or negatively linked to financial performance. Besides
this, some studies investigate the role of gender in the top manage-
ment team. Welbourne et al. (2007) find that short-term and long-
term financial performance (Tobin’s Q) improves when women are
part of the top management teams in firms undertaking an initial
public offering (IPO).
By far the most numerous studies concern the relationship be-
tween female directors and financial performance. This has been
addressed in cross-sectional and panel data studies, in event stud-
ies using time series, and in a natural experiment setting. First,
cross-sectional and panel data studies show conflicting results.
Some studies find a positive relation (Carter et al., 2003; Campbell
and Minguez-Vera, 2008), while others detect a negative relation
(Shrader et al., 1997; Smith et al., 2006;Rose, 2007; Adams and
Ferreira, 2009; Bøhren and Strøm, 2010; Carter et al., 2010; Galle-
go-Álvarez et al., 2010). Second, the event studies measure stock
price reaction to the announcement of a female director. Here,
the results lean towards a positive relationship. Farrell and Hersch
(2005) report no wealth effect, while Campbell and Minguez-Vera
(2010) and Kang et al. (2010) find positive reactions. Third, Ahern
and Dittmar (2012) utilize the natural experiment setting that the
quota regulation in Norway from 2003 to 2008 affords, and find a
negative relationship. Thus, different methodological approaches
yield conflicting results, both within methodological approach
and between approaches.
Can conflicting results be due to the measures used? For in-
stance, Konrad et al. (2008) forward the ‘‘critical mass’’ hypothesis
that at least three women must be on a board for their female
advantages to be realized. The female director effect is possibly
most evident in board committees (Carter et al., 2010). We are able
to test the first possibility, but lack information about the second.
Furthermore, it is difficult to accept psychological explanations for
superior female leadership. In fact, the Alvesson and Billing (2009)
comprehensive survey of the psychological literature on manage-
ment style reveals no large differences along gender lines.
The conflicting results may also stem from different institu-
tional and country heterogeneity (Aguilera and Jackson, 2010). La
Porta et al. (1998) underline differences in law traditions in com-
mon law and Roman (Civil) law countries. Terjesen and Singh
(2008) find cross-country differences in the fraction of female
directors using a 43 country sample. Furthermore, a robust result
in the general board literature is that the more complex the firm
is in terms of the size and span of its operations, the more outside
directors are recruited (Baker and Gompers, 2003; Boone et al.,
2007; Linck et al., 2008). Speckbacher (2008) argues that since
nonprofit firms often have complex objectives, they will typically
have larger boards. Thus, we include a number of institutional
and country controls for the regressions.
3. Women in microfinance: data and variable definitions
The microfinance mission is to provide low-income families and
small businesses access to financial services. MFIs have a double
objective, to serve the poor and to do so in a financially sustainable
way (Morduch, 1999). Starting as experimental development
schemes in Asia and Latin America in the 1970s, microfinance
has become a major industry today. More than 3000 MFIs report
their numbers to the Microcredit Summit (www.microcreditsum-
mit.org), and they provide more than 150 million people with cred-
it. More than 100 international funds invest in microfinance
offering equity, loans, bonds, and collateralized debt obligations
(www.mixmarket.org). The industry is young and entrepreneurial,
in fact, the median age is 8 years in our sample, 25% are under
banking authorities regulation, and the incorporation ranges from
shareholder ownership (25%) to cooperatives (16%) and non-gov-
ernmental organizations (52%).
Our data set is based on rating assessment reports gathered by
specialized rating agencies and encompasses 329 MFIs operating in
73 different countries worldwide in the years 1998–2008.
2
At each
rating, the raters collect data for the rating year and years immedi-
ately preceding. In this way, up to six years of data for an MFI are
available for the period 1998–2008. The amount of detail varies in
the reports, resulting in different numbers of observations. No data-
set is perfectly representative of the microfinance field. In particular,
our dataset contains relatively few megasized MFIs, and does not
cover the virtually endless numbers of small savings and credit coop-
eratives. The former are rated by such agencies as Moody’s and Stan-
dard & Poor’s, while the latter are not rated at all. Ratings data are,
however, considered among the most representative available for
the microfinance industry (Mersland and Strøm, 2009).
Information in the rating reports are collected during on-site
2
The dataset consists of rating reports from five rating agencies: MicroRate,
Microfinanza, Planet Rating, Crisil, and M-Cril. It is an updated version of the dataset
used by Mersland and Strøm (2009) and variables on gender leadership have been
included. At the time of collecting the data the rating agencies were supported by the
rating Fund of The Consultative Group to Assist the Poor (CGAP) and the Interamer-
ican Development Bank (IDB) and the reports were made publically available at the
World Wide Web. The donor support has now shrunk and the public availability
policy has changed. Some examples of reports are, however, still available at
www.ratinginitiative.org and www.ratingfund2.org or at the websites of the rating
agencies.
62 R.Ø. Strøm et al. / Journal of Banking & Finance 42 (2014) 60–75
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visits to the MFI by specialized evaluators working in the rating
agencies. The information is thus not self-reported by the MFI
but is collected by the evaluator and further screened by the rating
committee at the rating agency’s main office. The information in
rating reports is therefore regarded to be of high quality, and rating
of MFIs is one of the main transparency initiatives in the microfi-
nance industry (Beisland et al., 2014). The MFI rating assessments
are much wider than traditional credit ratings, as they aim to mea-
sure the MFIs’ ability to reach their multiple sets of objectives
(Beisland and Mersland, 2012). The purpose of rating reports is
to present independent information that stakeholders, such as
lenders, donors, owners or managers, can use to make informed
decisions. Even if a rating agency argues that its methodology is
different from that of other agencies, the core information used
in this study consists of standard indicators that are calculated
similarly across the industry and by all rating agencies.
Table 1 provides a detailed description of the main variables
used in our analyses, as well as a number of summary statistics.
The table contains definitions of female leadership, financial per-
formance, and governance, the set of MFI and country control vari-
ables, and social mission and institutional variables that enter as
instruments.
3.1. Female leadership
The table shows the measures for the three female leadership
3
categories, CEO, chair, and director. The female director is defined in
four different ways in order to accommodate for potential variations
in results when definitions change (Adams and Ferreira, 2009; Carter
et al., 2010), and for the Konrad et al. (2008) critical mass of at least
three female directors measure. Consequently, we use the indicator
variable as the main female director definition, and try the other
three in robustness checks. In some cases it has been impossible to
ascertain the fraction of female directors. When information on the
number of female directors is missing, we often know the gender
of the chair, in which case we are able to construct a binary variable
showing whether women are on the board or not. Both the female
director fraction and the binary for female director are used in our
regressions.
3.2. Corporate governance
We choose the number of board meetings, CEO/Chair duality,
internal auditors, and board size as governance mechanisms.
4
The number of board meetings measures the intensity of the board’s
work, and thus, the more meetings, the higher is the monitoring
function. The variable may be a relevant measure of monitoring
intensity in the fast-growing microfinance industry. The number of
board meetings is close to eight on average with a median of four.
This is fewer than Monks and Minow (2008) report, but then MFIs
do often not have sub-committees. Entrepreneurial firms often com-
bine the CEO and chair functions. However, governance recommen-
dations (Cadbury, 2002; Organization for Economic Co-operation
and Development, 2004) warn against such power concentration.
CEO/Chair duality is therefore an indication of less monitoring. An
internal audit linked to the board can give the board independent
information on goal fulfillment. An internal auditor means more
monitoring. The overview of the literature on audit fees by Hay
et al. (2006) shows that fees are related to the firm’s size, complexity,
governance, and independence. The CEO/Chair duality and internal
auditor fractions are both low on average. The average board size
of seven members (median six) seems to be on par with interna-
tional experience. Board size has turned out to be hard to categorize.
