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Purpose This study is motivated by recent research suggesting that the funding benefits of using Big Four auditors may not be as uniform as were previously assumed. The purpose of this paper is to analyze the relationship between use of Big Four auditors and access to debt capital by applying data from microfinance institutions (MFIs) in emerging countries, a population typically not investigated in accounting research. Design/methodology/approach The authors apply a unique hand-collected data set from 60 emerging markets and empirically investigate whether access to various debt categories is related to the use of Big Four auditors. Findings The authors find that access to international commercial debt, international subsidized debt and government agency debt is positively related to the use of a Big Four auditor. For local commercial debt, the authors find no association between auditor type and access to debt capital. The association between auditor choice and access to debt capital is stronger for nonprofit than for-profit MFIs. Originality/value This is the first audit quality study to include a broad sample of emerging countries, which in itself is an important contribution. As far as general audit quality research is concerned, the authors take the literature one step further by showing that the benefits of using a Big Four auditor may be dependent on the specific source of debt financing a firm or organization seeks to use. Moreover, the authors demonstrate that the for-profit vs nonprofit dimension influences the relationship between auditor choice and access to capital.
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Use of Big Four auditors and fund raising:
Evidence from developing and emerging markets
Leif Atle Beisland, Roy Mersland & Øystein Strøm
Published as:
Beisland, L. A., Mersland, R., & Strøm, Ø., (forthcoming), Use of Big Four auditors and fund raising:
Evidence from developing and emerging markets”. International Journal of Emerging Markets.
Abstract
Purpose This study is motivated by recent research suggesting that the funding benefits of using Big
Four auditors may not be as uniform as was previously assumed. We apply data from microfinance
institutions in emerging countries, a population typically not investigated in accounting research, to
analyze the relationship between use of Big Four auditors and access to debt capital.
Design/methodology/approach We apply a unique hand-collected dataset from 60 emerging
markets and empirically investigate whether access to various debt categories is related to the use of
Big Four auditors.
Findings We find that access to international commercial debt, international subsidized debt and
government agency debt is positively related to the use of a Big Four auditor. For local commercial
debt, we find no association between auditor type and access to debt capital. The association between
auditor choice and access to debt capital is stronger for nonprofit than for-profit microfinance
institutions.
Originality/value This is the first audit quality study to include a broad sample of emerging countries,
which in itself is an important contribution. As far as general audit quality research is concerned, we
take the literature one step further by showing that the benefits of using a Big Four auditor may be
dependent on the specific source of debt financing a firm or organization seeks to use. Moreover, we
demonstrate that the for-profit versus nonprofit dimension influences the relationship between
auditor choice and access to capital.
Keywords Audit quality; Cost of debt; Fund raising; Microfinance; Emerging markets.
Paper type Research paper
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1. Introduction
Improved audit quality is associated with fewer information asymmetries between an organization and
its stakeholders (Chen et al., 2011). Prior research suggests that audit quality has real consequences
for an organization; the reduced risk caused by lower agency costs improves fund raising possibilities
and reduces costs of capital (Kitching, 2009; Gul et al., 2013; Pratoomsuwan, 2012). An indicator
variable for use of Big Four auditors is the most commonly used proxy variable for audit quality (Hay
et al., 2006), and several studies suggest that use of Big Four auditors is associated with improved
fund raising opportunities and lower costs of capital (Boone et al., 2010; Pittman and Fortin, 2004;
Mansi et al., 2004).
However, recent reviews of audit quality research find that empirical results from this line of research
are mixed and highly country-specific (Gul et al., 2013; Eilifsen and Willekens, 2008; Tsipouridou and
Spathis, 2012). For instance, Francis and Wang (2008) suggest that Big Four auditors do not universally
enforce higher accounting quality. Their study concludes that the importance of auditor choice is
dependent on the investor protection level of the country in which the firms are situated. Building on
Francis and Wang (1998), Gul et al. (2013) find that cost of debt is lower for firms using Big Four
auditors, but the effect is most pronounced in countries with strong investor protection. In a similar
vein, El Ghoul et al. (2016a) find that equity financing costs are lower in the presence of Big Four
auditors, especially in countries with better institutions governing investor protection and disclosure
regulation. Interestingly, their findings suggest that audit quality does not matter in countries with
weak investor protection and disclosure regulation. To answer the challenge of Francis and Wang
(2008) to learn more about audit quality and its possible consequences in different settings, we turn
to the microfinance industry in developing and emerging markets and use a unique hand-collected
dataset as our case. Specifically, we analyze the association between Big Four auditors and access to
various sources of debt capital.
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The microfinance industry offers several advantages for novel audit research. First, the industry has
grown rapidly into a major industry in developing and emerging countries (Demirguc-Kunt and Klapper,
2012). Thus, it allows exploration of contexts and settings not normally examined in accounting
research (Dechow et al., 2010; De Zoysa and Rudkin, 2010). Access to good financial data from
developing and emerging markets is both a major challenge and a core explanation for the scarcity of
research on Asian, African and Latin-American countries, but the microfinance industry allows us to
apply reliable data collected by professional rating agencies. El Ghoul et al. (2016a) stress the
importance of increasing the geographical coverage when examining the consequences of audit quality
(also see Gul et al., 2013); our investigation includes (by far) the largest number of emerging countries
among the empirical studies published on this issue thus far.
Second, the microfinance industry offers a unique opportunity to study differences between for-profit
and nonprofit organizations. Some microfinance institutions (MFIs) are for-profit corporations,
whereas others are nonprofit entities
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. However, they operate in similar markets, offer similar
products and serve similar clients (see Beisland and Mersland, 2014). Notably, nonprofit organizations
have been subjected to very little audit research (Tate, 2007). In fact, we are aware of no international
study that has investigated the association between auditor choice and fund raising in the not-for-
profit sector. This situation is highly unfortunate, as the nonprofit sector is considerable in many
countries (for example, almost 7 % of national income in the US, according to Tate, 2007). Furthermore,
given that nonprofits in general help people in some type of need, it is very important that nonprofits
can optimize the cost/benefit-relationship associated with using external (expensive) auditors. When
investigating the microfinance industry, we respond directly to Dechow et al.’s (2010) call for research
on accounting-related choices for organizations seeking to meet multiple objectives.
