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Social enterprises are hybrid organizations that concurrently pursue social and economic goals and hence are mid-way between conventional capitalistic firms and non-profit organizations. Many social enterprises are becoming international; delivering services across borders. With the objective of understanding the internationalization of these unconventional organizations, this paper examines their international market selection decision based on host countries’ macroeconomic conditions. Generally, we hypothesize that the international market selection decision of social enterprises is tied to their hybridity, an overarching characteristic that sets them apart from other types of organizations. We build an original dataset with information on 41 European and North American impact investing organizations and 153 developing countries. Largely, our findings support the hypothesis, suggesting that social enterprises operate in foreign countries that offer a desirable balance between their social and financial goals. However, they avoid contexts with high country risk, factors that could cause a shortfall in expected returns.
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A Hybrid Approach to International Market Selection:
The Case of Impact Investing Organizations
Accepted for publication in
International Business Review
https://doi.org/10.1016/j.ibusrev.2019.101624
Roy Mersland
University of Agder, School of Business and Law; Universitetsveien 19, 4604 Kristiansand,
Norway. Phone: +47 38 14 17 87; Email: roy.mersland@uia.no
Samuel Anokye Nyarko*
University of Agder, School of Business and Law and Université Libre de Bruxelles (ULB),
SBS-EM, CEB, and CERMi; Avenue F. D. Roosevelt 50, 1050 Brussels, Belgium
Phone: +32 2 650 4126 ; Email: samuel.nyarko@ulb.ac.be / samuel.nyarko@uia.no
Amila Buddhika Sirisena
University of Agder, School of Business and Law; Universitetsveien 19, 4604 Kristiansand,
Norway; Phone: +47 38141266; Email: amila.b.sirisena@uia.no / amila@badm.ruh.ac.lk
* Corresponding author
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Abstract
Social enterprises are hybrid organizations that concurrently pursue social and economic goals and hence
are mid-way between conventional capitalistic firms and non-profit organizations. Many social
enterprises are becoming international; delivering services across borders. With the objective of
understanding the internationalization of these unconventional organizations, this paper examines their
international market selection decision based on host countries’ macroeconomic conditions. Generally,
we hypothesize that the international market selection decision of social enterprises is tied to their
hybridity, an overarching characteristic that sets them apart from other types of organizations. We build
an original dataset with information on 41 European and North American impact investing organizations
and 153 developing countries. Largely, our findings support the hypothesis, suggesting that social
enterprises operate in foreign countries that offer a desirable balance between their social and financial
goals. However, they avoid contexts with high country risk, factors that could cause a shortfall in
expected returns.
Key words: Cross-Border Investments, Internationalization, Social Enterprises, International Market
Selection, Macroeconomic Factors, Hybrid Organizations
JEL classifications: F23, G21, L31
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1. Introduction
The business of doing good, or what Miller, Grimes, McMullen, and Vogus (2012, p. 616) term
venturing for others with heart and head,has become popular. Across the globe, social enterprises are
gaining momentum. In the Netherlands, for example, the social enterprise sector grew by more than 70%
during the period 2010 to 2015 (Keizer, Stikkers, Heijmans, Carsouw, & Aanholt, 2016). Faced with
demographic changes and financial crises, governments and the general public have high hopes in social
enterprises because these firms promise to address social problems without the need for long-term public
(or private) subsidies (Zahra, Gedajlovic, Neubaum, & Shulman, 2009). According to Doherty, Haugh,
and Lyon (2014), social enterprises are organizations that strive to achieve desirable social goals, e.g.,
reducing unemployment, hunger and poverty eradication, while maintaining their financial sustainability.
Thus, by pursuing social and financial goals at the same time, social enterprises are hybrid organizations
that couple dual institutional logics―social and economic (Battilana & Dorado, 2010).
Three characteristics distinguish social enterprises from pure philanthropic organizations and capitalistic
firms. The first is their hybridity which stem from the simultaneous pursuit of social and financial
objectives (Battilana & Dorado, 2010). This is perhaps the most overarching and distinct feature of social
enterprises as both social and economic value creation is core to them (Peredo & McLean, 2006).
Hybridity is also the main source of tension in social enterprises since social and financial logics often
conflict (Wry & Zhao, 2018). Second, social enterprises must be financially self-sustainable, implying
that they must be able to generate income to cover their costs without donor support (Mair & Marti, 2006;
Townsend & Hart, 2008). Social enterprises do so by operating with conventional business models in the
delivery of their products and services at the marketplace. Due to their mission orientation and the low
economic status of their clients (less privileged people), social enterprises may not charge competitive
prices for their products and services. Yet, prices must be high enough to break even at least. This
explains why achieving financial sustainability is tricky for most social enterprises (Doherty et al., 2014).
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Third, social enterprises fill institutional voids that are unattended by governments and the market (Zahra
et al., 2009). Thus, social enterprises supply products and services that are unavailable in conventional
sectors due to resource constraints faced by governments and other private actors. Such voids are usually
costly and unprofitable to fill, a reason for their neglect by the market.
In addition to the global popularity of social enterprises, we have in recent years observed a significant
increase in cross-border operations by these hybrid firms (Porter & Kramer, 2011). These cross-border
activities can be global or regional (McKague, Menke, Arasaratnam, 2014; Wang, Alon, & Kimble,
2015). In some instances, pro-social organizations incorporated in western countries expand their
developmental interventions into developing countries either directly or through support-based
partnerships with local organizations (Golesorkhi, Mersland, Piekkari, Pishchulov, & Randøy, 2019;
Golesorkhi, Mersland, Randøy, & Shenkar, 2019). In most cases, such collaborations involve the
transfer of personnel, knowledge and international best practices.
Despite the burgeoning literature, the internationalization of social enterprises has received only a paucity
of scholarly attention (Pless, 2012; Zahra, Rawhouser, Bhawe, Neubaum, & Hayton, 2008) and until now,
no study has, to the best of our knowledge, investigated the international market selection decisions of
social enterprises. We aim to contribute to the literature by explaining the international market decision of
social enterprises based on the macroeconomic conditions of host countries. The study also contributes to
our understanding of hybrid firms, an understanding which has been a standing call in many previous
studies (e.g. See, Battilana et al., 2015; Doherty et al., 2014; Pache & Santos, 2013; Smith et al., 2013).
We set out to address the following research question: Into which macroeconomic environment do social
enterprises go when investing abroad? We argue that host country macroeconomic conditions have a
direct bearing on the ability of hybrid firms to balance the trade-off between their social and financial
goals (Ault & Spicer, 2014; Hermes, Lensink, & Meesters, 2011; Smith, Gonin, & Besharov, 2013). For
hybrid organizations, the core need to make a social impact distinguishes their internationalization
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process from those of mainstream firms. Therefore, conventional theories on internationalization may not
be sufficient to understand the cross-border operations of social enterprises (Peredo & McLean, 2006).
