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Human Capital in Social and Commercial
Entrepreneurship
Saul Estrin
London School of Economics, CEPR, and IZA
Houghton Street, London WC2A2AE, UK
s.estrin@lse.ac.uk
Tomasz Mickiewicz
Aston University and Global Entrepreneurship Monitor UK
Aston Triangle, Birmingham, B4 7ET, UK
t.mickiewicz@aston.ac.uk
Ute Stephan
Aston University, and Global Entrepreneurship Monitor UK
Aston Triangle, Birmingham, B4 7ET, UK
u.stephan@aston.ac.uk
version March 22nd, 2016
Data for this study were provided by the Global Entrepreneurship Monitor (GEM), which is a
consortium of research teams representing more than 85 countries across the globe. Names of the
members of national teams, the global coordination team, and the financial sponsors are published in
the annual Global Entrepreneurship Monitor Reports, which can be downloaded at
www.gemconsortium.org.We thank all the researchers and their financial supporters who made this
project possible. The authors acknowledge valuable comments from Paul Reynolds, participants of
the Aston Social Enterprise Research Day 2015, and from anonymous reviewers of both the Academy
of Management Annual Meeting and, last but not least, of this journal. Ute Stephan gratefully
acknowledges financial support by the European Commission, Socioeconomic Sciences and
Humanities Grant Agreement 613500 (Seforїs project). Any errors are our own. The authors are listed
in alphabetical order and contributed equally to the manuscript.
1
Human Capital in Social and Commercial
Entrepreneurship
ABSTRACT
We advance research on human capital and entrepreneurial entry and posit that in order to
generate value, social entrepreneurship requires different configurations of human capital
than commercial entrepreneurship. We develop a multilevel framework to analyse the
commonalities and differences between social and commercial entrepreneurship, including
the impact of general and specific human capital, of national context and its moderating effect
on the human capital-entrepreneurship relationship. We find that specific human capital is
relatively more important in commercial entrepreneurship, and general human capital in
social entrepreneurship, and that the effects of human capital depend on the rule of law.
Keywords: human capital; education; commercial entrepreneurship; social entrepreneurship;
institutions; rule of law; property rights; Global Entrepreneurship Monitor, multi-level
modelling
JEL codes: J24, L26, O17
2
1. Introduction
Many aspects of the venture creation process, such as the necessity to innovate, to
take risks, and to coordinate resources (Schumpeter, 1934), will be common to social and
commercial entrepreneurs. In this respect, the two occupations may draw on a similar
entrepreneurial talent pool. However, while social and commercial entrepreneurship both
create value, they differ in the primary objective of the activity. Social entrepreneurs identify
opportunities arising from “neglected problems in society involving positive externalities”,
which are neither incorporated into the market nor addressed by the government (Santos,
2012, p.342). By realising those opportunities, social entrepreneurs create “social welfare”
(Mair & Marti, 2006; Zahra, Gedajlovic, Neubaum & Shulman, 2009) while taking the
financial viability of their venture as a constraint. In contrast, commercial entrepreneurs
maximise “private welfare” by creating value and capturing the residual for themselves
(Santos, 2012). Because the goals and the way in which value is created differ for social and
commercial entrepreneurs, they may need to rely upon different skills and abilities; implying
that the two types of entrepreneur may not be drawn from exactly the same pool of
entrepreneurial talent.
Human capital is important for all entrepreneurs in making occupational choices in
the labour market between paid employment and venture creation (Parker, 2009 for a review).
It is useful to follow Becker (1964) in distinguishing between general human capital, which
can be employed across a variety of occupations and industries, and specific human capital,
for which the derived value is specific to a context, say a job, sector or occupation (Acemoglu
& Pischke, 1998). We argue that, in evaluating the decision to become an entrepreneur, it is
important to consider how specific and general human capital are combined. We explore the
proposition that their relative weights will differ for social as against commercial
3
entrepreneurship because social entrepreneurs do not pursue the same objectives and this
leads to differences in their activities.
Both types of entrepreneurs will rely on entrepreneurship specific skills and
knowledge. However, different and additional abilities may be needed for social
entrepreneurship, in particular to identify and exploit opportunities that can generate positive
external effects. Hence, we argue that general human capital, which is associated with a more
diverse cognitive perspective, may have relatively greater significance for social
entrepreneurs. This is because the scope of their objectives is broader and their activities
more complex; whilst employing market-based strategies, they also seek to create value that
is not captured within their venture. Thus they need to attend simultaneously to potentially
conflicting social and economic logics in their enterprise (Battilana & Lee, 2014; Mair, Meier
& Lutz, 2015) as well as to develop capabilities for relating to a wide set of stakeholders.