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A nonprofit organization is an organization whose main objective is something other than making a financial
profit. Many MFIs are referred to as hybrid organizations because they have the dual objectives of financial
sustainability and poverty reduction. Similar to other nonprofit organizations, nonprofit MFIs do not seek to
maximize profit, but financial sustainability is necessary to be able to fight poverty.
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Third, the microfinance industry is frequently involved in fund raising, which is a prerequisite when the
association between audit quality and access to capital is investigated. In fact, access to debt capital is
considered necessary to cover the increasing world demand for microfinance services (Ledgerwood et
al., 2013) because retained profits, subsidies and donations do not match the huge demand for
microcredit by low-income families (Gosh and van Tassel, 2013). Importantly, the microfinance
industry is an arena in which different debt providers such as professional banks, government agencies
and providers of subsidized debt meet, thus permitting investigation of whether the influence of Big
Four auditors on access to capital varies between different creditor types.
Our empirical investigation shows no association between the use of Big Four auditors and access to
local commercial debt. However, for international commercial debt, international subsidized debt and
government agency debt, we document a positive association between access to capital and the use
of a Big Four auditor. When dividing our sample between for-profit and nonprofit MFIs, we find that
auditor choice appears to be more important for access to debt capital for nonprofit than for-profit
MFIs.
Our study adds to existing research by demonstrating that the importance of auditor choice is
dependent not only on investor protection level and disclosure regime but also on the types of capital
providers. While prior research has acknowledged that “the benefits of acquiring an audit are multi-
faceted and the value of these benefits is likely to vary across firms” (Knechel et al., 2008, p. 65),
potentially differing preferences for auditor choice across creditor types have received little (if any)
attention. Moreover, to the best of our knowledge, the proposition that the for-profit versus nonprofit
dimension alone might have an influence on the relationship between auditor choice and access to
capital, has not been discussed in prior research. Our findings have obvious and direct policy
implications. Auditor choice matters if MFIs, particularly nonprofit MFIs, seek to raise international
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debt capital or government agency debt capital. However, auditor choice may be less relevant if local
debt is the main source of capital, and MFIs may then be less dependent on choosing the more
expensive (see Hay et al., 2006; Chung and Narasimhan, 2002) Big Four auditors. In general, because
auditor choice has real consequences, not only for individual companies but potentially also for
economies as a whole, we believe it is of major importance to examine how both client characteristics
and creditor characteristics can affect the relation between auditor choice and access to capital.
This paper is organized as follows: Section 2 discusses prior research and develops the study’s
hypothesis. Section 3 outlines the research methodology and presents the data sample. Empirical
findings are explained and discussed in Section 4, and Section 5 concludes the paper.
2. Prior Research and Hypothesis Development
DeAngelo (1981) defines audit quality as the joint probability that an auditor will detect and report a
material misstatement. Thus, the definition of audit quality consists of two components: the ability to
detect misstatements and the willingness to report misstatements that are uncovered during an audit.
Audit fees (Knechel et al., 2008; Lin and Hwang, 2010), auditor size (Francis and Krishnan, 1999; Boone
et al., 2010), and auditor reputation (Khurana and Raman, 2004; Pratoomsuwan, 2012) are the most
commonly listed indicators of audit quality. These indicators are all readily applicable to the Big Four
(or Five or Six) auditors. These Big Four auditors are not only the largest auditors in the world but are
also typically the auditors with the best reputations and highest prices. In fact, according to Hay et al.
(2006), a Big Four binary variable is the most commonly used indicator of audit quality. The proposition
that use of a Big Four auditor is related to high-quality auditing is supported by many empirical studies
(e.g., Knechel et al., 2008; Francis, 2004; Barnes, 2008; DeFond and Jiambalvo, 1993; Krishnan and
Schauer, 2000; Dechow et al., 2010). Hope et al. (2008, p. 360) summarize the use of Big Four auditors
as an indication of high quality as follows: “…the ability to detect material error in the financial
statement is a function of auditor competence, while the propensity to correct or reveal the material
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error is a function of auditor independence from the client big four auditors are perceived to be
competent, given their heavy spending on auditor training facilities and programs, and to be
independent by virtue of their size and large portfolio of clients”.
Prior research suggests that earnings are of higher quality for companies using a Big Four auditor (see
discussion in Francis and Wang, 2008).
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More credible financial reporting; hence, decreased agency
costs, are associated with lower information asymmetries between firm insiders and outside investors
(e.g., see Gul et al., 2013; El Ghoul et al., 2016a). The lower information risk caused by higher-quality
auditing is expected to lead to increased fund raising possibilities and lower costs of capital (e.g.,
Khurana and Raman, 2006) because capital providers can forecast companies’ future cash flows with
greater certainty. Both the positive association between fund raising and use of Big Four auditors and
the negative association between use of these auditors and the costs of (both debt and equity) capital
have been documented empirically (El Ghoul et al., 2016a; Boone et al., 2010; Pittman and Fortin,
2004). Thus, the prevailing view in the audit literature has traditionally been that use of such auditors
is generally beneficial for firms when raising funds (e.g., see Mansi et al., 2004). However, prior
research also indicates that there is no free lunch. Most empirical studies strongly suggest that Big
Four auditors are more expensive than other auditors (Hay et al., 2006), particularly for small- and
medium-sized firms (Choi et al., 2008).
Most Big Four research has traditionally focused on US companies (cf. Fleischer and Goettsche, 2012;
Hay et al., 2006). More recent international research finds that the evidence on general audit quality
differentiation is country-specific (Eilifsen and Willekens, 2008). Inspired by research suggesting that
the role of a Big Four versus a non-Big Four auditor can be highly context-dependent (El Ghoul et al,
2016a; Francis and Wang, 2008), we turn to the microfinance industry to investigate whether the
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Several definitions of earnings quality exist in the accounting literature. In general, earnings quality can be
regarded as a measure of the trustworthiness, usefulness and relevance of financial reporting (Beisland and
Mersland, 2014).
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conclusions on increased fund raising possibilities following the use of Big Four auditors are applicable
to settings other than listed Western companies.