To answer the research question, we use an original dataset comprising of data from 41 impact investing
organizations that originate from Europe and North America. Generally, impact investing organizations
invest with a dual motive: generating social impact and earning financial returns (Ashta, 2012). By
aiming to concurrently achieve both objectives, impact investing firms are faced with trade-offs because
these polar goals can conflict (Glac, 2009). For their desired social goal, the impact investing
organizations in our dataset contribute to fighting global poverty by providing finance as well as a wide
range of non-financial assistance to local microfinance institutions (MFIs) in developing countries. MFIs
are specialized organizations that are known for alleviating poverty through the provision of banking
services to marginalized and disadvantaged persons with income generating activities (Armendáriz &
Morduch, 2010). Previous studies have shown that many MFIs rely on their partners in the global
North―mostly impact investing organizations―for financing and technical solutions (Mersland, Randøy,
& Strøm, 2011; Mersland & Urgeghe, 2013). At the same time, being double bottom line organizations,
the impact investors in our dataset equally aim at earning financial returns on their investments in the
MFIs. In addition to the data on the impact investing organizations, we also gather macroeconomic data
on 153 developing countries.
Based on existing literature on hybrid organizations, we generally hypothesize that social enterprises, in
our case impact investing organizations, are likely to internationalize into countries where they have the
opportunity to balance the competing demands of their dual institutional logics. Thus, social enterprises
will target countries that are less developed, institutionally weak, and risky, but not countries where these
macroeconomic indicators are at the worst levels. Largely, our empirical investigation supports this
hypothesis.
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In sum, it appears that when going abroad, the average impact investing organization makes an optimum
choice by selecting countries that offer a desirable balance in the trade-off between social and economic
opportunities. We claim that impact investing organizations adopt this strategy to balance their often
conflicting social and financial institutional logics.
The paper proceeds as follows. Section 2 presents the conceptual and theoretical framework. Section 3
outlines the methodological approach and the data while Section 4 presents and discusses the empirical
findings. Section 5 presents our conclusions.
2. Conceptual Framework: The International Market Selection of Social Enterprises
We rely on existing literature on hybrid organizations to build a conceptual framework for our empirical
work. We acknowledge that hybrid organizations are not restricted to only organizations that blend social
and market logics (Pache & Santos, 2013). Nevertheless, existing works have mainly focused on social
enterprises. Therefore, we primarily rely on the social enterprise literature to develop our conceptual
model and to formulate the research hypotheses. More so, our sample organizations, impact investing
organizations, combine same institutional logicssocial and businessas other social enterprises do.
2.1. Social Enterprises
Social enterprises are hybrid firms that fill institutional voids, left unattended by governments and the
market, with business-based models (Pache & Santos, 2013; Stevens, Moray, & Bruneel, 2015).
Therefore, in regions and societies where government and market failures are commonplace, social
enterprises represent important rays of hope (Doherty et al., 2014). A unique characteristic of social
enterprises is their hybridity that stems from their subscription to dual institutional logics: social welfare
and financial sustainability (Battilana & Dorado, 2010; Battilana & Lee, 2014; Doherty et al., 2014;
Pache & Santos, 2013). Being hybrids, social enterprises are neither typical for-profit firms nor typical
non-profit firms, but share characteristics of both types of firms (Peredo & McLean, 2006). Social
enterprises are often subject to tension in maintaining their hybridity (Battilana, Sengul, Pache, & Model,
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2015; Smith et al., 2013). This tension is a direct consequence of balancing the conflicting demands of the
dual institutional logics of social welfare and economic viability (Battilana & Lee, 2014; Doherty et al.,
2014). Often, failure to strike a desirable balance between them results in a trade-off, a situation where
social enterprises sacrifice the prescriptions and outcomes of one logic in favor of those of the other
(Hermes et al., 2011; Jay, 2013; Smith et al., 2013; Wry & Zhao, 2018). Nevertheless, social enterprises
endeavour to achieve a satisfactory balance between the two logics since the definition of success
encompasses excellence in both logics (Mair & Marti, 2006; Townsend & Hart, 2008). Stated differently,
a social enterprise is said to be successful if it attains the feat of creating social value while at the same
time being financially self-sustainable (Battilana & Dorado, 2010).
On the international scene, social enterprises are confronted with this social-economic tension and need to
strike a desirable balance. We demonstrate this using the three host-country macroenvironmental
factorslevel of development, institutional strength, and country risk―discussed in the next section. We
argue that the international market selection decision of social enterprises is largely shaped by their
hybridity rather than the prescriptions of conventional approaches.
2.2. International Market Selection and Host-Country Macroenvironmental Climate
International market selection is one of the most salient as well as complex decisions an organization has
to make during its expansion across borders (Clark, Li, & Shepherd, 2018; Papadopoulos, Martín Martín,
& Gaston‐Breton, 2011). For social enterprises, this decision is highly bounded rational and complex due
to inherent operating challenges in developing economies (Papadopoulos & Martín, 2011). Despite the
seeming complexity, cross-border activities characterize many hybrid organizations (Zahra et al., 2009).
The international market selection of an organization is mainly influenced by factors at two levels: target
country-level factors and firm-level factors (Kim & Aguilera, 2016). Target country-level factors include
market potential, competition, economic factors, political factors, and social factors, while firm-level
factors include resources (human, financial, etc.), competencies (technical, managerial, etc.), and
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organizational goals (Brewer, 2001; Kim & Aguilera, 2016). The present study sheds light on how social
enterprises select international markets based on the host-country’s macroeconomic conditions―level of
development, institutional strength, and country risk (Bailey, 2017).
Level of development of host country
Market potential is a key determinant of international market selection by traditional firms (Brouthers,
Mukhopadhyay, Wilkinson, & Brouthers, 2009; Brouthers & Nakos, 2005). Naturally, greater market
potential is associated with higher profits, both in present and future terms (Head & Mayer, 2004). To
excel, social enterprises require markets with good potential. Although market potential is necessary to
guarantee the long-term profitability and growth of social enterprises, it is greater in more developed
countries (Hanson, 2005). At the same time, social enterprises have a mandate to tackle diverse societal
challenges, such as unemployment, financial and social exclusion, and hunger (Pache & Santos, 2013;
Stevens et al., 2015; Townsend & Hart, 2008). These societal challenges and institutional voids are
prevalent in most developing countries. As a result, developing countries provide attractive settings and
opportunities for social enterprises to create deep social impact (Edwards & Hulme, 1996b). In sum,
developed countries offer promising climate to create economic value but less opportunities for creating
social value (Edwards & Hulme, 1996b). The reverse is true for poor countries (Edwards & Hulme,
1996a). This is a clear manifestation of the trade-off thesis (Austin, Stevenson, & Wei‐Skillern, 2006;
Doherty et al., 2014). Faced with such conflicting demands, social enterprises resort to an optimal choice
that balances their social and economic objectives (Mair, Mayer, & Lutz, 2015). Pache and Santos (2013)
term this response “selective coupling.” Against this backdrop, we formulate our first hypothesis as
follows.