Furthermore, their actions may need to be strongly embedded in local communities, to
mobilize resources and stimulate a wider social impact (Austin, Stevenson, Wei-Skillern,
2006; Stephan, Patterson, Kelly & Mair, 2016).
To explain engagement in social entrepreneurship fully, we argue that one needs to
consider both motivational aspects and human capital theory. The existing social
entrepreneurship literature focusses on the importance of other-regarding values and
prosocial-motivation of individuals as determinants of social entrepreneurship (e.g., Miller,
Grimes, McMullen & Vogus, 2012; Renko, 2013). The specific skills and broader abilities
needed to act upon those values have been rarely considered (exceptions include: Bacq,
Hartog & Hoogendoorn, 2014; Parker, 2008). Yet an occupational choice of social
entrepreneurship is not necessarily purely driven by values: an individual characterised by
other-regarding values may follow a commercial project and realise his/her values outside
that project, say by philanthropic giving. The choice to engage in social entrepreneurship will
4
be conditioned on the individual’s human capital that enables the identification and formation
of a relatively complex venture which is characterised by the joint supply of commercial
output and of positive external effects. Moreover, the human capital characteristics that
enhance individual capacities to realise positive externalities may also be those that motivate
people to search for those opportunities; an example of the socializing effects of education
proposed in value theory (Schwartz, 2008). Values will influence an individual’s cognitive
alertness, and in turn, those values will be affected by perceptions: what we are able to notice
and understand1. This leads us to hypothesise about the relationship between both specific
and general human capital and entry into commercial as against social entrepreneurship,
considering the ability-enhancing (as typically discussed in economic approaches) and the
motivation-shaping effects (as alluded to in psychological approaches) of human capital.
These factors will be moderated by aspects of the institutional context. In considering
differences in the propensity to enter social and commercial entrepreneurship across nations,
the balance of returns from human capital from different occupational choices is contingent
on country-specific institutional characteristics. A weak rule of law increases the risk of
expropriation of entrepreneurial returns, more so than of income from employment, shifting
the balance of incentives to the latter (Estrin, Korosteleva & Mickiewicz, 2013b). Hence,
both commercial and social entrepreneurs are more common in societies with strong
constitutional-level institutions (Estrin, Mickiewicz & Stephan, 2013a). Furthermore, returns
to different forms of entrepreneurship and to different types of human capital may be
sensitive to institutional contexts, and this applies to social as well as commercial
entrepreneurship. The literature has not previously attempted to unpack these complex cross-
country differences by investigating the role that national institutions may play in moderating
the effects of individual-level human capital characteristics on different forms of
1 Schwartz, Sagiv and Boehnke (2000) provide arguments and general evidence that values influence
information processing and attention focus.
5
entrepreneurship. In particular, we explore whether the rule of law has a moderating effect on
how different types of human capital act to support both social and commercial
entrepreneurship.
Our research questions therefore span personal characteristics and national contexts,
which means that multi-level modelling is the appropriate methodology. We theorize about
why the rule of law moderates the impact of general and specific human capital on individual
choices to become a social entrepreneur, in ways that are different to the effects for
commercial entrepreneurs. We test our hypotheses on a large cross-national data set,
consisting of population-representative surveys combined with independent indicators of the
rule of law.
This study contributes to research on both human capital and entrepreneurship,
especially social entrepreneurship. It broadens our understanding of the role of human capital
in entrepreneurship by newly considering socialization effects of general human capital
alongside the well-established ability effects of human capital. Analysing human capital in
relation to social entrepreneurship highlights a broader insight, namely the need to
acknowledge heterogeneity among entrepreneurs, their preferences, and the varying returns
they seek – when investigating the effects of human capital. Moreover, we offer a more
contextualized understanding of the role of human capital by combining human capital theory
and institutional theory. Our multi-level framework leverages institutional theory to make
predictions about the heterogeneous effects of different types of human capital in a variety of
country contexts. This responds to calls for greater consideration of context in
entrepreneurship research (Zahra & Wright, 2011; Welter, 2011). In sum, while the link
between human capital and entrepreneurial performance is well documented, how human
capital influences entrepreneurial entry is less well researched and the findings are conflicting
to date. Our research suggests that theorizing about the heterogeneous effects of human
6
capital, among different types of entrepreneurs as well as across different institutional
settings, will lead to more consistent results.