Microfinance is the provision of financial services to low-income families and entrepreneurs. The
growth in the microfinance market is remarkable. Soon, the microfinance sector may become the
world’s largest banking market in terms of the number of customers (Mersland, 2013). Microfinance
is increasingly an important asset class for investors, particularly investors who are pursuing both
financial and social returns (www.mixmarket.org). The importance of close examination of the
consequences of external control mechanisms in the microfinance industry has greatly increased as
more investors and creditors have become involved in microfinance (Beisland et al., 2015; Hartarska,
2009).
The clear majority of MFIs pursue the dual objectives of financial sustainability and social outreach.
Funding for MFIs is supplied by sources that range from donations to commercial investments.
Microfinance is thus an arena in which donors and professional investors may meet. MFIs are typically
incorporated as shareholder firms registered as either commercial banks or non-bank financial
institutions, as nonprofit organizations often referred to as non-governmental organizations (NGOs),
or as formally registered, member-based organizations such as savings and credit cooperatives
(Beisland et al., 2014). Prior research suggests that there is no difference in performance between
different types of MFIs (Beisland and Mersland, 2014). Nonetheless, because of the dual objectives
and considerable numbers of grants and subsidies, correct performance measurements can be
unusually difficult to obtain (Manos & Yaron, 2009). Moreover, the industry has been criticized for
weak corporate governance (Mersland and Strøm, 2009). These factors suggest that information
asymmetries between managers and capital providers may be considerable in the microfinance
industry, thereby making the industry well-suited for research on the importance of auditor choice.
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To service the high demand for microloans, it is contended that MFIs need to shift their funding focus
from donors to the capital markets (Briere and Szafarz, 2015). In this study, we investigate the
association between fund raising and the use of Big Four auditors using observations from 60 emerging
and developing economies (see data sample section). The large number of subsidies and grants
obscures correct cost of capital measurement. Additionally, very different interest regimes make it
challenging to aggregate costs of capital across countries (cf. Chen et al., 2011). Therefore, we focus
the analysis of possible gains from using a Big Four auditor on binary indicator variables for access to
various types of debt capital rather than using cost of capital variables.
Based on theory and prior empirical research (Boone et al., 2010; Pittman and Fortin, 2004), a natural
starting point is to hypothesize that MFIs using Big Four auditors will more easily access capital than
will those using other, presumably lower quality, auditors. Moreover, MFIs are typically small entities,
and Gul et al. (2013) maintain that small firms and institutions have the most to gain from high-quality
auditing. However, Francis and Wang (2008) find that the association between auditor choice and
accounting quality is not invariant across countries (see also Tsipouridou and Spathis, 2012).
Specifically, their research suggests that the influence of Big Four auditors on accounting quality might
be less in countries with weaker investor protection. With weak investor protection, Big Four auditors
often do not have incentives to enforce high earnings quality (also see discussion in El Ghoul et al.,
2016a). In contrast, when investor protection is low, enforcement of high earnings quality might lead
to the dismissal of auditors (Jaggi and Low, 2011). Building on Francis and Wang (2008) among others,
it should thus come as no surprise that Gul et al. (2013) found that the negative relationship between
use of Big Four auditors and the cost of debt documented in prior research (e.g., Pittman and Fortin,
2004) was particularly strong in strict investor-protection regimes (see El Ghoul et al., 2016a for similar
evidence for the cost of equity). Most countries in our sample are known to have weak investor
protection. Thus, it is possible that the association between use of Big Four auditors and access to
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capital is less pronounced in this study than in previous studies that typically were based on Western
observations.
Another aspect of our sample is that it includes donors (inclusive of subsidized debt providers), an
additional stakeholder group typically absent in prior research. Because of the importance of donors
to the microfinance industry, it is possible that investors’ relative influence on audit quality is lower
than in other settings. One may argue that donors are less professional capital providers than
investors, thereby causing audit quality to be of less importance in microfinance than in industries
without donors. However, the opposite possibility cannot be ignored (cf. Harris and Krishnan, 2012).
Tate (2007) claims that because donors receive no direct and easily measurable benefit from their
contributions, they rely more heavily on monitoring than other stakeholders. Thus, following the line
of argument of Tate (2007), the presence of donors might increase the positive association between
fund raising possibilities and use of a Big Four auditor. The latter contention is indirectly supported by
Krishnan and Schauer (2000), who report higher audit quality in their sample of not-for-profit entities
(US Voluntary Health and Welfare Organizations) for users of Big Four auditors. In general, the role of
accounting (and hence auditing) may not be similar between nonprofits and for-profit entities.
Unfortunately, very little research on audit quality has been conducted on nonprofits (Tate, 2007). An
important contribution of our study is to contribute to filling this knowledge gap.
An additional topic that has received relatively little attention in audit research is the possible
difference between actual and perceived audit quality. Boone et al. (2010) document that perceived
differences in audit quality can be larger than the actual differences (cf. Karjalainen, 2011). Thus, even
if there may not be a real difference in audit quality between Big Four and non-Big Four auditors, the
clients of Big Four auditors might still access capital more easily. Little is known about how stakeholders
perceive audit quality (of Big Four auditors relative to other auditors) in developing and emerging
markets. With their Western origin, it is reasonable to assume that Big Four auditors have stronger
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positions in developed countries than in developing and emerging markets. However, international
influence is strong in the microfinance industry. Ashbaugh and Warfield (2003) document a positive
association between audit quality and foreign stakeholders (cf. Leuz et al., 2009). Accordingly, it is not
unlikely that the international relationships of many MFIs may reinforce a possible positive association
between fund raising and use of Big Four auditors. The large distances between capital providers and
MFIs may cause a particularly large demand for well-known Big Four auditors from MFIs trying to raise
capital. Notably, when talking about distances, the cultural aspect may be equality important as mere
geographic remoteness (Paredes and Wheatley, 2017).
Overall, it is uncertain whether the positive association between the use of Big Four auditors and fund
raising possibilities is stronger or weaker in our sample of MFIs from developing and emerging markets
than in traditional research from the US and other Western countries (e.g., Mansi et al., 2004). As a
starting point based on conventional arguments we maintain the hypothesis of a positive
association as follows:
- There is a positive association between use of Big Four auditors and access to various sources
of debt capital in our international sample of microfinance institutions.
The association between various debt types and Big Four auditors was analyzed by El Ghoul et al.