Hypothesis 1: In selecting international markets, social enterprises target less developed
countries but not the least developed ones.
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Strength of institutional environment
Institutions explain economic growth and the general business environment in a given country, and it has
been argued that institutions define the “rules of the game” (North, 1990, p. 3). The purpose of
institutions is to protect property rights, enforce contracts between individuals and firms, and provide
physical and regulatory infrastructure (Bailey, 2017; North, 1990). Stronger institutions facilitate business
transactions and increase the quality of life of individuals by reducing transaction costs (Chen,
Saarenketo, & Puumalainen, 2018; North, 1990; Roy & Oliver, 2009). Therefore, countries with stronger
institutions seem to provide conducive environments for economic exchange (North, 1990; Verbeke &
Kano, 2013). This explains why profit-maximizing firms prefer countries with stronger institutions (Chen
et al., 2018; Dau, 2013; Murtha & Lenway, 1994).
By contrast, countries with weaker institutions are often prone to developmental challenges. In such
countries, the by-products of weak institutions, such as corruption, create inequality, deprivation, poverty,
poor health care, and various societal ills, are prevalent (Aidt, Dutta, & Sena, 2008). Because of their
social objects, social enterprises regard such developmental challenges stemming from weak institutions
as opportunities and the associated countries as natural markets to enter (Koch, Dreher, Nunnenkamp, &
Thiele, 2009). On the other hand, these same institutional weaknesses could potentially prevent social
enterprises from becoming financially viable, thus posing a threat to their sustainability (Fowler, 1996).
Thus, we posit that social enterprises target countries that are positioned somewhere in between, i.e.,
countries that offer social enterprises the opportunities to earn sufficient profits to pursue social goals.
This leads to our second hypothesis.
Hypothesis 2: In selecting international markets, social enterprises target countries with weak
institutions but not those with the weakest institutions.
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Country risk
Country risk refers to all factors in a host country that could cause a shortfall in the expected returns from
a foreign investment (Meldrum, 2000). This risk is outside the purview of investors and is usually the
consequence of imbalances in socio-economic, political, geographic and structural factors between
countries (Cosset & Roy, 1991; Meldrum, 2000). Because of country risk, cross border transactions carry
incremental risks that are absent in domestic transactions (Meldrum, 2000).
In the mainstream management literature, it is theorized that the extent of risk in a target country
negatively impacts market selection strategies (Andersen & Buvik, 2002; Brouthers & Nakos, 2005). This
is primarily due to the volatile relationship between profitability and risk. Scholars have identified several
sources of country risk; e.g., political, social, economic, operational, and transfer and exchange rate risk
(Cosset & Roy, 1991; Meldrum, 2000; Root, 1987; Schneider & Frey, 1985). Yet, as far as social
enterprises are concerned, the impact of country risk is probably different due to their hybridity. In high-
risk countries, vulnerable people and communities are prevalent, thus providing greater opportunity for
social enterprises to fulfill their social utility functions (Porter & Kramer, 2011; Teasdale, 2010). At the
same time, however, social enterprises need to achieve some level of economic breakthrough in order to
advance their social welfare mission. For this reason, high-risk environments may be shaky grounds for
social enterprises. Therefore, a country risk level that is unfavourable to the realization of one objective
may be favorable to the realization of the other objective, and vice versa (Austin et al., 2006). To balance
this trade-off, the optimal choice for social enterprises may be to opt for countries where risk is neither
too high nor too low. This brings us to our third hypothesis.
Hypothesis 3: In selecting international markets, social enterprises target countries with high
country risk but not those that are most risky.
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3. Data and Methodology
3.1. Context
The present study focuses on European and North American impact investing organizations that operate
in developing countries.
1
These organizations are incorporated as non-governmental (NGOs), get their
income from the services they render rather than from donations, and mainly work in developing
countries to promote financial and social inclusion through partnership with local MFIs (Salamon &
Anheier, 1992). The microfinance industry offers a natural context for this study since most industry
players satisfy the principal criterion for defining a social enterprise, namely, the coupling of social and
business logics (Battilana & Dorado, 2010; Peredo & McLean, 2006). Moreover, the microfinance
industry is globally known and acknowledged for its commitment to developmental issues. For instance,
the United Nations declared 2005 as the year of microcredit and the 2006 Nobel Peace Prize was awarded
to microfinance pioneer Mohammad Yunus who founded the Grameen Bank in Bangladesh (one of the
first MFIs). Finally, microfinance is a very internationalized industry where international lenders, donors,
investors, and technical assistance providers offer their services (Mersland et al., 2011; Mersland &
Urgeghe, 2013). Principally, the increasing internationalization of microfinance is largely driven by an
infusion of international funds (Mersland & Urgeghe, 2013). The microfinance industry is thus a suitable
testing ground for analyzing patterns of international market selection by social enterprises.
1
The sample of developing countries in the dataset are those classified by the World Bank as upper middle-income,
lower middle-income and low-income countries. High-income countries are excluded since they fall outside the
mandate of our sample firms.
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3.2. Sample and data sources
The dataset was created by us with data from multiple sources. Our sample of social enterprises consists
of impact investing organizations listed in the 2013 directory of the European Microfinance Platform (e-
MFP). These impact investing organizations, also called microfinance investment vehicles (Mersland &
Urgeghe, 2013), channel funds from suppliers (donors and other fund providers) to country-based MFIs,
with the aim of achieving mutually beneficial goals (Mersland & Urgeghe, 2013). The relationship
between providers of funds in the global north and recipients of credit from MFIs is illustrated in Figure
1. Besides financial resources, microfinance investment vehicles, especially those incorporated as non-
governmental organizations, often provide other non-financial support to their partner MFIs (Mersland et
al., 2011). Figure 2 illustrates the financial and non-financial assistance offered by impact investing
organizations to their local partners, the country-based MFIs.
Figure 1: Flow of Funds
Figure 1 illustrates how funds flow from suppliers in developed countries to microentrepreneurs in developing countries.
Source: Adapted from European Microfinance Platform (2013).
Private
Government
Foundations
Reinvested
surplus
International
impact
investing
organizations
Country-
based
MFIs
Individual
Micro-
entrepreneurs
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Figure 2: Support activities provided International impact investing organizations to country-based
MFIs
Figure 2 illustrates the financial and non-financial assistance that are offered by International impact investing
organizations to locally MFIs in developing countries.
Source: Adapted from European Microfinance Platform (2013).
The European Microfinance Platform (e-MFP) has 114 members: 104 are organizations, out of which 64
are non-governmental impact investing organizations that provided information for the 2013 directory.