Early studies in Yermack (1996) and Eisenberg et al. (1998) find a
negative relation between board size and firm performance. Adams
and Ferreira (2009) and Mersland and Strøm (2009) confirm the neg-
ative sign. A possible explanation is that firms lose business oppor-
tunities due to longer decision time in larger boards. However,
studies of endogeneity of governance mechanisms (Baker and Gom-
pers, 2003; Boone et al., 2007; Linck et al., 2008) find that board size
tends to vary with the firm’s size and complexity. Therefore, the sign
is hard to predict. Furthermore, we note the large dispersion in the
data on the number of board meetings and board size. In a young
industry experiments with the best governance setup are to be ex-
pected. Adams and Ferreira (2009) use board attendance and CEO
turnover as measures of governance quality. We believe our proxies
more directly measure relevant governance issues in microfinance.
Thus, we expect that board meetings, internal audit, and board size
increases with female leadership, and that the CEO/Chair duality is
less prevalent with female leadership.
3.3. Financial performance
We use return on assets (ROA) and return on equity (ROE) as
financial performance measures together with operational and
financial self-sufficiency (OSS and FSS, respectively). Market per-
formance measures are impossible since no MFI in our sample is
listed. ROA, ROE, and FSS are all taken directly from the raters’ re-
ports. OSS is defined as portfolio revenues divided by operational
expenses. This measure is free from bias resulting from different
capital structure, access to subsidized funding and possible differ-
ences in default policies in the MFI. FSS is an adjusted measure of
OSS taking into account financial costs, default costs, subsidies and
other MFI specific adjustments. OSS and FSS are commonly used
metrics in MFI evaluations (Armendáriz and Morduch, 2010, p.
243). Table 1 shows that, on the whole, microfinance is not a lucra-
tive business. On average, both ROA and ROE are low and FSS is less
than 1.0. We expect financial performance to increase with female
leadership.
3.4. Social mission
The social mission variables encompass the MFI’s gender bias, its
rurality bias, and the average loan. Table 1 reveals that the average
loan is USD 734 and the median is USD 351. These are often used
measures of poverty outreach in the microfinance literature (Schre-
iner, 2002). In 44% of cases, the rating agencies attest that MFIs have
a female gender bias in their lending practices. Unfortunately, many
MFIs do not report their percentage of female customers. Those
who do, however, show a percentage in the 70–75% range, which
is close to that reported in Cull et al. (2009). Thus, the female frac-
tion is high in MFIs on both the customer and the leadership sides.
Especially when the MFI has a gender bias, it is important for the
MFI to recruit a leadership team that understands its chosen mar-
ket. But given that women are often at the lowest income level in
developing countries, the advantage of having women in leadership
positions should also carry over to the rural/urban measure and
average loan.
3.5. Institutions
The institutional variables are the MFI’s regulatory status, its
founding status (internationally initiated or not), the competition
in its product market, and its ownership type. MFIs are diversely
3
For most MFIs, the female leadership variables have been collected only once,
namely in the year in which the assessment by the rating agency was done. When this
is the case, we assume female leadership variables to be constant over the years in
which other time-varying information is available.
4
The corporate governance variables have typically been collected only once,
namely in the year in which the assessment by the rating agency was done. When this
is the case, we assume governance variables to be constant over the years in which
other time-varying information is available.
R.Ø. Strøm et al. / Journal of Banking & Finance 42 (2014) 60–75 63
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incorporated, covering ordinary shareholder-owned firms, mutu-
ally held institutions (COOP), non-governmental organizations
5
(NGOs), and state banks. We use two indicator variables, one indicat-
ing the MFI is an NGO, the other indicating it is a COOP. The owner-
ship variable is potentially important, since women may more easily
enter leadership positions in the often more mission-driven NGOs
and COOPs. The competition variable is the rater’s assessment of
the MFIs’ competitive challenge in its area, and converted to a com-
mon scale of 1–7. Finally, 36% of the MFIs in our sample are interna-
tionally initiated (prodded to start by a Western organization). This
rich institutional background provides an opportunity for studying
female leadership under a diversity of external conditions. Mersland
and Strøm (2009) find that these institutional variables are not re-
lated to the MFI’s financial performance, but did not investigate gov-
ernance issues.
3.6. MFI and country controls
Aguilera and Jackson (2010) point out that country specific tra-
ditions and institutions can be important in corporate governance
studies. We include a number of MFI level and country level vari-
ables for the governance and performance regressions in order to
account for MFI heterogeneity. The firm level controls are the MFI’s
age, the portfolio at risk (more than 30 days outstanding), and the
MFI’s size. The specification of size is the natural logarithm of total
assets, which reduces outlier bias. We expect that the larger the
MFI is, the more complex it becomes, and the more it will adopt
formal governance mechanisms, that is, monitoring becomes more
important and advising less. The MFI’s age is an important control
variable, since an MFI is likely to learn what governance mecha-
nisms work and how to achieve profitability.
6
Last, the risk is spec-
ified as the default rate, that is, the fraction of the portfolio 30 days
overdue. Cultural predispositions are likely to be found between
countries. The country control variable includes the Human Devel-
opment Index (HDI) from UN’s Development Programme. Further-
more, GDP per capita (adjusted for purchasing power parity), GDP
growth, and the Heritage Foundation’s index of economic freedom
enter regressions as controls along with the HDI index. Finally, we
check for country differences in gender inequalities by including
the UN index GII (gender inequality index).
The 73 countries rank low on development indices, and are
highly dispersed. Appendix A shows the countries in our sample,
the frequency of MFIs in each country and also in main world re-
gions. The MFIs in our sample range from countries ranked 35
(Argentina) to 135 (Niger) in 1998 and from 39 (Chile) to 134
(Democratic Republic of Congo) in 2008 out of 135 countries in
the HDI. Thus, the MFIs in our sample are situated in developing
countries. At the same time, the heterogeneity within the sample
with respect to the home country’s development level underlines
the need for country controls.
The emphasis on choosing a female leadership is not limited to
particular segments of countries in our sample. This is evident
from the scores on HDI and the gender inequality index (GII), see
Table 2. The percentage of female leadership categories in different
world regions is set out in the lower part of the table.
The table shows that only for the female CEO the score differ-
ence is significant in panel A, and even this at the rather low 10%
significance level. Thus, we cannot find clear country differences
with respect to female leadership. Likewise, the female CEO differ-
Table 1
Summary statistics for the variables used in this study. The number of observations (N) is stated in terms of firms for categorical variables and firm-years for continuous variables.
NMean Median St. dev. Min. Max.
Female leadership
Female CEO Binary: 1 if female CEO 329 0.27 0.00 0.44 0.00 1.00
Female chair Binary: 1 if female chair 238 0.23 0.00 0.42 0.00 1.00
Female director Binary: 1 if one or more female directors 167 0.75 1.00 0.44 0.00 1.00
# Female directors Number of female directors 148 1.69 1.00 1.76 0.00 9.00
Female dir. fraction Female directors as fraction of all directors 148 0.29 0.20 0.28 0.00 1.00
Dumfemdir P3 Binary: 1 if # female directors is 3 or more 148 0.23 0.00 0.42 0.00 1.00
Corporate governance
Meetings Number of annual board meetings 197 7.64 4.00 7.51 1.00 52.00
CEO duality Binary: 1 if CEO and chair are same person 304 0.15 0.00 0.36 0.00 1.00
Internal audit Binary: 1 if MFI has an internal auditor 280 0.39 0.00 0.49 0.00 1.00
Board size Number of directors 303 7.04 6.00 3.48 1.00 23.00
Financial performance
ROA Return on assets 1075 0.010 0.022 0.11 0.89 0.34
ROE Return on equity 987 0.053 0.070 0.27 0.91 0.56
OSS Operational self-sufficiency 1061 1.56 1.49 0.68 0.004 3.00
FSS Financial self-sufficiency 667 0.96 0.96 0.37 0.06 3.00
General characteristics
TA Total assets (USD 1000) 1125 5365 2227 9856 19.288 144,000
TLP Total loan portfolio (USD 1000) 1140 3786 1665 5801 3.425 59,700
PaR30 Fraction of loan portfolio 30 days overdue 1043 0.066 0.034 0.094 0.000 0.973
Average loan TLP divided by credit clients 1037 734 351 1330 1.00 24.589
Age Number of years in operation 1081 9.36 8.00 7.23 0.00 79.00
Rural Binary: 1 if emphasized area is rural 319 0.26 0.00 0.43 0.00 1.00
Urban Binary: 1 if emphasized area is urban 319 0.30 0.00 0.46 0.00 1.00
Gender bias Binary: 1 if priority is on female clients 324 0.44 0.00 0.49 0.00 1.00
Regulated Binary: 1 if regulated by banking authority 322 0.25 0.00 0.43 0.00 1.00
Internationally initiated Binary: 1 if internationally initiated 327 0.36 0.00 0.48 0.00 1.00
NGO Binary: 1 if type is NGO 329 0.52 1.00 0.50 0.00 1.00
COOP Binary: 1 if type is cooperative 329 0.16 0.00 0.36 0.00 1.00
Competition index Index from no comp. (1) to high comp. (7) 303 4.26 4.00 1.54 1.00 7.00
5
MFIs incorporated as Non-Governmental Organizations (NGOs) are not-for-profit
firms where no particular group or person can legally claim ownership of it or receive
residual earnings from it (Mersland, 2009).