(2016b). However, their focus was on different classes of debt maturity and not creditor type. Here,
we focus on the latter; specifically, our investigation is based on binary indicator variables for access
to different sources of MFI debt financing: commercial debt, subsidized debt and government agency
debt. These three debt types cover all sources of debt capital used by the MFIs in our sample. Regarding
the commercial debt variable, we have data for both local and international commercial debt. As an
additional test of access to capital, we also examine access to voluntary savings (from clients). One
might have proposed sub-hypotheses for the different variables. For instance, based on the finding
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that foreigners avoid investing in poorly governed firms (Leuz et al., 2009), one might assume that use
of Big Four auditors is more important when trying to access international rather than local commercial
capital (Leuz et al., 2009). Moreover, Big Four auditors might be less important for government
agencies than for commercial capital providers (Guedhami et al., 2009). However, given the novelty of
this study, we refrain from launching such clear-cut hypotheses.
3. Research Design and Data Sample
3.1. Research Design
We start the investigation with simple t-tests, in which the mean access to various sources of debt
capital is compared between the sample of MFIs that use Big Four auditors and the sample of those
that do not. We then proceed with a multivariate analysis. Here, an obvious starting point is to regress
binary variables for access to the sources of debt capital on a binary variable for the use of Big Four
auditors and a vector of control variables.
However, our explanatory variable of interest, the Big Four variable, is possibly endogenous (see
discussion in El Ghoul et al., 2016b). This means that running simple OLS regressions does not reveal
whether the use of a Big Four auditor really causes the MFI to more likely assume international
commercial debt, for instance, even if the correlation between Big Four and the assumed debt should
prove to be positive. We solve this problem by linking the Big Four variable to variables that explain
why the MFI has a Big Four auditor in the first place. We follow Beisland et al. (2015) in running a probit
regression with the Big Four as the dependent variable and the MFI’s main market segments
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, number
of branch offices, savings fraction of total portfolio, MFI age, and the presence of an internal auditor
as explanatory variables. From this regression, we obtain the predicted Big Four variable, and then in
a second step, we use predicted Big Four as the explanatory variable instead of the Big Four binary
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A complexity proxy. This variable takes a value of 1 if an MFI’s main market is strictly urban, a value of 2 if an
MFI’s main market is strictly rural, or a value of 3 if the MFI’s main market is a mix of urban and rural settings.
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variable. The interpretation of the predicted Big Four is the same as that of the original variable.
However, the predicted Big Four now incorporates conditions that may cause Big Four to appear in a
given MFI in the first place. The regression with Big Four as the dependent variable is not reported. It
shows that Big Four is related to the variables we mention.
The procedure set out above is from Heckman (1979) and is called the dummy endogenous variable
method. An alternative is the propensity score method of Rosenbaum and Rubin (1983). In our case,
the propensity score would be the probability that the MFI has a Big Four auditor. The score is then
used to construct matching pairs of MFIs with and without Big Four auditors. However, the method is
unsuitable in our case for two reasons. One is that the sample is rather small, so that dividing it into
two will likely reduce the statistical power. Thus, we can easily lose observations in construction.
Second, even if we could construct matching pairs, we cannot be sure that we have used the correct
model to construct the propensity score. If the correct model is not used, then the propensity score
will carry a bias. Heckman and Navarro-Lozano (2004) show that enlarging the number of control
variables in the estimation may create an even larger bias. Wooldridge (2010) concludes that the
Heckman two-step procedure is the more robust method.
To summarize, the multivariate results presented in the empirical sections are the results from step 2
of the dummy endogenous variable method:
CapAccess = α + βPredictedBigFour + γControl + ε
CapAccess represents binary variables for access to local commercial debt, international commercial
debt, government agency debt and international subsidized debt, respectively. PredictedBigFour
comes from the first step of the Heckman two-step procedure. Control is a vector of control variables
(cf. El Ghoul et al., 2016a; Gul et al., 2014) in which the choice of specific variables is adapted to the
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fact that we study non-listed entities from the microfinance industry. We control for MFI size using the
log of assets as our size proxy, the typical size proxy in audit research (Hay et al., 2006). Risk is
controlled for using portfolio at risk > 30 (PAR30) as the risk measure, the most commonly used risk
measure in the microfinance industry (Gutierrez-Nieto and Serrano-Cinca, 2007). PAR30 refers to the
outstanding balance of loans more than 30 days past due divided by the average outstanding gross
loan portfolio.
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Note that market-based risk metrics (such as the market model beta) are not applicable
for non-listed institutions. Profitability is controlled for through return on assets (ROA) (Ahlin et al.,
2011). We expect international connotations to have favorable consequences for fund raising (cf.
discussion in Guedhami et al., 2009). Thus, we control for possible international initiation through a
binary indicator variable. Microfinance is an industry in which certain players are regulated by local
banking authorities, while other entities do not experience this regulation (for more details, see Arun,
2005; McGuire, 1999). Regulations can be imposed in a manner that improves access to capital. We
control for this consideration through a binary indicator variable. Because of the limited sample size
(see below), neither country-specific regressions nor the inclusion of country-specific indicator
variables in the pooled regressions are appropriate. Thus, it is important to include sufficient controls
to account for differences between countries. Thus, we apply three country control variables. Based
on the findings of Francis and Wang (2008) and Gul et al. (2013), we control for investor protection.
Specifically, we apply the Index of Economic Freedom published by The Heritage Foundation (“Index
of Economic Freedom measures economic freedom of 186 countries based on trade freedom, business
freedom, investment freedom, and property rights”, please see www.heritage.org/index for more
details). Furthermore, we use the logarithm of GDP per person as a control variable for the level of
economic development of the countries in which the examined MFIs are located.
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More-developed
countries have more-developed financial markets, which may affect the demand for accounting
4
Note that we do not include leverage as an explanatory variable. Given that we focus the analysis on access to
debt capital, the use of leverage on the right-hand side of the equation would defeat the purpose of the test.
5
In robustness checks, we use the Human Development Index (HDI) from the UN Development Programme. HDI
is a composite index incorporating GDP per capita, health and education indicators.
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transparency (Guedhami at al., 2009) and the relative use of debt versus equity financing (Gul et al.,
2013). Moreover, as a further control for differences between national financial systems, we include
market share of domestic banks in each country.