However, not all the 64 organizations serve the purposes of this study and therefore we implement a
International
impact
investing
organizations
Technical Assistance
/Capacity Building
Financial
assistance
Non-financial
assistance
Research/Information
Dissemination
Policy advice/Lobby
Equity
Loans
Guarantees
Networking/Donor
Coordination
Subsidies in Money
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selection procedure that results in a fine-grained sample of 41 impact investing organizations that provide
funding and/or technical assistance to MFIs. The filtering of the organizations was done based on two
criteria: international presence and type of intervention. Regarding the international presence criterion,
only organizations that listed activities in at least one foreign country were selected, leading to the
exclusion of 2 organizations that operate solely in their country of origin. For the type of intervention
criterion, 4 universities, the United Nations, and 6 oversight organizations were excluded to further align
the data with our research interest in double bottom line impact investing organizations. The e-MFP
directory’s information was verified from the websites of the respective organizations. In cases of
discrepancies and missing information, the organizations were contacted by e-mail for clarifications.
After all these, data relating to the operating locations of 11 organization were still missing. After
excluding these 11 organizations, the final sample consists of 41 impact investing organizations
2
offering
financial and/or non-financial assistance to MFIs in at least one foreign country
3
. Country-level
macroeconomic data were collected from public sources mentioned in the subsections below.
3.3. Dependent Variable
The dependent variable is a binary variable that indicates whether a given organization operates in a given
country: the impact investing organization takes a value of 1 if it operates in the country and 0 otherwise
(Coeurderoy & Murray, 2008; Koch et al., 2009).
3.4. Independent Variables
For the independent variables, three commonly used macroeconomic factors that explain the
internationalization of firms are employed: level of development, institutional strength, and country risk.
2
The 41 organizations in the sample are headquartered in the following 17 European and North American countries:
Italy, Luxembourg, Germany, Spain, Belgium, Ireland, Netherlands, Sweden, Monaco, France, Norway,
Switzerland, Denmark, United Kingdom, Liechtenstein, Canada and United States of America.
3
List of all 41 impact investing organizations, their years of establishment, countries of origin, type of intervention,
and current international markets are available upon request.
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Level of development The Human Development Index (HDI), developed by the United Nations
Development Programme, is employed as a proxy for a country’s level of development. According to the
United Nations Development Programme, HDI is a compound index that measures a country’s standing
in three basic aspects of human development, namely, long and healthy life, schooling, and decent
standard of living. Several internationalization studies have approximated the overall level of
development of countries based on the HDI (Dow, 2000; Globerman & Shapiro, 2003). To avoid biases
resulting from specific year effects, we use the average HDI for the following years: 2000, 2004, 2008,
and 2012.
Institutional strength This is proxied by the rule of law score, as published by the World Bank (Du, Lu,
& Tao, 2008; Globerman & Shapiro, 2003). The rule of law “captures perceptions of the extent to which
agents have confidence in and abide by the rules of society, and in particular the quality of contract
enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence”
(Kaufmann, Kraay, & Mastruzzi, 2011, p. 4). Rule of law scores range from -2.5 to +2.5, representing
lower to higher perceptions, respectively. As with the HDI, the average rule of law score for the years
2000, 2004, 2008, and 2012 is used.
Country risk This is the risk emanating from socio-political, economic and structural factors in a host
country that adversely affects the expected returns or the value of a cross-border investment (Meldrum,
2000). The Euler Hermes Risk Index (EHRI) (Euler Hermes, 2014) is the proxy for country risk (Moser,
Nestmann, & Wedow, 2008). The EHRI combines five dimensions in determining country risk, including
macroeconomic status of the economy, structural soundness of the business environment, political
environment, financial flows and cyclical risk indications (Euler Hermes, 2014). The index has values
that range from 1 to 4, where higher values represent higher country risks and vice versa. For this
indicator, only data relating to the year 2014 are available.
3.5. Control Variables
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We control for the effects of nine factors: organizational experience, organizational size, distance between
home and host countries, type of intervention, bilateral relations between home and host country, bilateral
trade, size of host country, religion and host country’s natural resource endowment.
Experience This is regarded as one of the most important factors in internationalization literature
(Davidson, 1980; Kim & Aguilera, 2015). Experienced firms have a better understanding of, and ability
to predict, market conditions, thereby reducing their risk and uncertainty (Davidson, 1980). Experience is
operationalized by two variables. The first is the age of the organization and the second is international
experience which is measured by the total number of countries in which a given organization operates
(Dowell & Killaly, 2009; Lu, Liu, Wright, & Filatotchev, 2014; Mersland et al., 2011).
Firm size A firm’s size, reflected in the amount of resources it controls, plays an important role in
formulating its international marketing strategy (Dass, 2000). From a resource-based theory perspective,
large firms are able to harness and deploy the required resources that guarantee their internationalization
success in a more effective and efficient way than small firms do (Canabal & White, 2008;). In this study,
size is measured by the total number of employees in the organization (Dang, Li, & Yang, 2018).
Type of intervention Studies in the internationalization literature have shown that specific firm
characteristics―such as product and service offerings, technology, and management attributes―influence
the internationalization decisions and processes of firms (e.g., Li, 2018; Ramón-Llorens, García-Meca, &
Duréndez, 2017). Thus, two additional binary variables are included to control for the effects of type of
intervention: the provision of financial assistance and non-financial assistance.
Geographical distance Long distance discourages trade between two countries (Dow, 2000; Malhotra,
Sivakumar, & Zhu, 2009). Intuitively, firms more easily extend their operations to neighboring countries
than to distant ones (Dow, 2000). Moreover, using data on international alliances in the microfinance
industry, Golesorkhi et al., 2019b) report a clear negative relationship between geographical distance
between international partners and the MFI’s performance. Geographical distance is operationalized by
17
the direct distance between the capital of the home country and the capital of the host country. Distance
data were obtained from two websites that provide distance data between countries: Date and Time
(2014) and Geo Bytes (2006).
Bilateral relations The flow of investment and social services (such as aid) from developed to
developing countries is much influenced by bilateral relations and political arrangements―for example,
bilateral investment treaties (Neumayer & Spess, 2005). Accordingly, two controls are included to
account for the effects of bilateral relationships between the home and host countries. These include;
colonial ties and voting patterns at the United Nations (Neumayer & Spess, 2005; Weiler, Klöck, &
Dornan, 2018).
Bilateral trade Charity flows may follow patterns of existing economic ties between countries
(Berthélemy & Tichit, 2004; Younas, 2008; Maizels & Nissanke, 1984; Nowak-Lehmann, Martínez-
Zarzoso, Klasen, & Herzer, 2009). We account for this in our estimations by controlling for bilateral trade
between home and host countries. We use the volume of annual exports from home to host countries as a
meaningful proxy (Berthélemy & Tichit, 2004; Metzger, Nunnenkamp, & Mahmoud, 2010). This data is
obtained from the database of the International Trade Centre (http://www.intracen.org/).
Host country size The proxy for this control is the total population of the respective countries,
contained in the Central Intelligence Agency’s World Fact Book (2014). Scholars have argued that
populous countries attract more foreign investments thanks to their greater market potential (Nielsen,
Asmussen, & Weatherall, 2017).