6
The oldest MFI in our sample is DCC bank in India which started to offer
microfinance services in the early 1920s.
64 R.Ø. Strøm et al. / Journal of Banking & Finance 42 (2014) 60–75
Author's personal copy
ences across world regions are significant, but not the female chair
and the female director. The lowest incidence of female leadership
in any category is in the Eastern Europe and Central Asia (EECA) re-
gion. Appendix A shows that the EECA countries constitute 19.5% of
the sample. Evidently, Table 2 shows that controlling for country or
regional differences is important. But the table also shows that fe-
male leadership is not confined to a particular region, but shows a
fairly similar pattern in all regions. The female leadership similar-
ity and country controls should ensure that our results are not dri-
ven by specific conditions in a few countries.
We run multivariate regressions to analyze relationships. There
is always a danger that multicollinearity occurs in such a setting.
Furthermore, we run corporate governance mechanisms and finan-
cial performance analyses separately. A first way to check for mul-
ticollinearity and independence is to run a correlation analysis for
the main explanatory variables.
Kennedy (2008) puts the danger level for multicollinearity to a
correlation between two variables at about 0.80. Only the correla-
tion between the MFI’s assets and its loan portfolio reported in
Table 3 reaches this level. This simply means that these variables
are substitute definitions of the MFI’s size. The next highest levels
are found between the financial performance variables, the highest
being 0.75 between ROA and ROE. This is as expected. Since the
remaining correlations are low, the explanatory variables used in
regressions are independent on a satisfactory level, that is, they
may be used independently of each other. The table also shows
that the correlations between corporate governance mechanisms
and financial performance variables are weak, the highest being
0.22 between OSS and the number of board meetings. This result
is in fact in line with Mersland and Strøm (2009) who find hardly
any significant relationships between governance variables and the
MFI’s financial performance. This indicates that governance and
financial performance variables may be run independently, which
is the procedure we follow here. Nevertheless, we run robustness
checks on the potential relationships between governance and
financial performance. These robustness checks parallel the testing
procedure in Adams and Ferreira (2009).
4. Methodology
We use a straightforward probit method to predict the female
leadership variables. Thus, in the case of the female CEO we run
the probit regression:
PrðFemale CEOÞ¼fðSocial mission;Institutions;Controls;ErrorsÞ
ð1Þ
and then similar regressions for the chair and female directors. This
relation has an interest in itself, but is also fundamental in financial
performance regressions where female leadership may be endoge-
nously determined.
When either a corporate governance (CG) variable or a financial
performance (FP) variable is the dependent, the basic estimating
relation is as follows for the case of return on assets (ROA):
ROA ¼fðFemaleleadership;MFI controls;Countrycontrols;ErrorsÞ
ð2Þ
The corporate governance variables are meetings, internal audi-
tor, CEO duality, and board size. Financial performance is one of the
financial performance measures (ROA, ROE, OSS or FSS). The CEO
and the chair variables are both dichotomous. We also turn the fe-
male director variable into an indicator variable in the main regres-
sions. For the governance regressions we use the probit method
when the governance mechanism is dichotomous (internal auditor,
CEO duality), and tobit-censored regressions for the discrete gover-
nance variables (board meeting and board size). For instance, the
board size has a truncated distribution, since it cannot be smaller
than 1. The financial performance regressions are performed with
instruments in two different setups that we explain below. All
regressions are performed with a constant, but this constant is
not reported.
In the financial performance estimations we build upon the
Heckman (1978) endogenous dummy variable model and follow
the two-step procedure laid out by Wooldridge (2010, pp. 937–
945). In the first step, an instrument for each female leadership
indicator variable is generated from (1). This is the probability that
a given MFI has a female CEO, say. The extracted probability is then
used in the second step random effects model as an instrument for
female leadership, say the female CEO.
The two-step method yields the added advantage that female
leadership is regressed on variables that are likely to proxy for
the match with female leadership. This concerns social mission
variables, such as the MFI’s gender bias, but also institutional vari-
ables, such as the MFI’s ownership type. The generated instrument,
that is, the likelihood that the CEO is female, is likely to be highly
correlated with the female CEO, but not with any measure of finan-
cial performance. Wooldridge (2010) furthermore shows that the
generating regression (1) does not need to be correctly specified
in order to generate a useful instrument.
The two-step method has the further advantage that an inverse
Mill’s ratio test for endogeneity due to sample selection is easily
devised. Sample selection may arise in our context if it is the case
that some able women do not seek leadership positions in the
MFIs, despite being as qualified as the observed female CEO and
directors.
Table 2
Female leadership and mean scores on the human development index (HDI) and the
gender inequality index (GII) in Panel A and the percentage of female leadership
categories in world regions in Panel B. Higher HDI value means higher development, a
higher GII value means more inequality. HDI is an average over relevant years for the
MFI, the GII is indexed in 2008. Source: UN Development Programme and own data.
Female
CEO Chair Director
Table A
GII:
No 0.653 0.639 0.602
Yes 0.639 0.642 0.629
t-Test mean difference 1.276 0.147 1.352
MFIs 295 214 133
HDI:
No 0.604 0.626 0.660
Yes 0.633 0.625 0.651
t-Test mean difference 1.820
*
0.048 0.423
MFIs 325 235 147
Table B
Region:
Latin America 31.3 26.7 73.5
Africa 24.1 19.7 76.5
MENA 36.4 29.6 75.0
EECA 14.1 11.6 60.7
Asia 34.8 28.1 82.4
Total 27.1 22.7 73.0
MFIs 329 238 148
Chisqrd(4) 9.60
**
5.27 3.15
The HDI combines indicators of life expectancy, educational attainment (mean of
years of schooling for adults aged 25 years and expected years of schooling for
children of school going age) and income (the logarithm of GNI per capita (PPP
USD)) into a composite human development index. The GII includes three main
components, reproductive health (maternal mortality and adolescent fertility),
empowerment (parliamentary attainment and educational attainment) and labor
market (labor force participation rate). EECA is Eastern Europe and Central Asia;
MENA is Middle East and North Africa.
*
Significance at 10% level.
**
Significance at 5% level.
R.Ø. Strøm et al. / Journal of Banking & Finance 42 (2014) 60–75 65
Author's personal copy
Wooldridge (2010, pp. 809–813) shows how a general test for
sample selection can be performed. In our case, when the female
leadership category under consideration is endogenous, the first
step is to estimate (1) as before. From this estimation one saves
the predicted values for the likelihood that the CEO is female. This
is the inverse Mill’s ratio. The second step is to insert this predicted
variable into the financial performance regressions (2). If the pre-
dicted variable is significant, measured by an ordinary t-test, there
is a case for sample selection.