3.2. Data Sample
Following the rapid growth of the microfinance industry, the increased need for independent MFI
information has led several firms to offer specialized rating assessments of MFIs. These rating
assessments are much broader than traditional credit ratings, as they claim to measure MFIs’ ability to
reach their multiple sets of objectives simultaneously (Reille et al., 2002). The purpose of published
rating reports is to present independent information that stakeholders such as lenders, donors, owners
or managers can use to make informed decisions. Our dataset is hand-collected from these rating
reports using data reported by five of the leading rating agencies in the microfinance industry.
Mitra et al. (2008) report that there are approximately 16 active rating agencies in the microfinance
industry. Our five selected rating agencies have been chosen because they are the agencies that
provide the most information and involve the largest players in the microfinance industry. Specifically,
the agencies selected for this study include the American MicroRate agency, the Italian Microfinanza
agency, the French Planet Rating agency and the two Indian agencies Crisil and M-Cril. All these
agencies consider the entire world to be their market. The agencies are official rating agencies
approved by the Rating Fund of the Consultative Group to Assist the Poor (C-GAP, a branch of the
World Bank) (www.ratingfund2.org).
The MFIs included have voluntarily decided to be rated to reach out to more investors and to
benchmark themselves with other MFIs. A large firm bias is avoided because the very largest MFIs,
operating as commercial banks, are excluded from the dataset these players are normally rated by
traditional rating agencies such as Standards and Poor and Moody’s. Moreover, the dataset does not
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include small savings and credit cooperatives or development programs offering credit to poor people
as part of their social services. Thus, the MFIs included are typical representatives of professional
providers of microfinance services.
The original dataset contains information from 405 MFIs in 73 countries. From this, we pull all MFIs
that have an external auditor. However, some rating reports do not list the auditor name. The data
contains 255 observations on external auditors of MFIs situated in 60 countries (see Table 1). Lawrence
et al. (2011) document that differences in audit quality between Big Four and non-Big Four users can
be industry-dependent. An advantage of our study is that all entities examined belong to the same
industry and thus are similar with respect to products offered and clients served.
Table 1
The rating reports constituting our database are from between 2000 and 2009, with most reports
published during the last five years of this period. The rating agencies differ in their emphasis and in
the abundance of available information. This resulted in different numbers of observations for
different variables and in different years being reported. Where appropriate, all numbers in the
dataset were annualized and dollarized using then-current official exchange rates. Descriptive statistics
for variables used in the study are displayed in Table 2.
Table 2
Of the 255 observations on auditor choice, 30 % are from MFIs audited by PWC, KPMG, Deloitte or
Ernst & Young (now EY). Table 2 presents separate results for the Big Four versus the non-Big Four sub-
sample (cf. Kim et al., 2011). According to a simple t-test, there is no difference in access to local
commercial debt or government agency debt between the two sub-samples. However, MFIs employing
a Big Four auditor appear to have easier access to international commercial debt (56 % compared with
35 % for those not using a Big Four auditor) and international subsidized debt (65 % vs. 41 %). Although
the variable is only used as a robustness check (see below), we note that improved access to clients’
savings seems to be negatively associated with use of a Big Four auditor. Nonetheless, because these
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differences may be attributable to MFI characteristics other than simply auditor choice, we await the
multivariate analysis before drawing strong conclusions.
Moving on to the control variables, MFIs audited by Big Four auditors appear to be larger than others
as measured by total assets. This finding is consistent with prior research (Hay and Davis, 2004).
Moreover, MFIs employing Big Four auditors seem to be less risky as measured by PAR30 and more
profitable as measured by ROA. However, these differences are not significant. With respect to country
control variables, we find evidence of lower Heritage Foundation Economic Freedom Index values, a
higher share of domestic banks and a lower GDP per person in countries covering the Big Four sub-
sample. The finding that Big Four users appear to be situated in less developed countries, relatively
speaking, is somewhat surprising. A possible interpretation is that the signaling effect from using a Big
Four auditor is more important in the less developed countries. This result illustrates the importance
of controlling for systematic country differences between the two-sub samples in the multivariate
analysis.
4. Multivariate analysis
In the main analysis, binary indicator variables for access to local commercial debt, international
commercial debt, international subsidized debt and government agency debt are dependent variables,
and the predicted Big Four variable outlined above is the test variable. Additionally, the control
variables discussed in Section 3.1 are included as explanatory variables. The results of the analysis are
presented in Table 3.
Table 3
Consistent with the t-tests, we find a positive association between the use of Big Four auditors and
access to international commercial debt and international subsidized debt.
6
However, in the
6
We only observe 9 cases where MFIs have access to local subsidized debt a result strongly indicating that local
debt is not subsidized. These 9 observations are not included in the analysis.
17
multivariate analysis, in which systematic differences between MFIs beyond auditor choice are
controlled for, we also document a positive relationship between the use of Big Four auditors and
access to government agency debt. Only for local commercial debt are we unable to find any
association with auditor choice.
One may say that these findings extend the contentions of previous research stating that the
importance of auditor choice is setting-dependent. Investor protection level and disclosure regulations
may not be the only factors that matter the type of capital provider can also affect the relationship
between auditor choice and access to capital. Our findings fit well with previous audit and governance
research. Leuz et al. (2009) found that foreigners invested less in poorly governed firms, whereas
Guedhami et al. (2009) documented that privatized firms worldwide became more likely to appoint a
Big Four auditor along with the extent of foreign ownership.
Local commercial debt providers appear to be less concerned about auditor choice. However, this does
not necessarily mean that these players are less concerned about governance in more general terms.
Prior research suggests that internal auditing can supplement external auditing for nonprofits
(Beisland et al., 2015; Vermeer et al., 2009) and it could be that local capital providers focus more on
governance mechanisms other than the quality of the chosen auditor. Alternatively, inspired by the
research of Boone et al. (2010) and Karjalainen (2011) (also see de Zoysa and Rudkin, 2010), one may
ask whether the perception of audit quality is different in developing and emerging countries. Prior
research ignores many of the world’s developing and emerging countries. Emerging and developing
economies have less-developed financial markets and very different auditing environments (cf.
discussion in Lin and Liu, 2009). If the Big Four do not have the superior reputations in our sample-
countries that they do in developed countries, it is no wonder that we do not find a positive association
18
with access to local commercial capital.
7
Thus, our results might be attributed to Big Four auditors not
being perceived as higher-quality auditors by local debt providers.
