The Performance Impact of Informal and Formal Institutional Differences
in Cross-Border Alliances: The Case of the Microfinance Industry
Golesorkhi, S., Mersland, R., Randøy, T. & Shenkar, O. (2019), "The Performance Impact of Culture
and Formal Institutional Differences in Cross-Border Alliances: The case of the Microfinance
Industry”. International Business Review. Vol. 28(1), pp. 104-118.
This study addresses the simultaneous and diverse effects of differences in informal and formal
institutions on cross-border alliances’ financial performance. We utilize data from 405
microfinance institutions (MFIs), based in 74 developing countries, that have alliances with
partners from developed countries. We find that the impact of informal institutional differences
between MFIs and their cross-border partners is sigmoid-shaped, with performance first
increasing, then declining, before improving again as informal institutional differences grow
large. By contrast, formal institutional differences appear to be detrimental to MFIs’
performance. Consistent with our prediction, we find that MFIs’ cross-border experience
moderates both formal and informal institutional effects.
Key words: Cross-border alliance; Performance; Informal institutional differences; Formal
institutional differences; Microfinance industry.
Cross-border alliances typically involve the sharing and exchange of knowledge and resources
between partners embedded in varied institutional contexts (Carlsson, 2006). In this paper, we
extend this argument and draw performance implications for cross-border alliances. Past
research on alliance performance has considered structural and relational aspects (Burt, 1992;
Podolny, 1994; Rothaermel, 2001), while institutional differences among alliance partners
have been considered largely from the narrow perspective of “cultural distance” (Kogut &
Singh, 1988). Notwithstanding some notable exceptions (e.g., Filiou & Golesorkhi, 2016;
Lavie & Miller, 2008), there is a dearth of knowledge on the distinct and potentially variable
impact of informal and formal institutional differences on alliance performance. We redress
this gap in the empirical context of vast institutional differences, where alliance partners come
from developed and developing countries, respectively.
Past research has shown that differences in the nature of institutions shape alliance
partners’ attitudes and abilities to learn (Lyles & Salk, 1996; Parkhe, 1991; Simonin, 1999),
which in turn affect their firms’ financial performance. In addressing the role of national
institutional settings in cross-border alliances, we draw a fundamental distinction between
informal and formal institutions, in line with institutional economics (North, 1990). This
growing body of research has highlighted the coevolutionary nature of informal and formal
institutions, while calling for their distinct treatment (e.g., Alesina & Giuliano, 2015; Bowles,
1998; Tabellini, 2008). In this paper, we argue that informal and formal institutional differences
both have an impact on performance returns from cross-border alliances, however much their
To test our contention, we use a sample of 405 microfinance institutions (MFIs), based in
74 developing countries, that have alliances with partners from developed countries. The
microfinance industry makes an interesting testing ground for our research agenda because of
the many cross-border alliances between MFIs in developing countries and their partners in
developed countries (Mersland, Randøy, & Strøm, 2011). Moreover, thanks to transparency
guidelines introduced by international stakeholders like CGAP, which is a specialized
microfinance branch of the World Bank, relevant and high-quality data are available for this
industry, which is uncommon when it comes to data from developing economies in general
(Beisland, Mersland, & Randøy, 2014).
In line with our hypotheses we find that the impact from informal institutional differences
is sigmoid-shaped, with performance first increasing, then declining, before improving again
as informal institutional differences grow large. By contrast, we find a clear negative firm-
based performance effect from large formal institutional differences. A firm’s cross-border
experience has a positive moderating effect on both informal and formal institutional
Our study contributes to the international business literature in several ways. First, we
enhance our understanding of the impact of institutional differences on the performance of
cross-border alliances. Past research has highlighted the role of informal institutions at the
expense of formal institutions (Fey & Beamish, 2001), and often produced inconsistent results,
at times showing that domestic alliances outperform cross-border alliances (Hennart & Zeng,
2002; Mowery, Oxley, & Silverman, 1996), while at other times finding that alliances between
partners hailing from different informal institutional settings perform better than domestic
alliances (Park & Ungson, 2001). Using a global dataset, our study provides comprehensive
and clear results: formal institutional differences between cross-border alliance partners have
a negative effect on performance, whereas the effect of informal institutional differences on
performance depends on the extent of the differences between the partners.
Second, we contribute to the alliance literature by simultaneously addressing the impact of
informal and formal institutions. To do so, we investigate a matrix of interorganizational
partnerships exhibiting large variations in informal and formal institutions between cross-
border alliance partners across many heterogeneous countries (74 in this study) and continents.
Third, our study contributes to the literature on nonlinear performance effects from
internationalization. Specifically, we are motivated by the sigmoid performance effects found
in studies on internationalization through wholly owned subsidiaries (Contractor, Kundu, &
Hsu, 2003; Lu & Beamish, 2004), internationalization of alliance portfolios (Lavie & Miller,
2008), and the effect of institutional differences on firms’ innovation returns from alliances
(Filiou & Golesorkhi, 2016). By extending these past studies, we also shed light on the debate
on the curvilinear effect of informal institutional differences on the cross-border activities of
firms (e.g., Barkema & Drogendijk, 2007; Björkman, Stahl, & Vara, 2007; Stahl & Tung,
Fourth, by comparing the impact of informal and formal institutional differences as the key
contextual elements, we contribute to the growing body of literature emphasizing the need for
understanding the distinct attributes and economic outcomes of informal and formal
institutions (e.g., Alesina & Giuliano, 2015; Bowles, 1998; Tabellini, 2008). We strive to fill
the gap in our understanding of the impact of informal and formal institutions on firms’
financial performance. In particular in the context of developing countries, where informal
institutions have a prominent role in enabling or hindering business transactions and formal
institutions provide weaker business support (Khanna & Palepu, 1997, 2000; Verbeke & Kano,
2013). We also provide an understanding of the impact of firms’ cross-border experience along
each distinct dimension of informal and formal institutions. This gap especially exists in the
context of alliances, a popular and important venue for economic and managerial transactions.
Finally, we contribute to the understanding of organizations working in a rapidly expanding
global service industry (Ault & Spicer, 2014), whose financial returns from internationalization
are yet to be thoroughly researched (one exception is Mersland et al., 2011). Moreover, we
focus on the global microfinance industry, whose importance to economic and social
development and modernization has been widely acknowledged, and which is deeply
embedded in its respective home and host government systems, rendering national differences
2. ALLIANCES AND CONTEXTUAL DIVERSITY
We employ the institutional perspective (North, 1990) to argue that informal and formal
institutions demarcated at the national level (Edquist & Johnson, 1997) give rise to different
sources of enablers and constraints in cross-border alliances and have distinct effects on firms’
financial performance. The differing nature of such institutions shapes partners’ attitudes and
abilities to coordinate the liabilities of such differences and to leverage the financial potential
of cross-border alliances. Specifically, we argue that the tacit (informal) or explicit (formal)
nature of institutions engenders distinct effects on partners’ financial performance in cross-
border alliances. Informal differences, typically unwritten, encompass socially shared rules
and constraints (e.g., Sartor & Beamish, 2014; Sauerwald & Peng, 2013). Due to their tacit
(Polanyi, 1966) and elusive nature, such differences have the potential to generate either the
positive impact associated with, for example, resource complementarities, or the negative
Examples include the Dutch government-owned development bank FMO, with a microfinance portfolio of 8
billion USD in 85 countries (www.fmo.nl), the Belgium BIO, a private-public (50/50) company with more than
150 investments across the globe (www.bio-invest.be), and the Norwegian government-owned NORFUND with
a portfolio of 1.7 billion USD, where banking and microfinance is one of the main asset classes
impact linked to conflicting values, norms, and practices between cross-border alliance partners
(Parkhe, 1991). We posit that firms’ performance varies with the level of informal institutional
differences, following a sigmoid (S-shaped) pattern. When a firm encounters cross-border
partners that are marginally different, its performance is likely to increase due to the partners’
better understanding and appreciation of subtly different approaches; however, as differences
increase, conflicts will surface, eroding performance. Once differences have reached a high
level, awareness of the differences will emerge, and the urgency of collaboration will become
apparent to the partners, prompting cooperation and improved performance.