We advance the understanding of social entrepreneurship by drawing attention to the
important role of ability and human capital, where the current discourse is dominated by a
focus on motivation. In so doing we respond to a call by Parker (2008) to provide a simple
but theoretically grounded typology to understand who becomes a social entrepreneur, and in
particular how differences in human capital drive different choices in entrepreneurship. This
helps to answer whether social and commercial entrepreneurship compete for the same
entrepreneurial talent – they do so only to a limited extent. In fact, our findings further
corroborate a ‘crowding in’ effect, whereby social entrepreneurship attracts new talent into
the entrepreneurial process (Estrin et al., 2013a).
2. Theoretical framework
2.1 Entrepreneurship
Entrepreneurship - “new entry” through the efforts towards the creation of a viable
business (Gartner, 1989; Reynolds, Bosma, Autio, Hunt, De Bono, Servais, Lopez-Garcia &
Chin, 2005) - results from an individual’s occupational choice to work on his/her own
account (e.g., Hebert & Link, 1988). Commercial entrepreneurship implies entry into
business activities that rely on market exchange structures with the entrepreneur’s objective
being to maximise profits. Thus, commercial entrepreneurs capture privately the residual
value created within their enterprise. In contrast, social entrepreneurship definitions
commonly stress that the objective of the organization is to address social rather than
commercial needs, by social wealth creation (Mair & Marti, 2006; Zhara et al., 2009). Santos
(2012) moves this literature forward by observing that the typical activities by social
entrepreneurs can be conceptualised as generating positive externalities – value that is created
7
by the enterprise primarily accrues to wider society but will probably not be captured
privately within the enterprise. Of course, commercial entrepreneurs may also generate
positive externalities (e.g., through generating employment), as well as negative ones (e.g.
pollution). However, the generation of positive externalities can be viewed as the objective of
social entrepreneurship rather than a potential side effect as in commercial entrepreneurship.
Past research has highlighted how the social goals and positive externalities pursued
by social entrepreneurs add greater complexity to their activities compared to commercial
entrepreneurs. In contrast to either advocacy or government organizations, social enterprises
mobilize for bottom-up social change through empowerment processes (Santos, 2012;
Stephan et al., 2016). This extends the scope of activities beyond the boundaries of the
enterprise. It typically entails that the enterprise is open to diverse stakeholder influences, is
embedded in local communities, and is ‘relational’ in its approach by shaping networks
across sectors (commercial, non-profit, and government) to stimulate social change as well as
to leverage resources (Stephan et al., 2016). This compares with transactions in commercial
enterprises that are more focussed and address a narrower set of stakeholders (financiers,
suppliers, employees) who are largely aligned with the single goal of value capture within the
organization.
An increasing number of studies document how the different logics of action
associated with social and commercial goals, and the need to consider both simultaneously,
can lead social entrepreneurs into trade-offs, and increase the complexity of decision-making
(e.g., Mair, Meier & Lutz, 2015; Battilana & Lee, 2014, for a review). For instance, to gain
legitimacy and resources from funders, social entrepreneurs need to appeal to a commercial
logic, demonstrating their management capability and perhaps offering dividends, aligning
themselves with respective industry organizations. Yet to be credible to their beneficiaries,
they may be expected to closely engage with local stakeholders, to measure social impacts, to
8
reinvest profits in social impact creating activities, and to embed themselves in cross-sector
networks and partnerships (e.g., Austin et al., 2006; DiDomenico, Haugh & Tracey, 2010;
Pache & Santos, 2013).
2.2. Human Capital and Entrepreneurship
The traditional economic analysis of an individual’s choice to become an entrepreneur
focuses on alternative occupations in the labour market; paid employment as against
entrepreneurship (Lucas, 1978). Rational utility maximising individuals choose to become
entrepreneurs if the expected utility they gain from it is higher from that obtained from paid
employment. Greater expected utility from entrepreneurship may be caused, for example, by
differences in ability generated by variation in human capital (Van Praag, 2005); by
differences in attitudes to risk (Khilstrom & Laffont, 1979); or by differences in utility
functions, for example placing greater emphasis on non-pecuniary rewards such as
independence and job satisfaction (van Praag & Versloot, 2007). There have been as yet few
applications of this framework to the phenomenon of social entrepreneurship, though Parker
(2008) develops a life cycle model in which differences in time preferences lead individuals
to become social entrepreneurs at different points in their lives.