Our findings on government agency debt may be harder to interpret. Government agency debt is often
local, and hence, the results conflict somewhat with those just presented. The results are also slightly
harder to relate to prior research. Guedhami et al. (2009) find that state owners place less value on
credible financial reporting and are less apt to choose a Big Four auditor. Similarly, Chen et al. (2009)
document that audit quality is less important for state-owned enterprises. However, our findings on
government agency debt can be related to the following statement by Tate (2007, pp. 50-51): “[s]ince
donors receive no direct benefit from the charitable contributions they provide to a nonprofit and
therefore cannot directly see how the funds were used, they rely more heavily on monitoring to ensure
their funds were used consistently with their intent”. Government agencies will in some respects fit
the characteristics of donors in the microfinance industry. We return to this issue in a supplementary
test of differences between for-profit and nonprofit MFIs below.
8
Before proceeding, we note that many of our control variables are insignificant in this main analysis.
Nonetheless, risk appears to be relevant for access to debt capital. As expected, in the cases where the
coefficient of the PAR30-variable is significant, the relationship between risk and access to debt is
negative.
As a further test of fund raising possibilities, we conduct an additional analysis in which a binary
indicator variable for access to voluntary savings is applied as the dependent variable (see rightmost
7
All our observations are from this century. The reputation of the Big Four may have been negatively affected by
the audit scandals which occurred around the turn of the century. A question that cannot be answered from our
data is whether the reputation of the Big Four was more negatively affected in poor countries than in rich, Western
countries.
8
Government agency debt is typically provided through funds that are designed to support the microfinance
industry. A possible interpretation of the finding that government capital provision is related to the use of Big Four
auditors is that these funds appear to be professional and focused on transparency.
19
columns of Table 3). MFIs are not necessarily banks in the sense that they universally accept deposits,
and only approximately 30% of our sample offer savings. For these MFIs, an additional source of debt
financing becomes relevant; in fact, savings may be a large source of capital.
9
Table 3 suggests that
there is a negative association between the use of Big Four auditors and voluntary savings. However,
we do not interpret this to mean that depositors shy away from MFIs which use Big Four auditors.
Instead, the negative coefficient of the test variable may be interpreted to provide evidence in favor
of what Beisland et al. (2015) refer to as the signaling effect of deposits. That is, when MFIs have access
to savings, they are less dependent on other sources of (professional) capital and are therefore less
dependent on signaling high-quality governance structures through the choice of external auditor. We
note that savings are positively related to size and regulation. The finding that regulation is positively
associated with savings is expected. In fact, being regulated is often a pre-requisite for being allowed
to accept savings. We also note that MFIs that accept savings are less likely to be originated abroad.
Most MFIs subscribe to the dual objectives of financial sustainability and social performance (poverty
reduction). However, increasingly strict commercial players have recently entered the microfinance
market. One may generally argue that whereas NGOs and cooperatives typically are strictly nonprofit
entities, a for-profit objective is more explicit for banks and non-bank financial institutions
incorporated as shareholder companies (Galema et al., 2012).
10
There may be systematic differences
between the two sets of MFIs. Therefore, as an additional test, we separate the for-profit MFIs
(shareholder corporations) from the nonprofit MFIs (NGOs and cooperatives) and re-run all tests on
these two sub-samples; see Table 4. In this alternative procedure, we investigate whether the for-
profit versus nonprofit dimension of firms and organizations can have an influence on the funding
9
Mandatory savings are often applied in the microfinance industry, in that clients are required to save to access
credit. We focus this analysis on the portion of capital provision that is voluntary.
10
For the record; cooperatives may have a for-profit objective in some industries. Thus, our categorization of for-
profit and nonprofit entities should not necessarily be extended to other industries.
20
consequences of audit quality (cf. the contention of Chen et al., 2011, that the governance role of
auditing varies between firm types).
Table 4
In Table 4, auditor choice remains significantly related to international subsidized debt and
government agency debt, but only for nonprofit MFIs. The finding that audit quality is important for
nonprofit organizations is hardly surprising and can be related to prior research (Tate, 2007). However,
the result that auditor choice in our analysis is more important for nonprofits than more profit-oriented
organizations is somewhat unexpected. The data themselves cannot present any answer with respect
to the underlying reason for this empirical result. Nonetheless, based on prior research, we launch
some plausible explanations for the finding that auditor choice appears to be more important for the
capital access of nonprofit than for-profit MFIs.
First, the finding lends additional support to Tate (2007), who states that because the benefits from
providing capital to nonprofit entities may be difficult to measure, capital providers rely even more on
monitoring. Manos and Yaron (2009) maintain that microfinance is an industry in which correct
performance measurements are unusually complex to obtain, and it is reasonable to assume that this
complexity is largest for MFIs with multi-dimensional objectives. The complexity explanation may also
relate to accounting standards. Nonprofit MFIs may apply different accounting rules than shareholder
corporations. If creditors are not familiar with the accounting practice of nonprofits, high audit quality
may become relatively more important when loans are to be granted (cf. Hartarska, 2009). Second,
according to Mersland (2009), banks and non-bank financial institutions might be regarded as more
‘professional’ than NGOs and cooperatives. This aspect can make the use of Big Four auditors more
important for the latter group of MFIs when capital is to be raised. Third, our finding may be related
to other governance mechanisms. Beisland et al. (2015) document that internal auditors are less likely
to be present in nonprofit than for-profit MFIs. Lack of other governance mechanisms can increase the
relative importance of high quality (external) auditing in nonprofit MFIs. Moreover, this argument is
21
somewhat consistent with the contention of Vermeer et al. (2009) that recent governance failures in
nonprofit industries have led to increased scrutiny of nonprofit entities in general. Fourth, we cannot
rule out the possibility that nonprofit MFIs have larger agency conflicts than their for-profit
counterparts (cf. e.g., discussion of internal agency problems in Knechel et al., 2008). The non-
distribution of retained earnings, access to donations and the endowment funds found in nonprofit
MFIs are all characteristics that may exacerbate agency costs (for instance in the form of unnecessary
expenses). Higher agency costs increase the need for high quality auditing (Hay et al., 2006), in
particular if other governance mechanisms are weak or non-existent. .