In both the governance and financial performance regressions a
number of control variables are included so as to remove MFI spe-
cific heterogeneity as much as possible. First of all, we estimate
with MFI clustered standard errors to correct for heteroskedasticity
and autocorrelation. This alone in fact takes away most of the MFI
level heterogeneity (Petersen, 2009). Furthermore, the MFI level
and country level variables control for country heterogeneity
among MFIs. Last, we include indicator variables for the main
world regions, defined in Table 2, and for time. Time indicator vari-
ables control for market-wide impacts in financial performance
regressions. With panel data, instruments, and a wide set of control
variables the estimations should at least vouch for reliable correla-
tions, but perhaps causality only in the financial performance
regressions, where we have taken account of the endogeneity of fe-
male leadership.
We report on robustness checks in Section 8varying estimation
method, variable specification, especially the female director, and
the use of lagged variables. Estimations in Adams and Ferreira
(2009) and in Carter et al. (2010) show that results are not robust
to estimation method. For instance, the Adams and Ferreira results
vary with OLS, fixed effects at industry and firm level methods, and
finally, with a two-stage least squares IV method. We specify a dif-
ferent IV model for the financial performance estimations, where
instruments are the significant variables in the regressions of fe-
male leadership on background variables in relationship (1). We
check for the validity of the instruments with the Sargan test of
overidentifying restrictions.
Adams and Ferreira (2009) and Carter et al. (2010) also show
that the financial performance results may be sensitive to the def-
inition of female director. We perform regressions with different
definitions. In particular, we examine the Konrad et al. (2008) crit-
ical mass theory with an indicator variable being one if the number
of female directors is equal to or larger than three. Other female
director measures put to the test are the percentage of female
directors and the absolute number of female directors.
Governance and financial performance may be closely related,
although the correlation matrix in Table 3 and evidence in Mers-
land and Strøm (2009) cast doubt if this is the case in microfinance.
In the Hermalin and Weisbach (1998) model board independence
is endogenously determined by past performance. Accordingly,
Adams and Ferreira (2009) always use governance and financial
performance variables together in regressions. We run two robust-
ness checks for governance importance. The first is for governance
variables where former financial performance is an explanatory
variable. In the second robustness check we include governance
mechanisms among the explanatory variables in financial perfor-
mance regressions.
Adams et al. (2010) recommend the fixed effects method for pa-
nel data, which will remove time-invariant heterogeneity in the
data completely, but then recommend the random effects model
if the board attribute enters nonlinearly, and if different firms face
differently shaped tradeoffs between governance mechanisms. Our
random effects methodology with MFI level standard errors speci-
fications are linear, but also MFI specific. Furthermore, a number of
our explanatory variables are themselves time-invariant, and
would be wiped out with fixed effects. This concerns the female
leadership variables. Accordingly, the random effects method is
our preferred method.
The microfinance promise (Morduch, 1999) says that the MFI
follows its social mission to offer financial services to the poor
while being financially sustainable. This could pose a problem for
estimations, if there is a large, negative correlation between finan-
cial performance and outreach to poor customers. In our sample,
the correlation between ROA and average loan is only 0.111, indi-
cating goal independence. This lack of correlation allows separate
regressions for governance mechanisms and financial perfor-
mance. Nevertheless, we include social mission variables to control
for any influence.
5. The match of female leadership and the MFI
The first step in the IV procedure outlined above is to generate
the probability that the leader in the MFI is female. We test the
hypothesis that female leadership is more likely the greater weight
the MFI places on its social mission. This concerns first and fore-
most the MFI’s gender bias, but also the MFI’s choice of urban
and rural market and its average loan size.
We use straightforward probit regressions for the CEO and chair
regressions and OLS regression for the female director fraction
using data from the rating year. We have related the three female
leadership roles of CEO, chair, and director to the background char-
acteristics of MFIs and a country control,
7
first by using the whole
sample, and then using the sample for the rating year only. The re-
sults appear in Table 4.
Table 3
Correlation matrix. Pairwise correlations between all continuous variables.
1234567891011
1 # Fem. dir.
2 Meetings 0.06
3 Board size 0.33 0.11
4 Roa 0.11 0.06 0.02
5 Roe 0.03 0.06 0.08 0.75
6 Oss 0.08 0.22 0.01 0.51 0.51
7 Fss 0.13 0.02 0.07 0.65 0.50 0.43
8TA 0.01 0.01 0.04 0.07 0.13 0.18 0.17
9 TLP 0.01 0.01 0.01 0.08 0.14 0.20 0.18 0.98
10 par30 0.04 0.18 0.17 0.09 0.08 0.01 0.22 0.10 0.12
11 Average loan 0.12 0.19 0.09 0.01 0.10 0.38 0.02 0.01 0.03 0.01
12 Age 0.01 0.30 0.05 0.11 0.19 0.04 0.01 0.23 0.19 0.26 0.08
Variables are defined in Table 1.
7
The results are unaffected when the HDI control is replaced by the Heritage index
or by a more elaborate set of country controls including per capita GDP and GDP
growth.
66 R.Ø. Strøm et al. / Journal of Banking & Finance 42 (2014) 60–75
Author's personal copy
Table 4 shows that female leadership is more likely in MFIs that
have a gender bias toward female clients, in MFIs concentrating on
the urban market, in NGOs and cooperatives more than in share-
holder-owned MFIs (the omitted category), and in younger MFIs.
However, a female CEO is less likely when the MFI has an interna-
tional founder, while a female chair is less likely when competition
is high.
It is remarkable that gender bias and ownership type are signif-
icant for all three categories of female leadership for the full sam-
ple, and that the signs are the same. Gender bias is significant in all
leadership categories in the rating year regressions as well. Owner-
ship type is significant as well in the rating year regressions, except
for NGOs. Thus, the MFI’s choices of social mission and its owner-
ship structure are important for understanding the extent of fe-
male leadership. The instrument we generate can therefore be
used in governance and performance regressions.
The matching regressions from the relationship (1) have paral-
lels to the papers on the endogeneity of boards, e.g. Boone et al.
(2007), and may contribute to the understanding of the emergence
of female leadership in MFIs. Some of the variables are truly exog-
enous, this concerns first and foremost the internationally initiated
and the firm age variables, but also ownership type, regulation and
the social mission orientation are variables that hardly change.
6. Female leadership and corporate governance
Is internal governance better in MFIs with women as the CEO,
chair, or director? We perform regressions with the governance
variables as dependent and the female leadership variables as
independent and also include control variables and regional dum-
mies.
8
Each female leadership variable is introduced into the regres-
sion, one at the time. Table 5 gives an overview.
The overall statistics are satisfactory, with high F and Wald chi-
square statistics throughout. Thus, we cannot uphold the hypothe-
sis that all explanatory variables are insignificant in the
regressions.
Contrary to our expectations concerning the advantageous
match between female leadership and MFI governance, Table 5
suggests that female leadership is generally associated with weaker
corporate governance. A female CEO is negatively related to board
meetings but positively to the board size, the female chair and
director are negatively related to internal audits, but positively to
CEO duality. The female director is positively related to board size.
The findings are at odds with those of Adams and Ferreira (2009),
who find that female directors bring about better governance prac-
tices, although they use different measures, in particular board
attendance. We cannot confirm the evidence from a review of
Canadian governance (Brown et al., 2002) that ‘‘boards with more
women surpass all-male boards in their attention to audit and risk
oversight and control’’. Rather, the negative association with better
governance by all female leadership categories, not only the CEO,
suggests that mechanisms for CEO monitoring have low value in
female-led MFIs.
A number of the control variables have interesting implications,
and are generally in line with former research linking governance
variables to firms’ market conditions (Baker and Gompers, 2003;
Boone et al., 2007; Linck et al., 2008). MFI size in terms of lnTA
is highly significant in all regressions, inducing more meetings,
more often an internal auditor and CEO duality, and a larger board.