We seek to extend economic modelling of the occupational choice of entrepreneurship
to include social entrepreneurship by focusing on the differential effects of human capital.
Our approach draws on the distinction between general and specific human capital (Becker,
1964), with general skills being typically acquired through formal education, and specific
skills via experience (Unger, Rauch, Frese & Rosenbusch, 2011). In line with extant
literature; we consider the determinants of entrepreneurial occupational choice to be the
returns from general and specific human capital, compared to their opportunity costs (Le,
1999; Davidsson & Honig, 2003; Parker, 2011, Unger et al, 2011). However, relative to
9
commercial entrepreneurship social entrepreneurship entails broader objectives and activities,
which may alter the balance of advantage of general versus specific human capital. We argue
that the main difference will be through the effect of general human capital; the broader scope
of the goals and activities of social entrepreneurs will draw relatively more intensively on
general compared to specific human capital.
The goals of social and commercial entrepreneurs are sufficiently differentiated that
one might expect each activity to attract individuals characterised by quite different
motivations, or value sets (preferences)2. Yet motivation cannot be the sole differentiator of
social and commercial entrepreneurship; in practice other-regarding values may also be
realised both by those in salaried employment and in purely commercial entrepreneurship,
outside their occupation, for example by charitable activity and giving. Thus, it is also the
ability to realise benefits from the joint supply of a commercial product/service and positive
external effects, which induces an individual to create a social venture. Both of these
elements –the abilities set and the values set– are defined by his/her human capital
characteristics. Hence, we need to understand the features of human capital which drive the
choice of commercial and social entrepreneurship, which will include both values and the
relative rates of return to human capital.
2.2.1 General Human Capital and Entrepreneurship
Investment in general human capital has positive effects on both entrepreneurial entry
and performance because it leads to a broad knowledge base which enables individuals to
integrate new knowledge and adapt to new situations more easily (Lazear, 2005). In
particular, it enhances the individual’s ability to discover and exploit opportunities (e.g.,
2 Values and motivations differ in their level of abstraction, although the terms are often used interchangeably.
Values refer to general life goals and are more abstract than motivations which typically are focussed on specific
objects or events. In the economics literature, both concepts are typically referred to as preferences. Shalom
Schwartz introduced a general theory of values which is now corroborated by evidence from over 80 countries.
It differentiates two broad value dimensions including self- and other-regarding values and openness to change
vs. conservation values, as well as 10 more specific value types (see Schwartz, 2012 for an introduction).
10
Davidsson & Honig, 2003; Unger et al., 2011). However, empirical research linking
education to commercial entrepreneurship entry yields a mixed pattern (also Le, 1999). Some
studies report that education is positively associated with the likelihood to engage in
commercial entrepreneurship (e.g., Arenius & Minniti, 2005; Block, Hoogerheide & Thurik,
2013; Minniti, Bygrave & Autio, 2005b; Parker, 2009, 2011). Other studies find no
relationship (Van der Sluis, van Praag & Vijverberg; 2005, 2008). Finally, some research
suggests that it is important to consider the type of education: beyond secondary education,
higher education may not have an additional positive effect on entry into commercial
entrepreneurship (e.g., Parker & Belghitar, 2006). This is possibly due to rising opportunity
costs, because more highly educated individuals are likely to be offered managerial jobs in
wage employment that like entrepreneurship entail considerable decision latitude and variable
incentives – yet entail less risk bearing3.
Interestingly, there are as yet no systematic analytical studies about the effects of
education on social entrepreneurship4. Given its broader scope, higher levels of education
may be particularly important to identify and exploit opportunities for social
entrepreneurship. While investment in education is likely to increase the returns to
commercial entrepreneurship relative to alternative occupations, these returns may be even
higher in social entrepreneurship. Note that to conform to the objective of social welfare
maximisation for social entrepreneurs, returns must be defined broadly to incorporate the
overall value generated by the enterprise, whether the residual is captured privately or not.5
Psychological approaches stress that education, and especially higher education, has a
two-fold socializing effect. It enhances flexibility, openness and independent thinking (Kohn
3 The findings on human capital may also be confounded with results on financial capital (Le, 1999); in our
study we will proxy for the latter.