Religion Many organizations involved in microfinance are motivated by Christian faith (Mersland,
D’Espallier & Supphellen, 2013). Hence, following Alesina, and Dollar (2000) and Clist (2011), we
control for the effects of religion in our models. Religion data is obtained from the Central Intelligence
Agency’s World Fact Book.
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Natural resource endowment Social interventions and aid to developing countries may be driven by the
selfish interests of donors rather than the needs of recipient countries. These interests may include the
quest to gain access and to exploit resources in recipient countries or what Naim (2007) calls ‘rogue aid’.
To control for this possible effect, we include a binary variable that indicates whether a host country is an
oil and gas exporter (Alesina & Dollar, 2000; Clist, 2011). Oil and gas data is obtained from the Central
Intelligence Agency’s World Fact Book. The definitions and summary statistics of all variables are
reported in Table 1.
3.6. Econometric Models
First, we conduct a two-sample t-test to compare macroeconomic conditions in countries where impact
investing organizations operate and countries where they have no operations. We perform this test on four
samples (full sample, upper middle-income countries, lower middle-income countries, and low-income
countries) to assess if there are univariate differences. The reason why we also run the test on the sub-
samples is to better identify the hybridity proposed in the hypotheses. Then, we proceed to a multivariate
setting where we specify a probit regression model as follows:
To capture the non-linear relationship implied by the hypotheses, we test and run the model on
the full sample and three sub-samples. The sub-samples are based on the World Bank’s income
classification of countries. The first sample, upper middle-income (UMI), are countries with
gross national income (GNI) between $4,086 and $12,615. The second sample consist of lower
middle-income (LMI) countries with GNI values ranging from $1,036 to $4,085 and the third
19
sample consist of low-income (LI) countries with GNI values lesser than $1,035. Descriptive
statistics of each sample are reported in Table 3.
4. Empirical Findings and Discussion
4.1. Descriptive Statistics
Table 1 presents the descriptive statistics of the variables. The average impact investing organization in
the dataset operates in 14.8% of the total sample of countries, corresponding to an approximate number of
23 countries per impact investing organization. The mean level of country development corresponds to an
HDI score of 0.580. The mean institutional strength, measured by the World Bank’s Rule of Law index, is
negative (-0.432). Thus, most of the countries in the dataset are characterized by weaker institutions.
Similarly, the average country risk of 3.159 is high as it gets closer to the maximum possible value of 4.
On average, an impact investing organization in the dataset is 28 years old and has about 42 employees.
The share of impact investing organizations that offer financial and non-financial services are 73.2% and
95.1% respectively with most organizations combining both interventions. The average distance between
the home and host countries is 7042 km. 5.8% of the total sample of developing countries were previous
colonies of the countries from which the impact investing organizations originate. Regarding voting
patterns at the United Nations, averagely, the countries of origin of the impact investing organizations and
the 153 developing countries vote in the same direction in 58.2% of cases. The average annual volume of
exports from home to host countries is valued at approximately US$ 761.4 million. The mean value of
country size, measured by total population, is 37.5 million. Christianity is the main religion in 60.9% of
the host countries. For natural resources, 27.2% of the host countries are exporters of oil and/or gas.
20
In Table 2, the correlations between the independent variables are presented. Multicollinearity is a
common problem in studies that use macroeconomic data (e.g., Metzger, Nunnenkamp, & Mahmoud,
2010). Multicollinearity is detected when the variance inflation factor of a variable is greater than 5 or
when the correlation between two explanatory variables exceeds 0.9 (Hair, Black, Babin, & Anderson,
2010). Nonetheless, the numbers in Table 2 dispel any concerns of multicollinearity. The correlation
coefficients and the variance inflation factor values reported in the table are lower than the aforesaid
upper bounds. The highest correlation coefficient is 0.500 (the correlation between development and
institution) and the highest variance inflation factor is 2.09.
21
Table 1: Definition of variables and descriptive statistics
Variable
Definition
Obs
Mean
Std. Dev.
Min
Max
Dependent variable
Operate
“1” if the impact investing organization operates in a given country,
“0” otherwise
6,273
0.148
0.355
0
1
Independent variables
Development
Country’s Human Development Index score
5,453
0.580
0.152
0.270
0.880
Institution
Country’s score on the World Bank’s measure of rule of law
6,027
-0.432
0.749
-2.450
1.720
Country risk
Country’s score on the Euler Hermes Risk Index
5,658
3.159
1.105
1.000
4.000
Control variables
Age
Age of organization
6,273
28.196
20.124
1.000
72.000
Int. experience
Number of developing countries in which organization operates
6,273
22.682
18.871
2
99
Org. size
Number of Employees
6,120
41.644
136.995
1
874
Fin. Assistance
“1” if the impact investing organization offers financial assistance
and “0” otherwise
6,273
0.732
0.443
0
1
Nonfin assistance
“1” if the impact investing organization offers non-financial
assistance and “0” otherwise
6,273
0.951
0.215
0
1
Distance
Geographical distance (in km) between the home and host countries
6,273
7042.382
3885.378
157
49446
(ln)Distance
Logarithm of geographical distance between the home and host
countries
6,273
8.670
0.690
5.056
10.809
Colony
“1” if host country was a colony of home country and “0”
otherwise
6,273
0.058
0.234
0
1
UN voting
Percentage of agreement between home and host country during
voting at the United Nations
5,735
0.582
0.147
0.014
1
Export from home
Volume of export from home to host country (in US$ million)
6,035
761.387
4750.376
0
240000.000
(ln)Export from home
Logarithm of volume of export from home to host country
6,035
10.619
3.253
0
19.297
Country Size
Total population of country (in millions)
6,273
37.50
150.000
0.00986
1350.00
(ln)Country Size
Logarithm of total population of country
6,273
15.244
2.429
9.196
21.024
Christianity
“1” if Christianity is the main religion in the host country and “0”
otherwise
6,144
0.609
0.488
0
1
Oil/Gas exporter
“1” if the host country is an oil and/or gas exporter “0” otherwise
6,191
0.272
0.445
0
1
22
Table 2: Correlations and Variance Inflation factor
No.
1
2
3
4
5
6
7
8
9
10
VIF
Development
1
1.000
1.68
Institution
2
0.500
1.000
2.09
Country risk
3
-0.324
-0.444
1.000
1.59
Age
4
0.003
0.003
-0.001
1.000
1.18
Int. experience
5
-0.023
-0.031
0.011
0.056
1.000
1.04
Org. size
6
0.000
0.002
0.002
0.267
-0.007
1.000
1.08
Fin. Assistance
7
0.003
0.003
0.000
0.097
-0.150
0.067
1.000
1.13
Nonfin. assistance
8
-0.002
-0.002
0.002
0.176
0.103
0.048
-0.100
1.000
1.14
Distance
9
-0.199
-0.093
-0.039
0.028
0.000
-0.018
-0.018
0.025
1.000
1.38
Colony
10
-0.080
-0.009
-0.006
-0.081
-0.057
-0.019
0.086
0.041
0.045
1.000
1.05
UN voting
11
0.330
0.289
-0.150
0.097
0.031
0.016
-0.095
0.054
-0.389
-0.140
1.52
(ln)Export from home
12
0.182
0.068
-0.217
0.157
-0.012
0.025
0.189
0.209
-0.235
0.017
1.57
(ln)Country Size
13
-0.280
-0.420
-0.086
-0.003
0.029
-0.003
-0.001
0.001
0.000
0.023
1.91
Christianity
14
0.085
0.138
-0.213
0.001
-0.017
-0.001
0.003
-0.002
0.193
0.028
1.26
Oil/Gas exporter
15
0.118
-0.183
-0.043
-0.001
0.004
-0.002
-0.001
0.000
0.008
0.008
1.17
No.