The MFI’s age is likewise highly significant, but the MFI is likely to
split the CEO and chair role as the MFI gets older. Thus, corporate
governance becomes more important as the MFI is larger and older.
This seems reasonable, since formalization often tends to overtake
the entrepreneurial spirit as the firm matures. The internal auditor
results confirm Hay et al. (2006), that is, the same variables are
important for internal audits. We also find that the COOP owner-
ship type comes in combination with more board meetings, less of-
ten internal auditor, more often CEO duality, and a larger board.
The same is true for the NGO ownership form, except for the num-
ber of meetings. Thus, in more complex ownership structures such
as the COOP, governance is discharged by a large board meeting
frequently, and not to supporting mechanisms such as the internal
Table 4
Female leadership and MFI characteristics.
Dep. var All observations Rating year only
Fem. CEO Fem. chair Fem. dir. fraction Fem. CEO Fem. chair Fem. dir. fraction
Social mission
Gender bias 0.445
***
0.912
***
0.122
***
0.389
**
0.901
***
0.118
*
Urban 0.137
*
0.202
*
0.002 0.059 0.292
*
0.017
ln(Average loan) 0.072 0.083 0.010 0.095 0.061 0.002
Institutional variables
Regulated 0.125 0.281 0.103
***
0.095 0.371 0.074
Competition 0.056 0.065
*
0.008 0.067 0.092 0.021
Int. initiated 0.395
***
0.127 0.022 0.358
**
0.064 0.021
NGO 0.378
**
0.320
*
0.081
**
0.128
*
0.566
*
0.051
COOP 0.494
***
0.235
**
0.325
***
0.436
**
0.492
*
0.232
**
Firm/country controls
Age 0.009
*
0.027
***
0.001 0.004 0.041
**
0.001
PaR30 0.869 0.229 0.006 0.190 0.331 0.015
lnTA 0.011 0.118
**
0.032
**
0.049 0.011 0.039
HDI 0.734
*
0.964 0.223
*
1.113
*
0.562 0.427
N731 536 332 254 178 106
Wald
v
2
/F-stat. 57.30
***
87.83
***
9.78
***
18.86
*
32.20
***
2.42
***
Pseudo-R
2
0.07 0.12 0.25 0.07 0.14 0.17
Method Probit Probit OLS Probit Probit OLS
We analyze the characteristics that are associated with female leadership in terms of a female CEO, a female chair, a female director, and the fraction of female directors
(continuous) by means of pooled probit regressions and pooled OLS. Significance levels are based upon standard errors corrected for autocorrelation and heteroskedasticity.
*
Statistical significance at the 10% level.
**
Statistical significance at the 5% level.
***
Statistical significance at the 1% level.
8
In this regression no time-dummies are taken up since the governance variables
in our dataset do generally not vary over time.
R.Ø. Strøm et al. / Journal of Banking & Finance 42 (2014) 60–75 67
Author's personal copy
auditor. It appears that in member owned firms the members con-
trol the firm by sitting on the boards in frequent meetings. Thus,
the CEO’s power position is strengthened by the lack of internal
auditor and the CEO’s presence at the board, but curtailed by a
large board in frequent meetings. Our ownership type variables
confirm the main result in Desender et al. (2009), who compare
widely and closely held firms. These findings indicate that our con-
trol variables are highly relevant in this study.
Table 5 shows that the country variable GDP growth is signifi-
cant and positive in the regressions for board meetings and inter-
nal auditor, and that the Heritage Foundation Index is positively
related to CEO duality and board size. More meetings and better
internal auditing are plausible in a high-growth environment.
Thus, country differences are evident for corporate governance,
supporting Terjesen and Singh (2008), and complement the female
leadership findings.
7. Female leadership and performance
How is female leadership linked to MFI performance? Is the
match of the female leadership with social mission oriented MFIs
advantageous for financial performance? Table 6 shows the results
when we use the Heckman (1978) model for an endogenous indi-
cator variable, the female leadership categories being the indicator
variable, and the probabilities of being for instance a female CEO
derived from regressions in Table 4.
The Wald chi-squared test shows that we cannot leave all
explanatory variables out of the specification. The inverse Mill’s ra-
tio is not significant in any of the regressions, thus, our estimations
do not suffer from sample selection bias.
9
Table 5
Female leadership and corporate governance.
Meetings Internal audit
Female leadership
Female CEO 1.401
***
0.058
Female chair 0.760 0.282
**
Fraction fem. dir. 3.034 1.336
***
Female director 1.016 0.429
***
MFI controls
lnTA 0.858
***
0.764
**
0.976
**
1.017
***
0.328
***
0.265
***
0.408
***
0.381
***
Age 0.191
***
0.221
***
0.167
**
0.158
**
0.013
**
0.023
***
0.019
**
0.022
**
PaR30 0.900 0.370 0.032 0.063 0.335 0.773 0.115 0.068
Competition 0.024 0.297 0.383 0.326 0.056
*
0.002 0.049 0.023
NGO 0.371 0.686 0.558 0.341 0.649
***
0.768
***
0.515
**
0.658
***
COOP 5.827
***
5.121
***
5.997
***
6.086
***
0.596
***
0.621
***
0.124
*
0.702
***
Country controls
GDPpercapita 0.002 0.001 0.001 0.001 0.001 0.001 0.001 0.001
Heritage 0.079
*
0.056 0.056 0.041 0.001 0.001 0.021 0.009
GDPgrowth 24.166
***
20.009
***
19.841
**
19.086
***
1.421 3.470
**
1.823 1.408
Regional dummies Incl. Incl. Incl. Incl. Incl. Incl. Incl. Incl.
N591 458 336 370 863 623 405 467
Fstat./Wald
v
2
12.27
***
12.40
***
11.96
***
10.03
***
181.28
***
131.79
***
129.40
***
150.62
***
R
2
0.06 0.07 0.07 0.07 0.19 0.17 0.29 0.24
Method Tobit Tobit Tobit Tobit Probit Probit Probit Probit
CEO Duality Board size
Female leadership
Female CEO 0.082 0.761
**
Female chair 0.208 0.309
Fraction fem. dir. 0.746
**
0.293
Female director 0.275
*
1.052
***
MFI controls
lnTA 0.209
***
0.253
***
0.263
***
0.216
***
0.484
***
0.524
***
0.423
***
0.328
***
Age 0.026
***
0.034
***
0.074
***
0.039
***
0.009 0.001 0.042
***
0.051
***
PaR30 0.131 0.100 0.988 0.359 0.721 0.406 0.343 0.552
Competition 0.043 0.031 0.162 0.035 0.571
***
0.601
***
0.239
***
0.273
***
NGO 0.261
**
0.208
*
0.029 0.131 1.941
***
2.221
***
1.132
***
1.095
***
COOP 0.523
***
0.554
***
0.946
***
0.654
***
2.157
***
1.772
***
1.552
***
1.426
***
Country controls
GDPpercapita 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001
Heritage 0.037
***
0.041
***
0.026
**
0.034
***
0.042
***
0.043
**
0.019 0.041
**
GDPgrowth 2.079 2.901 0.453 2.062 2.398 0.598 1.745 1.985
Regional dummies Incl. Incl. Incl. Incl. Incl. Incl. Incl. Incl.
N910 685 392 453 883 646 447 500
Fstat./Wald
v
2
71.06
***
71.77
***
58.29
***
46.66
***
16.24
***
12.48
***
6.47
***
10.16
***
R
2
0.10 0.11 0.14 0.09 0.05 0.05 0.05 0.05
Method Probit Probit Probit Probit Tobit Tobit Tobit Tobit
We regress corporate governance on female leadership and controls. Governance is measured through the number of board meetings, a binary for internal audits, and a binary
for CEO duality and the number of board members. For the binary governance variables probit regressions are estimated, for the discrete governance variables, tobit-censored
regressions are estimated. Significance levels are based on heteroskedastic and autocorrelation-corrected standard errors.