By contrast, formal institutional differences, codified and explicit in nature (Polanyi,
1966), constitute “rules of the game” and are likely to produce differences between alliance
partners that would be disruptive rather than complementary. More “incompatible” formal
institutional pairs of cross-border alliance partnerships would increase the costs of conducting
business, due to the unfamiliarity of each partner with the other partner’s institutional setting
(Brouthers, Brouthers, & Werner, 2008). Once set, such “rules of the game” cannot be easily
changed and there are no established mechanisms with which to facilitate the rapprochement
of the disparate formal institutional sets of rules (North, 1990).
Finally, we also argue that firms’ cross-border experience helps bridge both informal
and formal institutional differences since experiential learning can capture both codified and
tacit knowledge. The theoretical driver of our argument also incorporates insights from the
literature on absorptive capacity and organizational learning (e.g., Levitt & March, 1988), and
is in line with the prediction of the internationalization (Uppsala) paradigm (Johanson &
The Uppsala internationalization model highlights how firm-based accumulated knowledge and learning
reduce the cost of doing international business by overcoming “psychic distance,” and thus enhance the
potential for profitable internationalization.
2.1. The impact of informal institutional differences
Informal institutions are systems of shared meanings, embedded in norms, values, beliefs, and
the collective understanding of a society, that are not formulated into documented rules and
standards (North, 1990). Furthermore, informal institutions consist of culture, which is
responsible for shaping human cognition, perception, mental models, behavioral norms,
traditions, customs, and belief systems. International business scholars have treated informal
institution similar to culture (e.g., Estrin, Baghdasaryan, & Meyer 2009; Filiou & Golesorkhi,
Cross-border alliances typically involve knowledge exchange between partners, and
this sharing and learning process is shaped by institutional differences (Lyles & Salk, 1996;
Parkhe, 1991; Simonin, 1999). Differences in informal institutions may limit familiarity, and
thus impair interfirm trust (Gulati, 1995), limit the scope of the convergence of values and
goals that are needed to elicit positive attitudes, increase coordination costs, and impair
resource exchange (Parkhe, 1991). However, evidence from existing empirical literature on the
effect of informal institutional differences on the performance of cross-border alliances is
highly inconsistent, showing a positive, negative, and/or no effect (Fey & Beamish, 2001;
Hennart & Zeng, 2002; Parkhe, 1991; Park & Ungson, 1997). The inconsistent results have
been attributed to a myriad of reasons, ranging from differences in theoretical frameworks to
divergences in conceptualization and method (Shenkar, 2001). By and large the literature has
considered informal institutional differences as detrimental to the performance of cross-border
alliances (Barkema, Bell, & Pennings, 1996; Barkema & Vermeulen, 1997, Stahl & Tung,
2015). Only recently has the literature explored the nonlinear effect of informal institutions on
the performance of firms’ cross-border activities and emphasized the positive effect of informal
institutions (Barkema & Drogendijk, 2007; Björkman et al., 2007; Stahl & Tung, 2015).
acknowledging the growing ambivalent and inconclusive influences of informal institutional
differences on the performance of cross-border alliances, we argue that the association between
cross-border informal institutional differences and firms’ performance returns from such
alliances may vary with the level of informal institutional differences.
At a low level of informal institutional differences, resource and skill exchange
opportunities are more accessible to cross-border partners, making organizational learning
more efficient and effective due to partners’ relative similarity. The conformities in perception
and attitudes toward problem-solving enable partners to establish a shared meaning of the rules
of engagement that underpin their collaboration (Ruigrok & Wagner, 2003; Lu & Beamish,
2001; Schenkar, 2001). This facilitates knowledge and resource-sharing, inducing partners to
focus on how they can combine their knowledge and take advantage of their respective
competencies in order to foster performance (Lane & Lubatkin, 1998; Jiang & Li, 2008;
Mowery et al., 1996). This is in line with the internationalization literature, specifically the
Uppsala framework (rooted in Hymer’s “liability of foreignness”), which postulates that firms’
internationalization path is determined by their experiential learning (Johanson & Vahlne,
1977, 2009; Hymer, 1975). The assumption is that the internal information processing
requirements for identifying and accessing network resources are less costly for firms whose
countries share informal institutional settings (Rugman & Verbeke, 2004). Evidence from the
literature also suggests that firms are better able to deal with informal institutional constraints
In addition, evidence from samples of UK-based biopharmaceutical and US-based software firms shows
internationalization of alliance portfolios to have an S-shaped (sigmoid) impact on partners’ innovation and
financial returns, respectively (Filiou & Golesorkhi, 2016; Lavie & Miller, 2008). However, their finding is
different from ours. We believe that this difference may be due to the context of our study, i.e., cross-border
alliances between partners from developing and developed countries, the disaggregation of informal and formal
institutional differences, and/or the nature of the dependent variable.
on expansion into nationally different but proximate regions that share the same informal
institutional settings (Buckley & Ghauri, 2004; Peng, 2002; Peng & Delios, 2006).
Past research also highlights how incremental increases in informal institutional
differences lead to more perceptible, tacit differences between partners in their interpretation
of and response to strategic and managerial issues (Chui, Lloys, & Kwok, 2002; Park & Ungson,
2001). Such institutional differences are likely to increase coordination costs that could
overshadow the marginal benefits of sharing resources and leveraging market opportunities
with cross-border partners (Hitt, Hoskisson, & Kim, 1997). As informal institutional
differences increase conflict, the ensuing mistrust, lack of commitment, and ineffective
interaction become more apparent, leading to lower cross-border alliance performance (Lane
& Beamish, 1990; Sirmon & Lane, 2004). Moreover, informal institutional differences
undermine unique opportunities and valuable network resources offered by partners. The
insufficient overlap between the knowledge bases and national informal institutional
backgrounds of partners impairs the ability of the partners to absorb and use valuable network
resources (Cohen & Levinthal, 1990; Phene, Fladmoe-Lindquist, & Marsh, 2006).
Informal institutional differences are difficult to fully perceive and recognize, making
their conscious accommodation within existing alliance routines uncertain (Nicholson, Stepina,
& Hochwarter, 1990; Park & Ungson, 2001). This is particularly relevant for tacit knowledge
transfer, such as management beliefs, experiences, and business-process development
(Ambrosini & Bowman, 2001). Increasing informal institutional differences hinders firms from
implementing firm-specific practices conducive to collaboration as cross-border partners’
informal institutional differences make their attitudes and approaches to work incompatible
(Björkman et al., 2007). In addition, Barkema and Vermeulen (1997) note that an elevated level
of informal institutional differences in cross-border alliances reduce the effectiveness of
collaboration, making the alliances less likely to survive. Inaccurate judgment of the factors
that hinder effective cooperation can trigger the application of unsuitable routines and
inappropriate business- and alliance-specific practices (Heimeriks, 2010). Exploration of
distant knowledge bases offered by one’s cross-border partners results in lower initial
performance (Barkema & Drogendijk, 2007).