11
12
13
14
15
UN voting
11
1.000
(ln)Export from home
12
0.151
1.000
(ln)Country Size
13
-0.032
0.353
1.000
Christianity
14
0.142
-0.079
-0.241
1.000
Oil/Gas exporter
15
-0.130
0.128
0.180
-0.095
1.000
23
Table 3 gives a brief description of the characteristics of the developing countries in the dataset. A total of
153 developing countries are represented in the dataset. These are countries categorized as upper middle
income, lower middle income, or low income by the World Bank
4
. Of the 153 countries, 132 host impact
investing organizations. The World Bank’s classification of the 132 countries are as follows: 45 are upper
middle income, 44 are lower middle income, 34 are lower income, and 9 are unclassified. Naturally, the
more developed countries according to the World Bank classification are characterized by higher HDI,
better institutions and lower country risk. The one-way ANOVA results reported in the table reveal that
the differences observed between the macroeconomic conditions of the respective income categories are
statistically significant (p < 0.01). Thus, we show that as one moves from upper middle-income through
lower middle-income to low-income countries, the macroeconomic indicators significantly deteriorate,
and the countries become more problematic environments for businesses. We rely on this received
knowledge to capture the non-linear relationship implied by the hypotheses and to show the international
market selection decisions of impact investing organizations and more generally, that of double bottom
line firms. We achieve this by performing the analysis on the total sample and the three sub-samples of
countries as outlined in the methods session.
Table 3: Comparison of macroeconomic conditions of countries in the respective income categories
using One-Way Analysis of Variance
Full sample
N=153
UMI
N=50
LMI
N=46
LI
N=34
F-
statistics
Development
0.580
0.700
0.558
0.398
4912.77**
*
Institution
-0.432
-0.200
-0.541
-0.939
787.68***
Country risk
3.159
2.766
3.326
3.758
451.56***
The table shows the characteristics of the sampled countries. There is a total of 153 developing countries in the
dataset, classified by the World Bank into upper middle-income (UMI), lower middle-income (LMI), and lower-
income (LI) categories.
4
Of the 153 countries, 50 are upper middle-income, 46 are lower middle-income and 34 are low-income A total of
23 countries are not classified by the World Bank into any of the income brackets. Impact investing organizations
are present in 9 of these unclassified countries
24
4.2. Results
In the following, we present the main findings of the study. First, we present initial evidence by means of
a t-test whereby we compare the microeconomic factors of countries where the impact investing
organizations in our sample are present with those of countries where they are absent. Next, we present
the probit regression results.
Mean comparison t-tests and graphical illustration
Table 4 presents the mean comparison t-test results. In panel A of Table 4, the test is performed on the
full sample of developing countries in the dataset. In panels B and C, the comparison is performed on the
sample consisting only of countries in the upper middle-income and lower middle-income categories,
respectively. Panel D shows the results of the comparison among countries in the low-income bracket.
In panel A, the results show that impact investing organizations generally operate in countries that are
significantly less developed and institutionally weaker than the countries where they do not operate. The
opposite, however, holds true for the country risk indicator. This finding also holds true in panel B where
we consider only upper middle-income countries. In panel C, the mean value of development of countries
where impact investing organizations are present is higher than that of countries where they are absent but
the difference in means is too small to be statistically significant. Further, the institutions in the countries
where impact investing organizations are present are weaker than the institutions in the countries where
they are absent, but similar to development, the difference is not statistically significant. The results also
show that impact investing organizations invest in lower middle-income countries where country risk is
significantly lower. In panel D, the results show that countries in which impact investing organizations
operate have stronger institutions but have similar level of development as countries where they are
absent. Again, the risk in the countries where impact investing organizations operate is significantly
lower.
25
Table 4: T-test comparison of macroeconomic of countries where impact investing organizations
operate and countries where they do not operate
Variables
Operate = 1
Operate = 0
t-value
Panel A: Full sample of developing countries
Development
0.536
0.589
9.729***
Institution
-0.637
-0.395
9.066***
Country risk
3.081
3.175
2.354**
Panel B: Upper middle-income countries
Development
0.687
0.702
2.853***
Institution
-0.384
-0.175
4.693***
Country risk
2.317
2.830
6.383***
Panel C: Lower middle-income countries
Development
0.561
0.557
-0.494
Institution
-0.579
-0.533
1.518
Country risk
3.029
3.388
6.199***
Panel D: Low-income countries
Development
0.402
0.396
-1.314
Institution
-0.809
-0.982
-6.934***
Country risk
3.583
3.818
7.795***
In this table, we employ two sample t-tests to compare the microeconomic factors of countries where impact
investing organizations operate and countries where they do not operate. *, **, and *** show statistical
significance at 0.1, 0.05, and 0.01, respectively.
In line with the hybridity hypothesis, it appears that impact investing organizations internationalize into
developing countries that are poor and institutionally weak but keep away from the poorest countries and
those with the weakest institutions. The results also suggest that impact investing organizations always
avoid high-risk countries. We posit that impact investing organizations approach their international
market selection decisions in this way in order to simultaneously “do social good” and be financially self-
sustainable.
Is there a tipping or turning point in economic conditions where impact investing organizations are most
likely to invest? To answer this, we fit a quadratic plot to each of the macroeconomic conditions and the
26
operating tendencies of impact investing organizations. Graphs A, B, and C of Figure 4 show the
quadratic plots for level of development, institutional strength, and country risk, respectively.
Figure 3: Quadratic plot of macroeconomic conditions and operating tendencies
Graph A: Level of development
Graph B: Strength of institutions
Turning point: Development (HDI) value of 0.416
Turning point: Institution (Rule of law) value of -0.779
Graph C: Country risk
Turning point: Country risk (Euler Hermes Risk Index) value of 2.281.
Figure 3 illustrates quadratic plots for each of the macroeconomic factors. Graphs A, B, and C are the plots for
level of development, institutional strength, and country risk, respectively.
Figure 3 shows clearly the tipping point of each of the macroeconomic factors. In graph A, the tipping
point corresponds to an HDI score of 0.436; in graph B, the tipping point corresponds to a rule of law
27
index of -0.929; and in graph C, the tipping point maps to a Euler Hermes risk index of 2.206. Thus,
above or below these points, the probability of investment diminishes.