*
Statistical significance at the 10% level.
**
Statistical significance at the 5% level.
***
Statistical significance at the 1% level.
9
The IMR-test was performed for all IV-regressions, we did not find any evidence
of sample selection bias.
68 R.Ø. Strøm et al. / Journal of Banking & Finance 42 (2014) 60–75
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The female CEO is positively and significantly related to finan-
cial performance for three out of four measures, confirming our
expectations and Mersland and Strøm (2009). The female chair
and female director are also significant with a positive sign in three
out of four regressions. Thus, results are remarkably similar across
manager and director roles. Shrader et al. (1997), Smith et al.
(2006), and Francoeur et al. (2008) find that performance is en-
hanced with a female CEO, but reduced with female directors.
Adams and Ferreira (2009) also find that female directors overall
have a negative impact upon performance. However, our findings
for female leadership in general support arguments for high ability
among female MFI leaders due to a superior match of leadership
and tasks.
The firm controls show that the firm effects are reasonable,
being positive for MFI size, negative for age, and negative for de-
fault risk. The only occasionally significant country effects are nei-
ther surprising in view of Allen and Gale (2000) finding that firm
performance is quite similar across financial systems of the world.
Thus, from Tables 5 and 6 we find that country idiosyncracies
partly associate with governing mechanisms, but few such associ-
ations are detectable for financial performance.
8. Robustness
How reliable are the above results? In this section, we report
various robustness checks for the estimation method and variable
Table 6
Female leadership and performance: a two-step experiment setting.
ROE ROA
(1) (2) (3) (4) (5) (6)
Panel A
Female leadership
Female CEO 0.189
**
0.035
Female CHAIR 0.178
*
0.104
***
Female DIRECTOR 0.152 0.092
*
MFI controls
lnTA 0.070
***
0.072
***
0.059
***
0.027
***
0.024
***
0.019
**
Age 0.005
**
0.002 0.007
*
0.001
*
0.004 0.001
par30 0.218
**
0.026 0.062 0.216
***
0.043 0.004
Country controls
GDP per capita 0.000 0.000 0.000 0.000 0.000 0.000
Heritage 0.002 0.002 0.003 0.001 0.001 0.001
GDP growth 0.226 0.082 0.237 0.298
**
0.298
*
0.402
*
Regional dummies Yes Yes Yes Yes Yes Yes
Time dummies Yes Yes Yes Yes Yes Yes
Model statistics
N608 456 348 649 485 366
R
2
0.13 0.12 0.09 0.17 0.14 0.13
Wald
v
2
920.72
***
27.77
***
21.59
***
121.87
***
32.10
***
24.07
***
IMR-test 0.137 0.247 0.133 0.082 0.168 0.137
OSS FSS
(1) (2) (3) (4) (5) (6)
Panel B
Female leadership
Female CEO 0.196
*
0.302
**
Female CHAIR 0.013 0.396
**
Female DIRECTOR 0.428
*
0.549
*
MFI controls
lnTA 0.216
***
0.213
***
0.212
***
0.101
***
0.112
***
0.121
***
Age 0.004 0.003 0.003 0.001 0.001 0.001
par30 0.384 0.501 0.351 0.648
***
0.741
***
0.729
***
Country controls
GDP per capita 0.000 0.000 0.000 0.000 0.000 0.000
Heritage 0.017 0.023 0.018
*
0.005 0.007 0.006
GDP growth 0.35 0.045 0.154 0.088 0.13 0.042
Regional dummies Yes Yes Yes Yes Yes Yes
Time dummies Yes Yes Yes Yes Yes Yes
Model statistics
N647 489 371 455 388 388
R
2
0.23 0.21 0.21 0.17 0.17 0.17
Wald
v
2
239.11
***
79.86
***
456.19
***
72.18
***
352.66
***
116.97
***
IMR-test 0.101 0.479 0.169 0.213 0.201 0.249
Instruments: fitted probabilities from a probit explaining binary variables for female CEO, female chair, and female director.
Financial performance in terms of ROE, ROA (panel A), OSS and FSS (panel B) regressed on female leadership, MFI and country controls using IV. As instruments, fitted
probabilities from a probit analysis explaining the dummy female CEO, dummy female CHAIR and dummy female DIRECTOR have been used, in the dummy endogenous
variable model of Heckman (1978). Significance levels based on heteroskedastic and autocorrelation-corrected standard errors clustered at the MFI level.
*
Statistical significance at 10% level.
**
Statistical significance at 5% level.
***
Statistical significance at 1% level.
R.Ø. Strøm et al. / Journal of Banking & Finance 42 (2014) 60–75 69
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specification. This is motivated by the diversity of results in the lit-
erature depending on the method used or the variable
specification.
In the first robustness test we use a different instrumental vari-
ables method for financial performance, where instruments are ta-
ken from the significant variables in the matching regressions in
Table 4. At the same time, we use the fraction of female directors
as our definition. Table 7 reports the results.
The signs are everywhere the same as in Table 6, but the signif-
icant results are fewer in this case. The MFI control variables be-
have much as in Table 6 as well. The Sargan test tells us that
instruments are relevant for these estimations.
10
Thus, overall,
the results in Table 6 are robust to the estimation method used.
Ambivalent results for the female director in former research
motivate alternative tests employing different specifications of the
female director. In particular, we examine the Konrad et al. (2008)
critical mass hypothesis that there needs to be at least three female
directors to realize their positive impact in full. We implement this
with an indicator variable being one if the number of female directors
is equal to or larger than three. Other female director measures put to
thetestareabinaryvariableforagendermixedboardandtheabso-
lute number of female directors. Table 8 reports the results.
It turns out that the new specifications of the female director
give significant results only for the FSS measure. Thus, we cannot
confirm the critical mass theory or other specifications of the
Table 7
Female leadership and performance.
ROE ROA
Panel A
Female leadership
Female CEO 0.202
**
0.136
Female chair 0.167
*
0.024
Fraction fem. dir. 0.245 0.016
MFI controls
lnTA 0.067
***
0.067
***
0.063
***
0.029
***
0.025
***
0.021
**
Age 0.005
**
0.002 0.006
**
0.001 0.001 0.002
PaR30 0.266 0.024 0.036 0.243
**
0.076
*
0.095
Country controls
GDPpercapita 0.001 0.001 0.001 0.001 0.001 0.001
Heritage 0.002 0.001 0.002 0.001 0.001 0.001
GDPgrowth 0.254 0.027 0.189 0.381
*
0.254
*
0.396
**
Regional dummies Included Included Included Included Included Included
Time dummies Included Included Included Included Included Included
Model statistics
N775 577 377 839 629 406
R
2
0.12 0.12 0.10 0.20 0.16 0.17
Wald
v
2
98.57
***
27.40
***
116.79
***
61.71
***
92.74
***
270.10
***
Sargan stat. 1.670 4.195 2.920 6.884 6.786 3.076
Sargan p-value 0.64 0.24 0.40 0.07 0.08 0.38
OSS FSS
Panel B
Female leadership
Female CEO 0.175
*
0.423
**
Female chair 0.034 0.487
**
Fraction fem. dir. 0.116 0.694
**
MFI controls
lnTA 0.212
***
0.204
***
0.191
***
0.117
***
0.131
***
0.121
***
Age 0.005 0.004 0.002 0.003 0.003 0.004
PaR30 0.191 0.345 0.341 0.662
**
0.724
***
0.549
*
Country controls
GDPpercapita 0.001 0.001 0.001 0.001 0.001 0.001
Heritage 0.014
***
0.018
**
0.011 0.001 0.001 0.005
GDPgrowth 0.965 0.502 0.150 0.048 0.016 0.478
Regional dummies Included Included Included Included Included Included
Time dummies Included Included Included Included Included Included
Model statistics
N842 638 408 553 471 360
R
2
0.25 0.19 0.22 0.15 0.14 0.18
Wald
v
2
420.30
***
116.74
***
122.12
***
64.25
***
82.24
***
727.11
***
Sargan stat. 1.979 6.684 5.126 4.659 2.083 1.878
Sargan p-value 0.19 0.15 0.16 0.20 0.56 0.60
Instruments used
Ownership type, internationally initiated, dumrural, gender bias
An instrumental variables approach to determine whether female leadership stimulates financial performance. We regress financial performance on female leadership using
2SLS. For instruments, the variables that have a clear relation with female leadership according to Table 4 have been used. The validity of the instruments is tested using the
Sargan test of overidentifying restrictions. Significance levels are based on heteroskedastic and autocorrelation-corrected standard errors clustered at the MFI level.