The negative effect that informal institutional differences have on cross-border
alliances increases with the differences. As the differences grow, partners can develop a mutual
antagonism, at least up to an inflection point where the differences are large enough to draw
attention to themselves and prompt cooperation efforts that will mitigate the negative effects
and set in motion an effort to identify and leverage complementary skills and resources. Given
the established tendency of firms to pay attention to and react to salient events (Levitt & March,
1988), it is reasonable to assume that awareness of informal institutional differences will ignite
only when the differences are considerable.
Therefore, at an elevated level of informal institutional differences, firms are likely to
both recognize the value of network resources and facilitate cooperation to enhance the
assimilation and use of external knowledge, by investing additional time and alliance-specific
resources to manage those differences (Dyer & Hatch, 2006). Such efforts can include training
and monitoring (Shenkar & Zeira, 1992), consulting (Kale & Singh, 2007; Zollo & Winter,
2002), and targeted staffing (Hennart & Park, 1993; Shenkar, Luo, & Yeheskel, 2008). The
argument that informal institutional differences can lead to positive outcomes by creating
opportunities in firms cross-border activities has been noted (Stahl & Tung, 2015). Lew,
Sinkovics, Yamin, and Khan (2016) also find that considerable informal institutional
differences do not amount to a liability in cross-border technology transfers. Based on previous
literature and our arguments above we suggest that:
Hypothesis 1. The relationship between informal institutional differences and a firm’s
performance in cross-border alliances is sigmoid (S-shaped), with performance first
increasing, then declining, and finally increasing.
2.2. The impact of formal institutional differences
Formal institutional differences reflect the codified and explicit national variations in, for
example, employment regulations, intellectual property regimes, business systems, rules and
regulations, financial market operations, and fiscal and economic stability (North, 1990). Such
differences can impose formidable barriers to cross-border alliances. Significant differences in
the functioning of financial markets may introduce alliance conflicts, as partners will prioritize
different types of outputs and different time horizons for achieving them (Park & Ungson,
1997). Formal institutional diversity in the form of different legal systems gives rise to higher
transaction and coordination costs, making the use of contracts as a control mechanism costly
and ineffective (e.g., Chen & Chen, 2003). Such differences are also likely to inhibit the transfer
of business practices between partners and constrain a firm’s ability to absorb and use valuable
resources, by severely limiting its knowledge of distant partners’ resources and capabilities
(Kostova & Roth, 2002). The complexity and diversity inherent in regulatory, legal, and
economic factors have important implications for learning and coordination (Li, Qian, & Qian,
2012). Substantial administrative, regulatory, and legal differences between cross-border
partners lead to boundedly rational constraints on the management of alliances, increasing the
costs of accommodating such differences to alliance management practices (e.g., Rugman &
Verbeke, 2007). Owing to such dissimilarities, a firm’s ability to absorb and use valuable
resources and knowledge of institutionally distant partners becomes constrained, undermining
partners’ efforts to effectively share knowledge, adapt, and coordinate their value-adding
activities (Meyer, 2001; Slangen & Beugelsdijk, 2010; Tong, Reuer, & Peng, 2008). The
tangible and explicit attributes of formal institutions may allow partners to readily access
information pertaining to the requirements of formal institutional settings and thus help alliance
partners to negotiate the terms of their cooperation. However, this may be less relevant to cross-
border alliance partners from less compatible pairs of developed and developing nations, “less
compatible” with respect to formal institutional settings, as there are risks of undesirable
resource spillover and value misappropriation (Hamel, 1991; Lavie, 2006).
Based on previous
literature and our arguments above we suggest that:
Hypothesis 2. The relationship between formal institutional differences and a firm’s
performance in cross-border alliances is negative.
2.3. The firm’s cross-border experience
Thus far, we have argued that a firm’s performance is affected by the informal and formal
institutional differences inherent in its cross-border alliances. However, a firm’s capacity to
extract benefits from its alliances may also depend on its cross-border experience. A firm’s
accumulated cross-border experience provides experiential knowledge in bringing gaps and
identifying opportunities with partners from diverse informal and formal institutional contexts
(Cyert & March, 1963; Levitt & March, 1988). Firms need to learn about the institutions – both
informal and formal – in order to enhance the success of foreign operations (e.g., Barkema &
In relation to informal institutional differences, a firm’s cross-border experience,
specifically in forming and managing alliances, enables it to understand tacit differences and
We suggest a linear relationship for the formal institutions, whereas the impact of informal institutions is
nonlinear (S-shaped). The main reason for this difference is that the firm can take organizational
countermeasures when the informal institutional differences are large (training, etc.), whereas the same
countermeasures are less effective in relation to formal institutional differences, which are less in the control of
the firm (legal differences, etc.).
develop further means for exploring external opportunities arising from its alliances with its
partners (Lavie and Rosenkopf, 2006). We expect that a firm’s specific learning curve can
provide it with a broader mind-set and a greater ability to respond to informal institutional
differences and hence with institutional capital (e.g., Ethiraj, Kale, Krishnan, & Singh, 2005;
Johanson & Vahlne, 1977). Furthermore, prior research has identified cross-border experience
as a distinct alliance capability element to overcome informal institutional liabilities (Barkema,
Shenkar, Vermeulen, & Bell, 1997). This is also the logic behind the Uppsala model, which
acknowledges that firms accumulate knowledge over time as they learn to make necessary
adjustments in foreign markets (Johanson & Vahlne, 1977, 2009). Firms’ cross-border
experience can help alliance partners to overcome relational impediments due to informal
institutional differences. This in turn enhances the scope of shared values and goals that are
needed to elicit positive attitudes and facilitate social exchange in cross-border alliances
(Parkhe, 1991). Based on previous literature and our arguments we suggest:
Hypothesis 3a. A firm’s cross-border experience positively moderates the trajectory
relationship, described in Hypothesis 1, between the firm’s performance and informal
Although “psychic distance” is identified by Johanson and Vahlne as the key factor
behind experiential learning,
it precludes a consideration of specifically formal institutional
differences. Therefore, to address formal institutional differences, the firm can also use its
accumulated cross-border experience to decipher key elements of local formal institutions,
thereby facilitating resource sharing and reducing the costs of coordinating activities (Das and
Teng, 1998). Firms with limited cross-border experience have difficulty interpreting,
This composite factor later morphed into a singular capture of “cultural distance” (Shenkar, 2001).
understanding, and adapting to their cross-border partners’ formal institutions. Their own
formal institutional context and mechanisms constrain their ability (and perhaps willingness)
to change (Oliver, 1997). In such cases, firms have difficulty internalizing changes. By
contrast, firms with substantial experience with cross-border partners can identify differences
in formal institutional environments and learn how to utilize the comparative advantages
embedded in the formal institutions of their partners (Parkhe, 1991). They can search out
reliable partners, effectively anticipate contingencies, and design suitable contracts and other
bonding mechanisms to discourage opportunism (Simonin, 1997). Based on previous literature
and our arguments we suggest:
Hypothesis 3b. A firm’s cross-border experience positively moderates the relationship,
described in Hypothesis 2, between the firm’s performance and formal institutional differences.