If (perceived) complexity is the reason why creditors of NGOs and cooperatives apparently rely heavily
on audit quality, one may contend that increased transparency with respect to, e.g., performance,
accounting rules and ‘business practice’ in more general terms can be beneficial for these nonprofit
MFIs when capital is to be raised. If creditors regard lack of professionalism and good control structures
as a challenge for nonprofits, increased focus on the quality of internal governance mechanisms may
pay off. However, before jumping to strong conclusions based on the results of Table 4 we stress that
the number of observations is smaller for shareholder firms than NGOs and cooperatives, which may
explain why we struggle to observe statistically significant relationships for the shareholder MFIs.
Nonetheless, even if disregarding significance levels as such, we note that the regression coefficient
on the Big Four variable is substantially larger in the nonprofit sample for international subsidized debt
and government agency debt (and for international commercial debt, where the Big Four variable is
insignificant in both samples).
5. Conclusions
This is the first study of the consequences of high audit quality that applies a broad sample of emerging
market countries. As instructively discussed by Lin and Liu (2009), high-quality auditing will be adopted
only if the benefits outweigh the costs of the choice. Many benefits from high-quality auditing could
22
arise. In this study, we focus on what has historically been assumed to be the main benefit from using
a Big Four auditor, specifically, increased fund raising possibilities and lower costs of capital (Boone et
al., 2010; Pittman and Fortin, 2004). Our empirical analysis suggests that these benefits are dependent
on the creditor type that an MFI wants to approach. If an MFI aims to increase its international or
government agency debt, the choice of a Big Four auditor can be important and effective. However, in
our sample from developing and emerging markets, we are not able to document any association
between the use of a Big Four auditor and access to local commercial debt. Another important aspect
of our study is that we can compare capital access for nonprofit and for-profit entities that are
otherwise similar. In this additional study, auditor choice appears to be more important for capital
access by nonprofit entities.
Our findings raise several interesting questions for future research. Few of the sample countries are
covered by other international studies of the consequences of audit quality (such as El Ghoul et al.,
2016a; Francis and Wang, 2008; Gul et al., 2013). It could be that the perception of the brand names
of the Big Four are different in countries such as Cambodia, Peru and Zambia than in Western countries
typically covered by traditional audit research (cf. Khurana and Raman, 2004). Emerging markets differ
from developed markets, and there are also distinct differences among countries within the emerging
markets category (Boamah, 2017). Therefore, to investigate how sensitive our conclusions are to the
specific countries covered by the sample, more research is needed on emerging countries in Africa,
Asia and Latin-America. Similarly, with respect to the importance of the nonprofit versus for-profit
dimension, it is important to investigate further to which degree our findings can be generalized to
other industries and settings.
Overall, we consider the most important finding of this study to be that the benefits of auditor choice
appear to be sensitive to the type of capital provider. For several decades, audit quality research was
based on the expectation that higher-quality auditing reduced information asymmetries and thereby
23
increased the access to capital (e.g., see Pittman and Fortin, 2004). More recent research has
suggested that the benefits of higher quality auditors are dependent on investor protection levels and
disclosure regulations (Gul et al., 2013; El Ghoul, 2016a). In this study, we take the literature one step
further by demonstrating that the relationship between auditor choice and access to debt capital may
be creditor-specific. Our findings on capital access for nonprofits and for-profits respectively that are
similar with respect to products, markets and clients are also a contribution to audit research.
However, here we stress that the sample sizes are small and the results therefore should be
interpreted with some caution.
In general, we regard the possible benefits of auditor choice to be an important issue in accounting
research. Big Four auditors might improve access to capital and lower the cost of capital. On the other
hand, Big Four auditors are more expensive than other auditors in developing as well as in developed
countries (Hay et al., 2006; Chung and Narasimhan, 2002) for unlisted entities (Peel and Makepeace,
2012), for nonprofits (Vermeer et al., 2009), for small organizations (Choi et al., 2008) and with respect
to non-audit services (Fleischer and Goettsche, 2012). It is important to weigh the costs of auditor
choice against the benefits. Our study shows that the benefits can be sensitive to both organization
type and creditor type. In the microfinance industry, it is important to remember that access to debt
capital is regarded as necessary to address increasing world demand for microfinance services
(Ledgerwood et al., 2013). International creditors may be the most important source of debt in the
years to come for the microfinance industry. Therefore, one of the benefits from the use of Big Four
auditors documented in this study, i.e., improved access to international debt, may be of vital
importance in the role that microfinance plays in bringing people out of poverty (cf. Imai et al., 2012;
Odell, 2010).
Some caveats are, however, in order. Our data contains little information on access to equity.
Therefore, it may be the case that our indicator variables for access to the various sources of debt
24
capital do not capture all aspects of fund raising. Moreover, we have good data for access to debt, not
cost of debt. Although it remains a challenge to obtain high-quality data for non-listed corporations in
many parts of the world, future research should try to develop both cost of equity and cost of debt
variables in alternative settings, contexts and geographical regions to further develop the literature on
the costs and benefits of auditor choice. Sample size is also important. It is notable that our results
represent average findings for 60 countries, and we cannot rule out that differences exist between our
sample countries.
25
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29
Table 1: Geographical Distribution of Data Sample
Country
Pct.
Country
Pct.
Country
Pct.
Albania
1,18 %
Gambia
0,39 %
Morocco
2,35 %
Armenia
1,18 %
Georgia
1,57 %
Mozambique
0,78 %
Azerbaijan
2,75 %
Ghana
1,57 %
Nicaragua
1,57 %
Benin
2,75 %
Guatemala
1,96 %
Niger
0,78 %
Bolivia
6,27 %
Guinea
0,39 %
Nigeria
0,39 %
Bosnia Herzegovina
5,49 %
Haiti
0,78 %
Paraguay
0,39 %
Brazil
5,49 %
Honduras
2,35 %
Peru
5,10 %
Bulgaria
0,78 %
India
3,92 %
Philippines
0,78 %
Burkina Faso
0,78 %
Jordan
1,57 %
Romania
0,39 %
Cambodia
3,14 %
Kazakhstan
0,78 %
Russian Federation
4,71 %
Cameroun
1,18 %
Kenya
1,96 %
Rwanda
1,57 %
Chad
0,39 %
Kosovo
1,18 %
Senegal
1,57 %
Chile
0,78 %
Kyrgyzstan
1,57 %
Serbia
0,39 %
Colombia
0,39 %
Madagascar
0,78 %
South Africa
0,39 %
DR Congo
0,39 %
Malawi
0,39 %
Tajikistan
2,75 %
Dominican Republic
0,39 %
Mali
0,78 %
Tanzania
0,78 %
Ecuador
5,10 %
Mexico
3,14 %
Togo
1,96 %
Egypt
1,57 %
Moldova
0,39 %
Tunisia
0,39 %
El Salvador
1,18 %
Mongolia
1,18 %
Uganda
1,18 %
Ethiopia
2,75 %
Montenegro
0,78 %
Zambia
0,39 %
Total
100 %
Table 1 lists the geographical distribution of the sample set used in this study.