*
Statistical significance at 10% level.
**
Statistical significance at 5% level.
***
Statistical significance at 1% level.
10
In Table 7, we have 2 out of 12 regressions were at the 10% significance level in
the ROA regressions. Table 8 shows significance levels at 1% and 2% in two OSS
regressions. Thus, the null hypothesis of no overidentifying restrictions cannot be
upheld in these cases.
70 R.Ø. Strøm et al. / Journal of Banking & Finance 42 (2014) 60–75
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Table 8
Instrumental variables performance regressions with different definitions for female director.
ROE ROA
Panel A
Fraction fem. dir. 0.245 0.016
# Female directors 0.025 0.008
dumfemdir P3 0.071 0.013
lnTA 0.063
***
0.058
***
0.056
***
0.021
**
0.021
***
0.021
***
Age 0.006
**
0.006
*
0.007
**
0.002 0.002
*
0.002
*
PaR30 0.036 0.044 0.039 0.095 0.093 0.095
Sargan stat. 2.920 3.293 4.352 3.076 2.976 2.305
Sargan p-value 0.40 0.35 0.23 0.38 0.39 0.51
N377 377 377 406 406 406
R
2
0.10 0.10 0.10 0.17 0.17 0.18
Wald
v
2
116.79
***
40.28
***
253.88
***
270.10
***
400.83
***
536.08
***
OSS FSS
Panel B
Fraction fem. dir. 0.116 0.694
**
# Female directors 0.005 0.085
dumfemdir P30.019 0.391
*
lnTA 0.191
***
0.191
***
0.190
***
0.121
***
0.107
***
0.102
***
Age 0.002 0.003 0.001 0.004 0.003 0.005
PaR30 0.341 0.034 0.035 0.549
*
0.521 0.547
Sargan stat. 5.126 11.170 10.028 1.878 1.592 1.562
Sargan p-value 0.16 0.01 0.02 0.60 0.66 0.67
N408 408 408 360 360 360
R
2
0.22 0.15 0.11 0.18 0.11 0.11
Wald
v
2
122.12
***
199.42
***
52.05
***
27.11
***
37.68
***
380.32
***
Country control variables: GDPpercapita, Heritage, GDPgrowth
Instruments used
Ownership type, internationally initiated, dumrural, gender bias
Fraction fem.dir. is the percentage female directors. # female directors is a continuous variable denoting the number of female directors. Dumfemdir P3 is a dummy that is 1
if the MFI has three or more female directors. For instruments, the variables that have a clear relation with female leadership according to Table 4 have been used. The validity
of the instruments is tested using the Sargan test of overidentifying restrictions. Significance levels are based on heteroskedastic and autocorrelation-corrected standard
errors clustered at the MFI level. Year dummies and country controls are included.
*
Statistical significance at 10% level.
**
Statistical significance at 5% level.
***
Statistical significance at 1% level.
Table 9
Corporate governance and financial performance.
Meetings Internal audit CEO duality Board size
Lagged financial performance
ROA t10.213 0.549 0.645 0.023 0.163 0.250 1.391 1.912
Female leadership
Female CEO 1.608
***
0.065 0.121 0.694
**
Female chair 0.775 0.296
**
0.279
*
0.382
MFI controls
lnTA 1.076
***
0.956
***
0.344
***
0.270
***
0.201
***
0.242
***
0.529
***
0.621
***
Age 0.219
***
0.225
***
0.014
*
0.023
*
0.032
***
0.039
***
0.011 0.001
PaR30 0.614 0.751 0.956 0.324 0.258 0.531 0.989 0.352
Competition 0.007 0.285 0.071 0.008 0.055 0.042 0.586
***
0.613
***
NGO 0.171 0.601 0.681
***
0.823
***
0.254
*
0.191
*
1.880
***
2.259
***
COOP 6.144
***
5.101
***
0.623
***
0.667
***
0.495
**
0.507
***
2.002
***
1.604
***
Country controls
GDPpercapita 0.002 0.001 0.001 0.001 0.001 0.001 0.002 0.001
Heritage 0.116
**
0.088 0.002 0.004 0.042
***
0.049
***
0.055
***
0.053
**
GDPgrowth 25.865
***
22.055
***
2.483 3.761
**
0.601 1.241 3.502 0.102
Regional dummies Incl. Incl. Incl. Incl. Incl. Incl. Incl. Incl.
N422 325 615 444 650 587 626 458
Fstat./Wald
v
2
8.78
***
9.05
***
136.43
***
103.29
***
51.81
***
50.15
***
12.19
***
9.60
***
R
2
0.07 0.06 0.19 0.18 0.10 0.10 0.05 0.05
Method Tobit Tobit Probit Probit Probit Probit Tobit Tobit
We regress corporate governance on lagged financial performance and controls. Governance is measured through the number of board meetings, a binary for internal audits,
and a binary for CEO duality and the number of board members. For the binary governance variables probit regressions are estimated, for the discrete governance variables,
tobit-censored regressions are estimated. Significance levels are based on heteroskedastic and autocorrelation-corrected standard errors.
*
Statistical significance at the 10% level.
**
Statistical significance at the 5% level.
***
Statistical significance at the 1% level.
R.Ø. Strøm et al. / Journal of Banking & Finance 42 (2014) 60–75 71
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female director. The overall findings on the female director vari-
able are inconsistent, partly driven by different specifications of
the variable. Thus, we confirm findings in Carter et al. (2010) that
the definition of female director matters. This implies that different
definitions of female director may cause the different result we
find in the literature.
Next, we turn to the possible link between governance and
financial performance, as the Hermalin and Weisbach (1998) mod-
el suggests. We add the lagged ROA to the set of right hand side
variables and rerun Table 5 in the female CEO and chair regres-
sions. Results are set out in Table 9.
We find no significant relationships between the lagged ROA
and governance. Given the very low correlations in Table 3 between
governance and financial performance variables this comes as no
surprise. Also, the results in Table 9 confirm findings in Mersland
and Strøm (2009), where internal governance mechanisms are
not associated with financial performance. These findings support
the regression specifications we have used here, treating gover-
nance and financial performance in separate estimations.
Finally, we turn the question around following Adams and
Ferreira (2009) in including corporate governance variables into
the financial performance regressions. We include board meetings
and board size among the explanatory variables in the financial
performance regressions, using the Heckman (1978) dummy
endogenous variable method from Table 6. The regressions are
set out in Table 10.
Table 10
Financial performance when board size and the number of board meetings are included.