We use data from the microfinance industry to test our hypotheses. We argue that this global
industry has several advantages for testing our hypotheses. Microfinance activities are quite
homogeneous across countries (similar technology and financial services are used around the
world), with an extensive matrix of interorganizational partnerships
exhibiting large variations
in informal and formal institutions between cross-border alliance partners across a large
number of heterogeneous countries and continents (Mersland et al., 2011).
These are arrangements between microfinance institutions (MFIs) based in the developing world and their
cross-border partners in the developed world.
This result holds even after controlling for economic differences.
assessable marginal performance impact from informal and formal institutional differences is
due to relatively uniform and transparent financial reporting within the industry (Beisland et
al., 2014). The utility-like nature of MFIs’ operations, with typically few head-on competitors
in the local market, enables the observation of suboptimal (costly) organizational arrangements.
The lack of a stock market for corporate control of MFIs,
and the fact that we do not study
conventional firm-to-firm arrangements but rather agreements involving a donor or investor on
the one hand and a funded organization on the other, imply that a suboptimal (“unprofitable”)
alliance arrangement, in terms of informal and formal institutional differences between the
partners, can be sustained over long periods. This in turn implies that we can observe larger
performance variations (extremes) than what can be expected in a regular for-profit context
with fierce competition.
We utilize data on 405 MFIs (the unit of analysis in this study) in 74 countries. The
MFIs were assessed from 1998 to 2010 by one of the five leading rating agencies specializing
in microfinance: MicroRate, Microfinanza, Planet Rating, Crisil, and M-Cril. The MFIs were
also assessed by professional third parties according to a transparency measure introduced in
the late 1990s by international policy agents like the CGAP, a specialized microfinance branch
of the World Bank (Beisland et al., 2014). A comparison of the five rating agencies’ rating
methodologies reveals no major differences between their variables and the variables we use
in our study, and thus supports the reliability of our dataset. Table 1 provides information on
the proportion of MFIs in our dataset. In addition, we use a dummy variable to account for
whether the MFIs in our dataset had cross-border alliances. The types of cross-border alliances
we consider range from MFIs being part of an international microfinance network, to MFIs
In fact, only around 10 MFIs are listed worldwide (Briere & Szafarz, 2015). Using data from unlisted
companies to study the research questions is in fact a strength of our study, considering the thin and sometimes
nonexistent capital markets in low-income countries.
having an international debt (commercial or subsidized), to MFIs having an international
partner – an investor or sponsor – who acted as the main initiator of the MFI See Appendix 1
for descriptive information on the type and content of the MFIs’ relationships with their cross-
border partners. The MFIs in our dataset take a number of legal and organizational forms but
all are either non-profit member-based cooperatives or for-profit shareholder-controlled firms.
We do not include other microfinance providers, such as central banks, small savings and credit
cooperatives, or development programs that offer microcredit solely for welfare.
It can be argued that our dataset has a certain sample selection bias, since only rated
MFIs are included. However, in practice, MFIs interested in engaging in cross-border
partnerships and accessing funding need to present an external rating report as a credential
before entering into negotiations. This applies in particular to younger MFIs without an
international reputation. The dataset thus represents internationally oriented MFIs with the
intention to practice microfinance in a business-oriented and transparent manner (Beisland et
al., 2014). Moreover, we argue that data from the MFIs’ rating reports have some distinct
advantages over the data from commonly used MFI databases (e.g., the general MIX Market
database: www.mixmarket.org). First, the data contain valuable information, e.g., on the MFI’s
international initiator and its network membership, that is unavailable from other sources.
Second, the data are not self-reported, as is the case in MIX Market; instead, a third party – the
rating agency –collects and verifies the data. Third, MIX Market data contain relatively more
information on very large MFIs, which are not subject to microfinance rating reports because
they are rated by traditional agencies such as Standard & Poor’s. Thus, the bias toward large-
sized MFIs in the MIX Market data is less prevalent in our dataset, which has a wide size
distribution (see Table 2). In addition, we employ random effects estimations that assume that
the unobserved heterogeneity error term is uncorrelated with each independent variable. We
also run Harman’s single factor test to detect common bias method as a source of endogeneity
(see Sharma, Yetton, & Crawford, 2009). It must be noted that our dataset is the up-to-date
version of a dataset used in several prior studies (e.g., Mersland et al., 2011; Mersland & Strøm,
Insert Table 1 and 2 about here
3.2. Dependent variable
We measure the MFIs’ financial performance (the dependent variable) in terms of the real
inflation-adjusted return on assets (ROA). We also use the ratio of operational expenses to
assets to measure financial performance (Mersland & Strøm, 2010). Whereas costs and income
drive the ROA, operational costs are of interest, as the competitive environment of the MFI
does not “distort” them. This is important since the competitive environment can vary
significantly from country to country (Assefa, Hermes, & Meesters, 2013). However, the
results for operating costs were in line with the results for ROA that we report in this paper.
The unreported results are available upon request. ROA is our main indicator of financial
performance because it “summarizes” an MFI’s financial success and has been used in prior
studies (e.g., Mersland et al., 2011).
We recognize that most MFIs operate with a “double bottom line” approach, striving
to achieve social returns as well as financial returns (Armendáriz & Morduch, 2010).
Interestingly, past research shows that the legal status of the organization, whether for-profit
or non-profit, does not impact its ROA (Mersland & Strøm, 2008). A common denominator
across MFIs is that they are all pushed in the direction of cost efficiency. Nevertheless, as
indicated by Mersland and Strøm (2010), the MFI’s main financial challenge is related to its
operational costs and financial performance, which are prerequisites for long-term social
Given the fact that only about 10 MFIs worldwide (Briere & Szafarz, 2015), and only
two in our dataset, are stock exchange-listed, we can’t use market-based performance measures
(e.g., Tobin’s q). In fact, the use of listed firms could potentially have brought in other biases
since capital markets in low-income countries, especially in non-Anglo-Saxon countries, are
often unrepresentative, thin, and sometimes nonexistent (as in the case of African firms; see
3.3. Independent variables
Culture is an important reflection of national informal institutions, representing shared values
and non-codified standards, and reflects a socially constructed reality shaping cohesion, logics
of action, and coordination among individuals within the society (North, 1990). Using
indicators of national cultural differences based on Kogut and Singh’s (1988) index, previous
studies have captured the role of informal institutions in raising obstacles to cross-border
alliances (e.g., Filiou & Golesorkhi, 2016). However, the use of cultural distance based on
Kogut and Singh’s index has raised widespread concern (Shenkar, 2001) about symmetry,
illusions of equivalence, and the adequacy of the statistical techniques used to construct and
validate Hofstede’s original dimensions of culture on which Kogut and Singh’s index is based,
among other things (Javidan, House, Dorfman, Hanges, & De Luque, 2006; Shenkar, 2001).