30
Table 2: Descriptive Statistics for Data Applied in Multivariate Analyses
Big4
Not Big4
Difference
Mean
Std.
Obs.
Mean
Std.
Obs.
in means
Dependent variables
Local commercial debt
0.37
0.49
65
0.43
0.50
159
-0.06
Int. commercial debt
0.56
0.50
70
0.35
0.48
158
0.21
Int. subsidized debt
0.65
0.48
66
0.41
0.49
157
0.24
Government agency debt
0.37
0.49
65
0.39
0.49
159
-0.02
Voluntary saving
0.21
0.41
77
0.36
0.48
178
-0.15
Explanatory variables
Assets
12198
20657
77
5792
10387
178
6406.14
PAR30
0.035
0.066
77
0.060
0.071
173
-0.02
ROA
0.051
0.068
77
0.031
0.074
176
0.02
Int. initiated
0.532
0.502
77
0.404
0.492
178
0.13
Regulated
0.250
0.436
76
0.288
0.454
177
-0.04
Investor protection
56.411
7.015
75
57.091
4.792
172
-0.68
Domestic bank fraction
39.998
30.428
77
34.569
24.795
178
5.43
GDP per person
1738
1324
77
2490
2380
178
-751.77
Table 2 lists descriptive statistics for the variables applied in the multivariate analysis; data are listed separately
for Big Four and non-Big Four users. The difference in means between the two sub-samples is presented, with
boldface denoting significantly different means (at a 5 % level) as measured by a standard two-sided t-test.
31
Table 3: Main Analysis of Access to Capital
Local commercial
debt
International
commercial debt
International
subsidized debt
Gvmt. agency debt
Voluntary savings
Coeff.
z-value
Coeff.
z-value
Coeff.
z-value
Coeff.
z-value
Coeff.
z-value
Big4 predicted
0.098
0.460
0.347
1.990
0.449
1.990
0.541
2.350
-0.810
-2.900
Size (ln Assets)
0.078
0.790
0.201
1.510
0.154
1.510
0.069
0.690
0.448
3.540
Risk (PAR30)
-1.767
-0.950
-3.504
-2.370
-3.886
-2.370
1.840
1.120
2.229
1.180
Profitability (ln(1+ROA))
-0.653
-0.450
-0.965
-1.710
-2.429
-1.710
2.307
1.490
-2.140
-1.090
International initiation
-0.286
-1.460
0.362
1.200
0.240
1.200
-0.481
-2.370
-1.263
-4.290
Regulated entity
-0.046
-0.200
-0.362
-1.750
-0.423
-1.750
0.045
0.190
1.464
5.580
Investor protection
0.006
0.400
-0.031
0.640
0.011
0.640
0.011
0.610
0.001
0.030
Domestic bank market
-0.004
-1.060
-0.009
-0.780
-0.003
-0.780
0.005
1.400
-0.022
-3.640
ln(GDP per person)
0.179
1.110
0.389
0.310
0.052
0.310
0.131
0.790
0.444
2.180
Constant
-2.065
-1.010
-2.190
-0.730
-1.554
-0.730
-2.248
-1.050
-7.206
-2.900
Pseudo R sqrd
0.024
0.126
0.092
0.099
0.403
Observations
197
201
196
199
225
Table 3 lists regression coefficients, z-values, number of observations and Pseudo R2 from the second step of the dummy endogenous variable method described by Heckman
(1979), where the following regression specification is applied: CapAccess = α + βPredictedBigFour + γControl + ε. Regression coefficients in boldface are significant at the 5
% level (two-sided).
32
Table 4: Analysis in Sub-Groups of Shareholder MFIs (SHF) and Non-Shareholder MFIs (-SHF)
Local commercial
debt
International
commercial debt
International
subsidized debt
Government
agency debt
Voluntary savings
SHF
-SHF
SHF
-SHF
SHF
-SHF
SHF
-SHF
SHF
-SHF
Big4 predicted
0.216
0.170
-0.183
0.586
-0.010
0.914
0.181
0.837
0.536
-1.342
Size (ln Assets)
0.379
0.042
0.293
0.184
0.211
0.056
0.628
0.061
0.363
0.583
Risk (PAR30)
-3.211
-2.392
0.224
-6.267
-1.676
-5.253
10.934
-0.952
3.456
2.430
Profitability (ln(1+ROA))
-3.425
-0.547
-1.955
-0.927
0.635
-3.881
3.842
1.292
0.004
-1.829
International initiation
-0.729
-0.075
0.916
0.225
-0.243
0.365
-0.103
-0.748
-2.047
-1.336
Regulated entity
0.357
-0.182
0.012
-0.524
-0.077
-0.515
-1.160
0.533
2.436
1.266
Investor protection
0.043
-0.007
-0.039
-0.032
-0.031
0.047
-0.044
0.057
0.000
0.016
Domestic bank market
-0.015
-0.004
0.002
-0.019
-0.005
-0.002
0.003
0.013
-0.029
-0.024
ln(GDP per person)
1.568
-0.004
0.645
0.312
0.166
-0.149
0.400
0.094
-0.313
0.640
Constant
-16.227
0.394
-5.349
-0.739
-0.496
-1.068
-6.431
-4.348
-1.044
-10.714
Pseudo R sqrd
0.303
0.016
0.121
0.176
0.071
0.137
0.267
0.168
0.603
0.410
Observations
57
140
61
140
58
139
59
140
68
157
Table 4 repeats the analysis of Table 3 on two sub-samples, respectively shareholder (SHF) and non-shareholder (-SHF) MFIs. Regression coefficients in boldface are
significant at the 5 % level (two-sided)
33
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