ROE ROA
(1) (2) (3) (4) (5) (6)
Panel A
Female leadership
Female CEO 0.198
**
0.116
*
Female CHAIR 0.223
*
0.088
*
Female DIRECTOR 0.154 0.096
*
MFI controls
lnTA 0.071
***
0.078
***
0.087
***
0.032
***
0.027
***
0.029
***
Age 0.008
*
0.007
*
0.010
*
0.002
*
0.001 0.002
par30 0.267
**
0.339
*
0.585
*
0.075 0.051 0.122
Board variables
Board size 0.003 0.007 0.004 0.002 0.001 0.004
# Board meetings 0.004 0.003 0.002 0.001 0.001 0.001
Country controls
GDP per capita 0.001 0.001 0.001 0.001 0.001 0.001
Heritage 0.002 0.003 0.003 0.001 0.001 0.001
GDP growth 0.187 0.028 0.041 0.165 0.152 0.227
Regional dummies Yes Yes Yes Yes Yes Yes
Time dummies Yes Yes Yes Yes Yes Yes
Model statistics
N374 303 246 401 325 260
R
2
0.16 0.12 0.11 0.11 0.11 0.17
Wald
v
2
72.71
***
53.47
***
51.26
***
73.61
***
55.54
***
39.36
***
OSS FSS
(1) (2) (3) (4) (5) (6)
Panel B
Female leadership
Female CEO 0.674
*
0.578
*
Female CHAIR 0.083 0.383
**
Female DIRECTOR 0.412
*
0.091
MFI controls
lnTA 0.226
***
0.237
***
0.223
***
0.114
***
0.121
***
0.088
***
Age 0.010
*
0.003 0.007 0.001 0.001 0.002
par30 0.090 0.059 0.285 0.625
**
0.857
**
0.782
**
Board variables
Board size 0.001 0.019 0.006 0.004 0.008 0.006
# Board meetings 0.019 0.024 0.028
*
0.006 0.001 0.001
Country controls
GDP per capita 0.001 0.001 0.001 0.001 0.001 0.001
Heritage 0.018
*
0.024
*
0.015
*
0.004 0.004 0.004
GDP growth 0.223 0.405 0.536 0.806 0.721 0.841
Regional dummies Yes Yes Yes Yes Yes Yes
Time dummies Yes Yes Yes Yes Yes Yes
Model statistics
N403 328 263 317 286 236
R
2
0.23 0.20 0.20 0.16 0.16 0.15
Wald
v
2
116.69
***
97.40
***
82.39
***
56.87
***
61.83
***
43.51
***
Instruments: fitted probabilities from a probit explaining binary variables for female CEO, female chair, and female director.
The Heckman (1978) dummy endogenous model is used in estimations. Significance levels are based on heteroskedastic and autocorrelation-corrected standard errors.
*
Statistical significance at the 10% level.
**
Statistical significance at the 5% level.
***
Statistical significance at the 1% level.
72 R.Ø. Strøm et al. / Journal of Banking & Finance 42 (2014) 60–75
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We find only a weak relationship in the OSS regressions when
the female director is taken to be the female leadership variable.
This means that our results from Table 9 are upheld, and that we
do not risk much in not including governance mechanisms in our
main financial performance regressions in Table 6. An interesting
result in Table 10 is that the female CEO relates significantly to
ROA when the board size is in the regression. This is in fact the
same result that Mersland and Strøm (2009) obtain, supporting
again the enhanced financial performance in female-led MFIs.
In unreported regressions we use winsorized data, but find no
material difference in results from those presented here. Further-
more, we also tried regressions without the GDP Growth and
regressions with interactions between governance variables and
ownership type. None of these robustness regressions gave results
that differ from those reported.
The overall conclusion of the robustness checks is that our re-
sults are confirmed when we vary estimation method, variable def-
inition for female director, and regression specifications. Thus, our
conclusions from the governance and financial performance sec-
tions are upheld.
9. Conclusions
Microfinance institutions (MFI) are remarkable in that they hire
more female CEOs and elect more female chairs and female directors
than firms in advanced countries. AnMFIsmissionistosupplyloans
to low-income families and small businesses, especially women, in
the developing world, and it aims to do so in a financially sustainable
manner (Morduch, 1999). This paper investigates the conditions under
which female leadership tends to emerge, and the relationships be-
tween female leadership and corporate governance, as found in Adams
and Ferreira (2009), and its association with the MFIs’ financial perfor-
mance. Unlike other studies, that often include only directors, ours spec-
ify female leadership as a female CEO, chair, or director. The data are
hand-collected from third-party raters’ reports on 329 MFIs in 73 coun-
tries, and each MFI rating report provides information for up to six years.
Three main conclusions emerge from the investigation. The first con-
cerns the conditions under which female leadership is generally found.
Female leadership increases with the MFI’s mission to supply credit par-
ticularly to women, with being a cooperative or a NGO. However, having
an international founder tends to reduce female leadership. This sup-
ports the matching argument from the Becker (1973) model in the sense
that female CEOs are indeed matched with gender biased MFIs.
The second main conclusion deals with the relationships be-
tween female leadership and corporate governance. Unlike Adams
and Ferreira (2009), we find female leadership to be associated
with weaker corporate governance, since board meetings are few-
er, internal audits less common, and CEO duality more common
when women hold leadership positions.
Finally, the third main conclusion confirms the findings of
Shrader et al. (1997), Smith et al. (2006), and Francoeur et al.
(2008) that female CEOs are positively related to firms’ financial
performance. This also applies to female directors, adding evidence
to the just mentioned studies and also to Adams and Ferreira
(2009). We also find that the female chair performance effect is
similar to that of the female CEO; financial performance is better
in MFIs with a female chair. The conclusion is that in an industry
largely catering for female customers, having female leadership is
likely to improve the MFI’s financial performance. Taken together
with the negative relationship between female leadership and gov-
ernance mechanisms, this means that female-led MFIs perform
better with less oversight, less monitoring. The upshot is that the
quality of leadership is decisive in microfinance institutions.
We believe the contrasting governance and financial perfor-
mance results are due to the fact that MFIs are young, entrepre-
neurial firms. The optimal governance form has perhaps not been
settled. Furthermore, the abilities of the individual CEO are pivotal
in this rapidly expanding segment. Future research would benefit
from exploring the extent and the implications of the female lead-
ership attribute, like education and experience, in more detail.
Future research could also fruitfully explore whether female lead-
ership is better at meeting the MFI’s outreach goals than male.
Appendix A
Countries in the sample and the frequency in each country, and
frequency of firms in different world regions
No. Country Total No. Country Total No. Country Total
1 Albania 1 31 Moldova 2 61 Chad 1
2 Argentina 1 32 Morocco 4 62 Rwanda 4
3 Armenia 3 33 Nicaragua 9 63 Zambia 1
4 Benin 6 34 Pakistan 1 64 China 1
5 Bolivia 14 35 Paraguay 1 65 Serbia 1
6 Bosnia Hercegovina 10 36 Peru 25 66 Ghana 3
7 Brazil 13 37 Philippines 7 67 Malawi 1
8 Bulgaria 2 38 Romania 1 68 Gambia 1
9 Burkina Faso 3 39 Russian Federation 12 69 Kosovo 4
10 Cambodia 12 40 Senegal 8 70 Rep of CongoBrazz 1
11 Chile 2 41 South Africa 1 71 Burundi 1
12 Colombia 6 42 Sri Lanka 1 72 Niger 1
13 Dominican Republic 3 43 Tanzania 1 73 DRC Kinshasa 1
14 Ecuador 16 44 Togo 3 Grand total 329
15 Egypt 4 45 Trinidad and Tobago 1
16 El Salvador 4 46 Tunisia 1
17 Ethiopia 7 47 Uganda 5 Regional distribution
18 Georgia 5 48 Montenegro 2 Code Region Total
19 Guatemela 5 49 Cameroun 3 1 Latin America 99
20 Haiti 2 50 Guinee 1 2 Africa 87
21 Honduras 8 51 East Timor 1 3 MENA 33
(continued on next page)
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Appendix A (continued)
No. Country Total No. Country Total No. Country Total
(continued on next page)
22 India 30 52 Bangladesh 2 4 EECA 64
23 Indonesia 2 53 Nepal 5 5 Asia 46
24 Jordan 3 54 Vietnam 1 Grand total 329
25 Kazakhstan 3 55 Azerbaijan 5
26 Kenya 6 56 Mongolia 2
27 Kyrgyzstan 3 57 Nigeria 3
28 Madagascar 1 58 Mozambique 1
29 Mali 3 59 Tajikistan 4
30 Mexico 16 60 Croatia 1
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