To address these concerns, we also apply two alternative cultural indices to capture informal
institutional differences: one based on the GLOBE study (House, Hanges, Javidan, Dorfman,
We also run a robustness check using a measure of social outreach performance (accounting for the dual
mission of MFIs) proxied by the average loan size, where a positive social outreach implies a lower average
loan size for MFIs. Although using average loan size as a proxy for poverty outreach has been criticized
(Armendáriz & Szafarz, 2011; but see Mersland et al., 2011), researchers have so far not come up with a better
alternative measure for social performance. The unreported results confirm our hypotheses and are available
& Gupta, 2004; Javidan & House, 2001) and the other based on the World Values Survey
(WVS). The GLOBE study shows nine indices based on recent surveys and uses contemporary
empirical techniques in their construction and validation (Javidan et al., 2006). The “practices”
indices of the GLOBE study are preferred to the “values” indices because MFIs’ cross-border
partners are more likely to be concerned with the informal institutional indices that they
actually encounter in the MFIs’ countries.
In addition, it has been argued that both Hofstede’s
study and the GLOBE study might capture marginal rather than absolute levels of values
(Maseland & Van Hoorn, 2009), which also suggests using the “practices” indices (Estrin et
In addition, we use the WVS’s cultural dimensions of traditional/secular-rational and
survival/self-expression (Inglehart & Baker, 2000; Inglehart, Basanez, Diez-Medrano,
Halman, & Luijkx, 2004) to construct an alternative informal institutional measure (e.g.,
Brouthers et al., 2008; Salomon & Wu, 2012). This choice was based on the sampling,
timelines, scale, and validity of the WVS in its focus on normative and cognitive national
culture. Compared to Hofstede (2001) and House et al. (2004), the WVS captures the national
cultural characteristics of the overall population of a country, instead of just those of managers
of corporations. Given the complexity of the concepts that are measured under the umbrella of
national culture, the more diverse set of respondents sampled by the WVS may provide
additional information. Furthermore, the WVS has been applied to a wider range of fields, such
as institutional economics (e.g., Tadesse & White, 2008), sociology (e.g., Curtis, Baer, &
Grabb, 2001), and international business (e.g., Salomon & Wu, 2012), establishing it as a
validated and reliable construct.
We obtain similar results using “value” indices.
Overall, in operationalizing informal institutional differences, we measure informal
institutional differences by using cultural differences between alliance partners’ countries of
origin. We use Kogut and Singh’s (1988)
index of cultural distance – based on Hofstede’s
(1980) four dimensions of culture: uncertainty avoidance, individuality, tolerance of power
distance, and masculinity-femininity
– as a robustness test in Appendix 2. We then calculate
each indices of cultural differences based on the GLOBE study, using the formula
/n , where EC
is the measure of the k-th GLOBE cultural indicator, for k=1 to 9,
c is cross-border partner j’s country of origin, m is the MFI’s country of origin, and n is the
number of partners involved with MFI i in year t. The methodological concerns related to the
sigmoid nature of the relationship tested by the informal institutional differences
we have to reduce the number of variables (Hair, Babin, Money, & Samouel, 2006). Therefore,
we conduct a principal component analysis (PCA) of the informal institutional differences
based on the nine indicators of GLOBE with varimax rotation. The exploratory factor analysis
suggests that the theoretical constructs indeed load onto one factor. The one-factor solution
also shows a high level of reliability with a Cronbach alpha of 0.886 and an eigenvalue of
6.116. Various debates can be found in the literature about how many factors are to be retained
(Hair et al., 2006). According to the Kaiser criterion, eigenvalues >1 should be retained as
Kogut and Singh (1988) designed an overall index that defines the cultural differences between a given nation
and other nations as follows: (cultural distance
4/ V/)( 2iiuij II −
We acknowledge that recent discussions in the literature question such a notion of distance and argue that
friction better captures the impact of informal institutional differences (Shenkar, 2001; Shenkar, Luo, &
Yeheskel, 2008). Nevertheless, empirical studies invariably employ distance-based constructs and measures and
their findings indicate that the various proxies for such differences are broadly consistent (e.g., Dow & Larimo,
2011; Estrin et al., 2009).
These include high multicollinearity, the degree of freedom in the regression models, and the correlated
nature of informal institutional indices.
separate factors. However, this criterion might underestimate the number of factors (Hair et al.,
2006). Given that the result of this factor analysis is in line with previous studies (e.g., Gaur &
Lu, 2007; Mitton, 2008), one single solution seems suitable. Similarly, we use the same
approach to operationalize informal institutional differences based on the WVS by subtracting
the MFI’s WVS aggregated score from its cross-border partner’s score.
We use items selected from the economic freedom index developed by the Heritage
Foundation, as indicators of the presence of formal institutions and the openness of the
institutional environment (Berggren & Jordahl, 2005; Meyer, Estrin, & Bhaumik, 2009; Stroup,
2007). The index provides aggregated annual values, including evaluations of countries in
terms of business activity, trade, investment, labor markets, financial freedom, corruption,
property rights, and the like. This index is highly correlated with other proxy measures, such
as the Global Competitiveness Report (World Economic Forum) and World Bank database
indicators (Hanke & Walters, 1997; Berger & Bristow, 2009). Following insights from Lavie
and Miller (2008), we compute formal institutional difference measures, using the formula
EI −EI │/n , where EI is the measure of the k-th indicator, for k=1 to 10, c is
cross-border partner j’s country of origin, m is the MFI’s country of origin, and n is the
number of partners involved with MFI i in year t. For each MFI in our sample, we determine
its cross-border partners’ identities and their countries of origin. We then construct a composite
measure based on the factor score derived from the 10 indicators, which we found to be highly
The literature also indicates that formal institutional indicators generally tend to
overlap with each other (Mitton, 2008; Dow & Larimo, 2011). We use principal components
The correlation matrix is available upon request.
and factor analysis with varimax rotation, which produced a single factor score with an
eigenvalue of 7.82 and a standardized Cronbach’s alpha of 0.91.
The final tested independent variable is the MFI’s cross-border experience. As stated
previously, the MFIs in our dataset have been assigned international ratings, and the industry
is highly global, with most MFIs having had some form of cross-border support since start-up,
as noted in Appendix 1. Therefore, we use as a proxy for an MFI’s cross-border experience the
cumulative number of years since it started its microfinance activities, lagged by one year
(Lavie, Kang, & Rosenkopf, 2011). Following past research recognizing that the marginal
value of each incremental unit of experience declines as overall experience increases, we
transform this variable into its natural logarithm (e.g., Goerzen & Beamish, 2005). Capturing
cross-border experience with an MFI’s age is a reflection of the extent of the
internationalization of this industry (Mersland et al., 2011) and in particular the MFI
founder(s’) role in creating cross-border connectivity (Randøy, Strøm, & Mersland, 2015). As
a robustness check we test our hypotheses on a subsample of MFIs with only cross-border
initiators (indicating the MFI’s age to be equivalent to the cross-border influence and
experience of its founders from its inception). The results confirm our predicted hypotheses.
To further isolate the effects of informal and formal institutional differences on MFIs’
performance, we control for the diversity of cross-border partners’ countries of origin (Goerzen
& Beamish, 2005; Lavie & Miller, 2008), using an inverted Herfindahl index. For each MFI i
in year t, we use the formula 1−
(n /n ) , where n is the number of partners of
MFI i that originate from country c, and n is the total number of cross-border partners of MFI
i in year t. This composition demonstrates the dominance of developed economies as MFIs’
Unreported results are available upon request.
cross-border partners. A high value for this measure would suggest that an MFI’s partners were
globally dispersed. Figure 1 shows the distribution of MFIs’ cross-border partners’ countries
of origin. We control for whether MFIs have a shared language with their cross-border partners
using a dummy variable, in line with the argument that it is a one-off effect. The dummy
variable is assigned a value of 1 when MFIs and their cross-border partners have a shared
language and 0 otherwise.