Multiple regressions
We run a probit regression first on the full sample consisting of all developing countries in the dataset (1);
second, on the sample of upper middle-income countries (2); third, on the sample of lower middle-income
countries (3); and finally on the sample of low-income countries (4).
Table 5 shows the probit regression results of the macroeconomic determinants of the international
market selection decisions of impact investing organizations. The results displayed in the table confirm
the univariate differences observed in Table 4 and largely support the formulated hypotheses. Social
enterprises internationalize into poor and institutionally weak countries but avoid the most problematic
countries
5
. At the same time social enterprises always avoid risky countries. This later finding is counter
to our hypothesis.
In the full sample of developing countries (1), the coefficient of development is negative and significant
(p < 0.01). Thus, impact investing organizations are more likely to target and operate in developing
countries that are characterized by low levels of development. In the sample consisting of upper middle-
income countries (2), the coefficient of development changes to positive but insignificant. It appears that
level of development is not a priority for impact investing organizations in upper middle-income
countries. In the sample of lower middle-income countries (3), the development variable has a positive
significant coefficient (p < 0.05). In this income category of countries, impact investing organizations
prefer to operate in countries with good development.
5
In an unreported analysis for robustness checks, we employ alternative proxies for each of the macroeconomic
factors. Specifically, development is proxied with the gross domestic product per capita retrieved from the World
Bank database, institutional strength is proxied with Transparency International’s corruption perception index (CPI),
and country risk is proxied with the country risk classification published by the Organization for Economic
Cooperation and Development (OECD). Overall, the results are analogous to those reported in the text and hence
support the hybridity hypothesis.
28
Table 5: International market selection and host-country macroeconomic condition
SAMPLE
UMI, LMI & LI
UMI
LMI
LI
Dependent variable: Operate; 1=Yes, 0=No
VARIABLES
(1)
(2)
(3)
(4)
Development
-1.235***
1.049
1.207**
1.432**
(0.207)
(0.739)
(0.485)
(0.572)
Institution
-0.111*
-1.050***
0.067
0.444***
(0.060)
(0.265)
(0.105)
(0.131)
Country risk
-0.117***
-0.416***
-0.119**
-0.536***
(0.031)
(0.112)
(0.046)
(0.121)
Age
0.002
0.008**
0.006**
-0.004
(0.002)
(0.004)
(0.003)
(0.003)
Int. experience
0.026***
0.032***
0.029***
0.029***
(0.002)
(0.004)
(0.003)
(0.003)
Org. size
0.000
0.000
0.000
-0.001
(0.000)
(0.001)
(0.000)
(0.000)
Fin. Assistance
-0.023
-0.052
-0.271**
0.238*
(0.080)
(0.178)
(0.136)
(0.140)
Nonfin. assistance
0.015
-0.292
-0.116
-0.054
(0.199)
(0.421)
(0.388)
(0.315)
Distance
0.091*
0.125
0.075
-0.206
(0.052)
(0.105)
(0.089)
(0.177)
Colony
0.453***
0.502**
0.321*
0.723***
(0.102)
(0.252)
(0.174)
(0.174)
UN voting
-0.406
-1.519*
-0.905**
-0.907
(0.279)
(0.880)
(0.429)
(0.650)
(ln)Export from home
-0.007
0.006
0.038*
-0.018
(0.012)
(0.029)
(0.022)
(0.022)
Country size
0.244***
0.245***
0.160***
0.254***
(0.020)
(0.065)
(0.033)
(0.049)
Christianity
0.184***
-0.094
0.136
0.085
(0.056)
(0.163)
(0.110)
(0.095)
Oil/Gas exporter
-0.245***
-0.330*
-0.076
-0.772***
(0.059)
(0.186)
(0.097)
(0.249)
Constant
-5.194***
-6.272***
-5.119***
-1.353
(0.641)
(1.664)
(1.070)
(1.654)
Origin dummies
Yes
Yes
Yes
Yes
Observations
4,736
1,635
1,517
1,265
Pseudo R2
0.223
0.348
0.207
0.246
LR χ2
958.4
340.9
306.9
361.7
Prob> χ2
0.000
0.000
0.000
0.000
This table shows the probit regression results of the macroeconomic determinants of the international market
selection decisions of impact investing organizations. UMI = upper middle income, LMI = lower middle income,
and LI = low income. Observations = product of total number of impact investing organizations and number of
countries in the sample. Standard errors are in parentheses. *, **, and *** show statistical significance at 0.1, 0.05,
and 0.01, respectively.
29
Similarly, in the sample consisting of low-income countries (3), the coefficient of development is positive
and significant (p < 0.05).
Overall, the regressions show that impact investing organizations internationalize into less developed
countries but not the least developed countries. This confirms our first hypothesis. Social enterprises
prefer to invest in countries where they can create some social value (Edwards & Hulme, 1996b) without
hurting their economic viability (Hanson, 2005).
The second macroeconomic factor, institution, is significantly and negatively related to impact investing
organizationsdecision to operate in the full sample of developing countries (p < 0.05). Therefore, in
general terms, social enterprises are drawn to countries with weak institutional environments (Aidt et al.,
2008). The same results are obtained, and conclusions drawn, after running the model on the sample of
upper middle-income countries. In the remaining samples―lower middle-income and lower-income
countries―the sign of the coefficient changes to positive. Thus, in these income categories, impact
investing organizations avoid countries with the weakest institutions. However, the observed positive
relationship is only significant in the sample of low-income countries (p < 0.01). Thus, in the quest to
maintain their economic viability, social enterprises avoid low-income countries with the weakest
institutions (Dau, 2013; Murtha & Lenway, 1994). Again, this result supports the trade-off hypothesis
(Hermes et al., 2011; Jay, 2013; Smith et al., 2013; Wry & Zhao, 2018) and confirm our second
hypothesis that social enterprises balance their conflicting objectives by generally entering countries with
weak institutions but avoiding those countries with the weakest institutions (Mair & Marti, 2006; Pache &
Santos, 2013; Townsend & Hart, 2008).
The coefficient of the third macroeconomic variable, country risk, is significantly negative in all
estimations. This is interesting because it shows that the organizations in our sample always consider
country risk as something negative when entering an international market. In essence, the impact
investing organizations in our sample behave as conventional firms do when it comes to a host country’s
30
risk (Andersen & Buvik, 2002; Brouthers & Nakos, 2005; Rothaermel et al., 2006). What kind of country
risk could these organizations be avoiding? Indeed, most country risk measures are composite indices of
multiple risk components. In a further analysis (unreported), we examine the effects of six (6)
components of country risk which we obtained from the database of the Economists Intelligence Unit
(http://country.eiu.com/AllCountries.aspx). These include financial risk (e.g. devaluation risk, marketable
debt), foreign trade payments risk (e.g. discriminatory tariffs, trade embargo risk), infrastructure risk (e.g.
port facilities, transportation and communication network), macroeconomic risk (e.g. exchange rate
volatility, recession risk), political stability risk (e.g. social unrest, orderly transfers) and security risk (e.g.
armed conflict, violent crime). Results of this supplementary analysis closely match the main results. It
appears that the impact investing organizations in our dataset avoid country risk, regardless of the source.