We also apply the following organization-specific MFI control variables that have been
included in recent microfinance performance research (Cull, Demigüc-Kunt, & Morduch,
2007; Mersland et al., 2011): type of ownership, assets (size), and whether or not assets are
regulated by banking authorities. This information is from rating reports, i.e., the main data
source we use. Further, given the high degree of variation in the economic environments of our
MFIs’ countries of origin, we use country variables to reduce misspecification of MFIs’
performance (e.g., Mersland & Strøm, 2010). This includes the country’s human development
index (HDI), which is a composite country index covering life expectancy, education, and
income (GDP per capita). HDI and GDP per capita are taken from World Bank and United
Nations Development Program, respectively. Table 3 provides a summary of all the variables.
Insert Table 3 and Figure 1 about here
Table 4 provides descriptive statistics for the variables used in the analysis, and the correlation
matrix; see also the variance inflation factors of the baseline models in Appendix 3. None of
the correlation coefficients is of high magnitude (Kennedy, 2008).
The MFIs’ mean values
of informal and formal institutional differences (GLOBE) in cross-border alliances are 2.05
and 14.36, respectively. Table 5 reports the generalized least squares estimation in the panel
data, with missing values subject to list-wise deletion, and ROA as the dependent variable. We
chose the random effects model due to the nature of the study variables, which are mainly time
invariant, and because our robustness check (Hausman, 1978; test Prob>chi2 = 0.055) revealed
random effects to be appropriate to test the effects of informal and formal institutional
differences on the MFIs’ performance. Models 1 and 5 are the baseline models with the linear
terms of the GLOBE and WVS measures, respectively. We test Hypothesis 1 (using the
GLOBE and WVS measures) and Hypothesis 2 in Models 2 (6) and 3 (7), in which we test for
a sigmoid relationship between informal institutional differences and MFIs’ performance,
respectively, by adding GLOBE and WVS squared terms in Model 2 (6) and GLOBE and WVS
cubic terms in Model 3 (7). Hypothesis 3a and Hypothesis 3b are tested by introducing the
interaction effect of cross-border experience on informal institutional differences (the GLOBE
and WVS measures) as well as on formal institutional differences in Model 4 (8) respectively.
Models 9 and 10 serve as the full models with the GLOBE and WVS measures, respectively.
We conclude with Wald tests on the significance of each model against the baseline models.
Insert Tables 4 and 5 about here
It must be noted that we trim outliers from the dataset. For example, MFIs with more than 50 years of
experience were removed from our dataset, given that MFIs are nearly all young organizations; our observations
center on MFIs with almost ten years of experience on average.
Models 1 and 5 indicate that informal institutional differences have a negative and significant
effect on MFIs’ performance. While both the GLOBE and WVS measures are significant at 5
percent, we expect a nonlinear relationship to better capture the effect of informal institutional
differences on MFIs’ performance at each level of difference. The joint test of the linear,
squared, and cubic terms of informal institutional differences, as demonstrated by the GLOBE
and WVS measures, are significant at the 1 (5) percent level in Model 3 (7). This supports the
hypothesized sigmoid relation between informal institutional differences and MFIs’ financial
performance. In addition, the Wald chi-square statistic indicates that the inclusion of the cubic
terms significantly improves our model’s fit. Models 9 and 10 (the full models) also confirm
these results. Overall, these results support Hypothesis 1, with a significant effect of the
positive linear term, the negative squared term, and the positive cubic term. Our estimated
relationship suggests that the minimum ROA level is at the 2.150 point of informal institutional
differences (GLOBE), which corresponds to a negative ROA of 0.450 and the maximum ROA
at the 0.971 point of informal institutional differences, which corresponds to a positive ROA
of 0.236 (see Figure 2).
To contextualize our results, the informal institutional difference
between an MFI from, say, Mexico and a French cross-border partner is 2.363, while the
difference between a Mexican MFI and a US partner is 1.136. Our results suggest that, at low
levels of informal institutional differences, MFIs are better able to reap the benefits of exposure
to different cultures, due to the tacit and elusive nature of informal institutional characteristics
that makes subtle differences difficult to decipher and acknowledge. At higher levels of
informal institutional differences, our interpretation is that MFIs become more aware of the
sources of difference while being unable, or unwilling, to redress the moderate negative effect,
while at the highest levels of differences MFI are willing to make explicit investments in
The graph of the WVS on the MFI’s performance also confirms the hypothesized S-shaped pattern.
alliance management practices and other resources to enhance financial performance returns
from cross-border alliances.
Our findings is in line with the international business literature,
highlighting that the learning capacity of the firm will be greatest when the overlap between
the firms’ cultural knowledge is fairly large, yet small enough to stimulate learning (e.g.,
Barkema & Drogendijk, 2007). Furthermore, our results are also in line with the organizational
learning literature, suggesting that firms that move away from their knowledge-base of
experience could encounter short-term performance decline, however, enhanced learning and
better performance in future expansions (e.g., Levinthal, 1997; Gavetti & Levinthal, 2000). In
all the models discussed above, we find support for Hypothesis 2 in the negative and significant
relationship between formal institutional differences and MFIs’ financial performance (at 1 and
5 percent levels). We believe that this negative relationship is caused by the costs of forming
cross-border alliances with partners embedded in very different formal national institutions,
and that it is also reflected in the average formal institutional difference the MFIs confront with
their cross-border partners, namely, 14.36 in Table 4. MFIs’ investments may increase with the
customization of products and technologies to match cross-border partners’ banking
preferences and standards from the developed countries. Filiou and Golesorkhi (2016) also find
that increased formal institutional differences have a negative impact on firms’ innovation
returns from cross-border alliances. Furthermore, the risk of undesirable resource spillover and
misappropriation of value (Hamel, 1991; Lavie, 2006) increases with the disparity in economic
and financial development. National institutions affect transaction costs and the efficiency of
the business exchanges in MFIs’ cross-border alliances, and such institutions are seen as the
main driver of MFIs’ financial stability (Ahlin, Lin, & Maio, 2011). We also find support for
We conduct an extensive set of robustness tests, such as testing our hypotheses on random samples and
running semiparametric regressions for panel data. The unreported results of these robustness tests provide
further support for our hypothesized relationships between informal institutional differences and MFIs’
performance returns from cross-border alliances.
Hypothesis 3a and Hypothesis 3b, revealing two positive and significant (at 5 and 1 percent
levels) interaction effects from MFIs’ cross-border experience on informal and formal
institutional differences, using models with the same interaction terms as in Model 4 (8). These
results support the argument that organization-specific learning from accumulated cross-border
experience contributes to MFIs ability to bridge informal and formal institutional differences
with its cross-border alliance partners, hence improving MFIs’ financial performance (e.g.,
Barkema & Drogendijk, 2007, Barkema et al., 1997). Figure 3 depicts this relationship.
Insert Figures 2 and 3 about here
Turning to the performance effects of the control variables, shown in Table 5, first, the variable
attesting to the diversity of the cross-border partners’ countries of origin exhibits a negative
and significant sign at the 5 percent level in Model 2. This indicates that as the number and
diversity of cross-border partners in an MFI’s portfolio of alliances increase, the ability of the
MFI to coordinate and access its partners’ networks diminishes. However, our results also
suggest that an MFI’s cross-border experience can improve its learning and absorptive
capabilities for managing informal and formal institutional differences, enhancing performance
returns from cross-border alliances. Regulation of the MFIs by banking authorities has a
negative and significant impact, at the 5 percent level in Model 1 and 5. We find no significant
impact of the MFI’s ownership type on its performance, which is in line with previous
microfinance research indicating that type of ownership has negligible impact on MFI
performance (e.g., Mersland & Strøm, 2008). In most models, the MFI’s size (proxy for its
assets) exhibits a positive and significant effect on its performance, which indicates the
existence of organizational scale economies in microfinance banking, as previously reported
by Hartarska, Shen, and Mersland (2013). We also find a positive and significant effect of the
language variable at the 5 percent level in Models 1, 5 and 8, suggesting that MFIs enhanced
their performance by sharing a language with their cross-border partners. This is in line with
international business research where it has been found, for example, that a shared language
improves the absorptive ability of firms’ employees to share globally relevant company
information such as technological development, financial data, health and safety procedures, and
employment conditions (e.g., Piekkari, Welch, & Welch, 2014). Finally, we find a low HDI for
the MFI’s home country negatively affects the MFI’s performance at the 5 percent significance
level in Models 5 and 10. This finding illustrates the challenges involved in operating
businesses in poor countries. Overall, the results from Table 5 show the regression
specifications to have acceptable explanatory and predictive abilities.
5. DISCUSSION AND CONCLUSION
In this study we examine financial performance returns from cross-border alliances from an
institutional perspective. We highlight that the nature of institutions, both formal and informal,
and the extent of informal institutional differences are important in understanding the
intricacies of cross-border alliances and firms’ financial returns. We find support for the notion
that informal and formal institutions are of an implicit and explicit character, respectively, with
different impacts on partners’ abilities to address and negotiate such institutional differences
with their cross-border alliance partners. Our study affirms Shenkar’s (2001) contention that
the impact of informal institutional differences can be nonlinear. We also show that this pattern
does not extend to differences between formal institutions, reaffirming the dissimilar nature of
informal and formal institutional differences, as claimed in both classical sociology and
institutional economics. We argue that the theoretically motivated and observed sigmoid
pattern of the relationship between informal institutional differences and firm performance
enriches the existing literature on cross-border alliances.
Furthermore, we provide empirical support for the notion that informal institutional
differences can have marginal positive as well as negative effects, compounded as a net
sigmoid effect. This notion is motivated by the fact that cross-border alliances can help MFIs
to access possible complementarities or value-adding resources, as well as be a source of
disruption to MFIs (Stahl & Tung, 2015). Specifically, we argue that the realization of positive
as well as negative outcomes depends, among other factors, on the extent of the observed
We demonstrate that firms can leverage their cross-border experience to moderate the
performance impact of informal and formal institutional differences. We suggest that firms can
capitalize on experiential learning to form and manage cross-border alliances, specifically
utilizing institutional experience and capabilities (Barkema et al., 1997; Cyert & March 1963).
To our knowledge, our study is among the first to consider the impact of organizational
experience on institutional heterogeneity. Our study hypothesizes and observes positive
moderating effect of firms’ cross-border experience on both informal and formal institutional
differences, which extends the findings of previous studies in this area (e.g., Lavie & Miller,
2008; Kale & Singh, 2007). Another relevant study is that of Hall (1959, p. 156) who builds an
experience-based model of institutional adjustment. Our findings are particularly appealing
to firms based in developing countries, whose accumulated cross-border experiences are
commonly less developed. Experiential knowledge could assist such firms in establishing a
range of alliance routines to manage cross-border alliances and to overcome potential frictions
and coordination problems due to institutional differences. In turn increasing the potential
financial sustainability of cross-border alliances and subsequent social outreach.
It is a commonly observed fact that cross-border partnerships between
organizations in the developed world and partners in the developing world are on the rise
and our study addresses this phenomenon. Specifically, our study has
managerial implications for strategy-making, institutional adaptation, and international
business, as called for by Jennings, Greenwood, Lounsbury, and Suddaby (2013). A first
implication of our study is that “informal institutional due diligence” may not be sufficient
for handling cultural gaps, and that firms should put continuous monitoring in place in order
to identify inflection points (Rothaermel & Deeds, 2006). A second managerial implication
is that enhancing performance by accessing diverse knowledge bases in cross-border
alliances depends on the type of institutional differences being studied. This suggests a need
to pay close attention to such differences as a criterion for selecting cross-border alliance
partners as well as for a criterion developing adaptations to such differences as an important
alliance capability (e.g., Hitt, Ahlstrom, Dacin, Levitas, & Svobodina, 2004). Thus, we
recommend a more informal institutionally targeted approach when cross-border actors seek
partners in developing countries. We argue that an institutional perspective on MFIs’
partnerships can enhance our understanding of what drives their cross-border alliance
In acknowledging the debate on the constructs of informal and formal institutional
differences (Berry, Guillen, & Zhou, 2010; Brewer & Venaik, 2011; Luo & Shenkar, 2011),
we have attempted to identify informal and formal institutional differences by means of
disaggregating them. However, in identifying the effect of each individual index of informal
and formal institutions, we have faced challenges of conceptualization and methodology. In
The Economist, “Democracy in America,” April 3, 2014.
future research, it may be worthwhile to explore new directions of how to measure and
disaggregate individual indices of institutions (e.g., Dow & Larimo, 2011). For instance, it
may be worthwhile to explore the extent to which informal institutions share similarities with
the concept of culture, in order to compare whether these constructs produce similar or
divergent results. Another possible fruitful avenue for future research is to explore the
motivations underlying the formation of cross-border alliances (especially between partners
in very dissimilar countries), as well as in what direction and by what mechanism knowledge
is transferred between partners.
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Appendix 1: MFIs’ cross-border dimensions: Descriptive statistics
Mean Std. Min Max
International initiator 0.38% 0.485 0.000 1.000
International commercial debt 0.41% 0.491 0.000 1.000
International subsidized debt 0.51% 0.500 0.000 1.000
International network member 0.33% 0.471 0.000 1.000
The descriptive statistics for MFIs’ cross-border partners in Appendix 1 show that as many as
38% of MFIs have an international initiator, 41% have an international commercial debt,
51% have an international subsidized debt, and 33% are members of a recognized
Appendix 2: The test for a sigmoid relationship between informal institutional differences
and MFIs’ financial performance returns from cross-border alliances based on Kogut and
Singh’s index and the uncertainty avoidance dimension of Hofstede.
We computed the informal institutional difference, based on the Kogut and Singh’s (1988)
index (henceforth referred to as KS) to provide a comparison with our other measures of
informal institutional differences reported in Table 5. In addition, in acknowledgment of
Shenkar (2001), who argues for the disaggregation of cultural dimensions, we also calculated
the difference between MFI m and its cross-border partner’s country cj as the absolute
differences of each of Hofstede’s dimensions: power distance, uncertainty avoidance,
individuality, masculinity, femininity, long-term/short-term normative orientation, and
indulgence/restraint. Our preliminary results highlighted that only one indicator was
significant, namely, uncertainty avoidance indicator (henceforth referred to as UAI). Therefore,
we also tested the sigmoid pattern of H1 using this indicator. Overall, the results confirm the
sigmoid relationship between informal institutional differences and MFIs’ financial
performance from cross-border alliances. Results of the random effects model with ROA as
the dependent variable and KS and UAI variables are reported below.