However, we conjecture that this is probably because these organizations are involved in financial
intermediation. All the same, the third hypothesis is only partly supported by this result.
Some of the control variables yield interesting, significant results. For example, the size of the host
country seems to matter when impact investing organizations go global. Populous countries are preferred,
as the country size variable is significant in all estimations. This corroborates many other extant studies
on mainstream firms (Brouthers et al., 2009; Brouthers & Nakos, 2005). The international experience
variable is significantly positive in all regressions, suggesting that the decision to operate in a given
county is influenced by the past internationalization experience of the organizations (Davidson, 1980;
Kim & Aguilera, 2015). The coefficient of age is mostly positive but significant in models (2) and (3).
Intuitively, experienced organizations are more knowledgeable than inexperienced ones. Hence the
findings on international experience and age concurs with existing studies that theorize the
internationalization process as a function of organizations’ knowledge and their internationalization
experience (Johanson & Wiedersheim‐Paul, 1975; Johanson & Vahlne, 1977). The results also show that
impact investing organizations are influenced by bilateral relations between countries when selecting their
foreign markets (Neumayer & Spess, 2005; Weiler et al., 2018). This is evidenced by the high
31
significance of Colony in all estimations. Surprisingly, the effect of UN voting is contrary to our
expectation as it is negative in all regressions, though significant only in (2) and (3). Perhaps, impact
investing organizations’ decision to invest in a country is influenced by the need in the host country as
well other forms of bilateral relations (e.g., colonial ties) rather than mere commonalities during UN
voting.
The finding on geographical distance is particularly interesting. Impact investing organizations do not
seem to bother about distance when deciding where to invest. This is contrary to the preference of
mainstream firms, which tend to opt for shorter distances when going international (Dow, 2000; Malhotra
et al., 2009). A possible explanation for this is that countries classified as developing are far away from
Europe and North America; thus, whether a social enterprise enters Uganda or Bolivia does not matter. In
any case, it is far away from home (Golesorkhi et al., 2019b). The effect of religion is significantly
positive in model (1), suggesting that the organizations in our sample generally invest in countries where
Christianity is the main religion. This finding is expected because Christianity is the major religion in
most European and Northern American countries, where the impact investing organizations originate.
However, the effect of religions vanishes in the models estimated on the sub-samples. Lastly, oil/gas
exporter is significantly negative in all regressions, except in (3). This result is unsurprising since oil
exporting countries may be well resourced to combat social challenges than others.
5. Conclusions
In this article, the international market selection of social enterprises is examined based on the
macroeconomic conditions of the host countries. By investigating this relationship, our aim is to shed
light on the location preferences of social enterprises, in terms of macroeconomic conditions in the host
country, when they go international and whether this is tied to their hybridity. This phenomenon is
explored using data from 41 impact investing organizations that on average operate in 23 developing
counties.
32
The empirical results reveal that impact investing organizations that expand their activities across borders
target less developed and institutionally weak countries. However, they do not target the least developed
countries and those with the weakest institutions. We argue that this is because social enterprises must
balance their social and financial logics (Mair & Marti, 2006; Mair et al., 2015; Pache & Santos, 2013).
Thus, social enterprises fulfill their social obligation by targeting poorly developed countries with weak
institutions, but at the same time they ensure their financial sustainability by not entering the most
problematic countries. The study further shows that impact investing organizations avoid high-risk
countries, a finding that may be related to the type of services, namely, financial intermediation, that they
provide. Overall, the optimal choice for social enterprises seems to be to internationalize into countries
that offer a desirable balance between social and economic opportunities.
We highlight two practical implications of our findings. First, managers of MFIs in developing countries
that wish to attract foreign investors (that originate from the global north) should understand and be aware
of their own macroeconomic context. This may be an important step to develop the right strategy to
mitigate macro-environmental risk. For example, MFIs that operate in weaker economies could attract
foreign investors through their commitment to financial sustainability, by showing good social outcomes
or by promising higher returns (Cobb, Wry, & Zhao, 2016). Second, foreign investors should endeavour
to look beyond factors at the macro level by considering firm level risks whenever possible. By doing so,
foreign investors can assess whether conditions at the firm level compensates for those at the macro-level.
Our study contributes to the nascent literature on the internationalization of social enterprises and more
generally to the literature on hybrid organizations. The study identifies the host-country macroeconomic
factors that social enterprises consider important in their international market selection decisions. In
particular, our study sheds light on the hybridity approach that social enterprises adopt in selecting their
international markets. Our study provides empirical evidence that social enterprises target foreign markets
that enable them to balance their dual institutional logics and thus preserve their hybridity. Based on these
findings future studies are encouraged to be mindful of the hybridity of social enterprises when theorizing
33
the internationalization of these firms. Moreover, the hybrid approach to internationalization needs further
investigation. Do social enterprises cross-subsidize between countries with good macroeconomic outlook
and those with inferior macroeconomic conditions? Do social enterprises initially enter strong economies
before weaker ones or vice versa? These possible nuances could be fruitful avenues for future research.
Evidence on the specific organizational characteristics of social enterprises that motivate their
internationalization could shed further light on the discussion (Brewer, 2001; Nielsen et al., 2017). An
example is the role of knowledge (Johanson & Vahlne, 1977). Even though we infer the effect of
knowledge through our measures of experience, this approach does not exhaustively capture the role of
knowledge (e.g. in mitigating risk) during the internationalization process of social enterprises. Finally, in
this paper, we use data from only organizations involved in financial intermediation. Financial institutions
that provide credit facilities are concerned about the repayment capacities of their investees.
Consequently, such institutions may avoid organizations which operate in countries that have high
chances of default. This might be related to why the impact investing organizations in our dataset strongly
avoid risky countries regardless of income category. Additionally, the universe of social enterprises is
complex and diverse, involving a wide range of players with heterogenous social interventions (Defourny
& Nyssens, 2010; Young & Lecy, 2014). Consequently, future studies on other types of social
enterprises, i.e., those not involved in financial intermediation, and their internationalization strategies are
needed.
34
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-
for-profit sectors.
Declaration of interest: None
Acknowledgements
We are grateful to the editor Pervez Ghauri, Jorma Larimo, Ariane Szafarz, Arvind Ashta, François-
Xavier Ledru and three anonymous reviewers for their useful comments and suggestions. We also thank
the participants of the following conferences for their valuable comments and suggestions: 7th Aalborg
International Business Conference (Aalborg. May 2018), the 2018 Center for Research on Social
Enterprises and Microfinance (CERSEM) Research Day (Kristiansand. April 2018), the Centre for
European Research in Microfinance (CERMi) Research Day (Brussels. October 2018), 44th European
International Business Academy Conference (Poznań. 2018) and the 6th European Research Conference
on Microfinance (Paris. June 2019).
35
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