Formal inst. diffs.
MFI cross-border exp.
Square values of KS
Cubic values of KS
Square values of UAI
Quadratic values of UAI
Experience* formal inst. diffs.
Wald chi square
Wald test chi square
This table tests our hypotheses by using Kogut and Singh’s index (KS) to capture informal institutions. Models 1 and 5 are the baseline models with linear terms of KS and
UAI measures, respectively. We test H1 (using KS and UAI measures) and H2 in Models 2 (6) and 3 (7), in which we test for a sigmoid relationship between informal
differences and MFIs’ performance, respectively, by adding KS and UAI squared terms in Model 2 (6) and KS and UAI cubic terms in Model 3 (7). H3a and H3b are tested
by introducing the interaction effect of experience on informal institutional differences (KS and UAI measures) as well as on formal institutional differences in Model 4 (8).
Models 9 and 10 serve as the full models with either KS or UAI measures, respectively. ***, **, and * denote statistical significance at 1%, 5%, and 10%, respectively.
Standard errors appear in parentheses. The reduced number of MFIs from the original dataset of 405 reflects the missing values subject to list-wise deletion due to factors
such as: use of different informal institutional constructs; rating agencies reporting a different number of variables; and/or whether MFIs have cross-border partners.
Variance inflation factor (VIF) based on baseline Model 1 (5)
Formal inst. diffs.
MFI’s cross-border exp.
Table 5 of Appendix 3 reports the results of variance inflation factors of the baseline models. Testing for
potential multicollinearity indicates that the maximum variance inflation factor in the full models (Models 9 and
10) is relatively high (Kleinbaum, Lawrence, Muller, & Nizam, 1998). We can attribute this to multiple
occurrences of the GLOBE and WVS measures in the independent variables and interactions. Although VIFs
fell to conventional levels when the quadratic and cubic terms were dropped, we did not find any symptoms of
multicollinearity in the full models (Maddala, 2001).
Table 1: The frequency and percentage of MFIs in our dataset
Trinidad and Tobago
Rep. of Congo
DRC - Kinshasa
Table 1 provides information on the proportion of MFIs in our database based on their
frequency and percentage.
Table 2: Comparing data from MIX Market and rating reports (our data)
Variables MIX Market (2006), 704 MFIs Rating reports, 405
Mean Median Mean Median
Age (years) 12 9 9 8
Total assets (USD) 45,566,650 6,169,918 6,348,701 2,672,081
Total staff (#) 400 94 85 49
# Active loan clients 73,564 10,102 12,543 4,878
Gross loan portfolio (USD) 33,072,688 4,438,677 4,276,508 1,972,629
Average outstanding loan (USD) 1,026 456 602 387
Table 2 documents the data used in this study, which is based on rating reports from five
independent rating agencies. For comparison, we have also compared our data to data in the
publicly available MIX Market database (https://www.themix.org/mixmarket/datasets),
which is supported by World Bank.
Table 3. Definition of variables
Return on assets (ROA)
Operational net income divided by
average annual assets and adjusted
for country inflation
Formal inst. diffs. Indicators
include business, trade, fiscal,
and investment, and financial,
monetary, labor freedom,
freedom from corruption,
property rights, and
│EI −EI │/n
We used principal components factor
analysis with varimax rotation, which
produced a single factor score
GLOBE’s nine indices:
orientation, power distance,
and uncertainty avoidance
We used principal components factor
analysis with varimax rotation, which
produced a single factor score
Aggregated measures from the
World Values Survey (Inglehart et al.,
2004): traditional authority vs. secular-
rational authority; survival values vs. self-
expression values. We calculated the
difference between MFI m and its
international partner’s country cj:
MFI’s cross-border exp.
The natural logarithm of the years
(lagged by one year) since the MFI started
its microfinance operations
MFI control variables
Diversity of MFI’s cross-
border partners’ countries
(n /n )
Indicates MFI’s type of cross-border
alliances in terms of whether (1) the MFI is
a member of an international network, (2)
an international partner was active in
initiating the MFI, and/or (3) the MFI has
international debt (subsidized or
Indicates whether the MFI has a shared
language with its cross-border partner
Indicates whether the MFI is a shareholder
firm or a non-profit firm (we grouped
NGOs and cooperatives under non-profit
firms and non-bank financial institutions
and banks under shareholder firms)
Shareholder= 1, Non-profit=0
Whether or not the MFI is regulated by
The natural logarithm of the MFI’s assets
Human development index
A composite country index covering life
expectancy, education, and income (GDP
Table 3 describes the variables used in this study.
Table 4: Descriptive statistics and bivariate correlation matrix
2 Formal inst. diffs.
3 KS index
7 MFI cross-border exp.
8 Cross-border MFI
9 Cross-border diversity
Table 4 provides descriptive statistics (mean and standard deviation) for variables used in this study. Significance level: two tails (* p<0.01). To save space, the individual
formal institutional and GLOBE indices are not reported.
Table 5: Results of random effects model with ROA as the dependent variable
Formal inst. diffs.
MFI cross-border exp.
Square values of GLOBE
Cubic values of GLOBE
Square values of WVS
Quadratic values of WVS
MFI cross-border exp.* formal inst. diffs.
MFI cross-border exp.* GLOBE
MFI cross-border exp.* WVS
Wald test chi-square
Table 5 exhibits the models for testing our hypotheses. Models 1 and 5 are the baseline models with linear terms of GLOBE and WVS measures, respectively. We test H1
(using GLOBE and WVS measures) and H2 in Models 2 (6) and 3 (7), in which we test for a sigmoid relationship between informal differences and MFIs’ performance, by
adding GLOBE and WVS squared terms in Model 2 (6) and GLOBE and WVS cubic terms in Model 3 (7). H3a and H3b are tested by introducing the interaction effect of
experience on informal institutional differences (GLOBE and WVS measures) as well as on formal institutional differences in Model 4 (8). Models 9 and 10 serve as the full
models with either GLOBE or WVS measures, respectively. ***, **, and * denote statistical significance at 1%, 5%, and 10%, respectively. Standard errors appear in
parentheses. The reduced number of MFIs from the original dataset of 405 reflects the missing values subject to list-wise deletion due to factors such as: use of different
informal institutional constructs; rating agencies reporting a different number of variables; and/or whether MFIs have cross-border partners.
Figure 1: Distribution of MFIs’ cross-border partners by country of origin.
Figure 2: Sigmoid effect of informal institutional differences (GLOBE) on MFIs’ performance (Model 9). In
this figure, the variables of interest (ROA and GLOBE) are represented in standard deviation units, while all
remaining variables are kept at their mean levels.
Figure 3: The moderating effect of cross-border experience on MFIs’ performance (Model 9). This figure
illustrates how the effect of informal institutional differences on the MFI’s financial position shifts with one
standard deviation (+/-) change in the MFI’s cross-border experience.
Moderating effect of cross-border exp. on
Moderating effect of cross-border exp. on