4 Although descriptive findings indicate that the relationship is positive (Terjesen, Lepoutre, Justo & Bosma,
2012).
5 Bacq et al. (2014) find that in relative terms, the weighting of entrepreneurial skills in human capital is lower
for social than for commercial entrepreneurs, who may be superior in general skills; those skills enable them to
identify the nature of positive external effects.
11
& Schooler 1983; Schwartz, 2008) – as also emphasized by economic approaches. In
addition, higher education has been found to enhance other-regarding values and engagement
in self-initiated, pro-social actions such as volunteering and political activism (Abrahamson
& Inglehart, 1994; Schofer & Fourcade-Gourinchas, 2001; Schwartz, 2008, 2010). Thus,
higher education instils preferences and motivations consistent with the core aspiration of
social entrepreneurs to contribute to the welfare of others and to create societal wealth
(Stephan, Uhlaner & Stride, 2015).6 These motivations are less likely to sit comfortably with
commercial entrepreneurship (Lukes & Stephan, 2012; Noseleit, 2010). Thus, we argue that
higher levels of education may have a more pronounced effect on social as against
commercial entrepreneurs. This is because socialization element of education might favour
both a better understanding of the more complex nature of social entrepreneurial
opportunities and the objective of social welfare maximisation rather than profit
maximization (see also, Nga & Shamuganathan, 2010). Through socialization, higher levels
of education may therefore introduce a ‘pro-social bias’.
Taken together, the socializing effects of education and human capital theory lead us
to expect relatively stronger effects from education (general human capital) on social than
commercial entrepreneurship: (i) adopting other-regarding values is a necessary condition of
social entrepreneurship; and (ii) the latter also requires a broader set of skills conducive to
identifying opportunities in producing positive external effects.
Hypothesis 1a: Individuals who have completed tertiary (higher) education have a
greater likelihood to choose social, compared to commercial, entrepreneurial entry.
The same reasoning does not apply to specific (entrepreneurship related) human
capital as we elaborate next.
6 There may also be a self-selection effect such that those with pro-social values self-select into higher
education.
12
2.2.2. Specific Human Capital and Entrepreneurship
With respect to specific entrepreneurial human capital, social or commercial
entrepreneurial activities have much in common. Many aspects of the venture creation
process are the same for both, such as the necessity to identify opportunities, to bear risk, and
to organise resources (e.g., Dacin et al., 2010; Meyskens, Robb-Post, Stamp, Carsrud &
Reynolds, 2010; Reynolds, 2011). This would suggest that individuals with similar specific
(entrepreneurial) skills and resources would choose to set up social and commercial
enterprises in preference to accepting paid employment. Yet there may also be differences in
terms of opportunity cost.
In economic models of occupational choice of entrepreneurship, individuals seek
careers that will maximize their benefits from their human capital, and unlike for their
general human capital, specific entrepreneurial capital may entail lower opportunity costs.
Individuals with entrepreneurial skills choose entrepreneurship over paid employment when
the returns from the former exceed the market wage from employment. Entrepreneurial skills,
i.e. the specific know-how related to the starting and running of a business, are often
measured through past start-up experience (Unger et al., 2011). Such skills involve being
sensitive to opportunities and crafting business models to exploit them as well as being
proactive and finding solutions to the various obstacles in the way of creating a new business.
These skills are not necessarily valued in wage employment where the division of labour
gives rise to organizational hierarchies and processes that typically require some degree of
compliance as well as deeper subject matter expertise. Entrepreneurial skills may weigh less
heavily in the set of social entrepreneurs’ skills; they are likely to be only one important
ingredient alongside other skills necessary in creating positive externalities (and which are
captured by general human capital). This contrasts with commercial entrepreneurship, where
13
specific entrepreneurial skills generally yield stronger effects than general human capital (i.e.
education, Unger et al., 2011). That in turn implies higher opportunity cost of the former for
commercial entrepreneurship.
Thus while specific human capital is important for social as well as commercial
entrepreneurship, the relative weight of specific human capital is higher for the latter.
Hypothesis 1b: Individuals who have specific (entrepreneurial) human capital have a
greater likelihood to choose commercial, compared to social, entrepreneurial entry.
2.3 Rule of Law and Entrepreneurship
The relationship between human capital, both general and specific, and an
individual’s occupational choice will also be sensitive to the institutional context in which
those choices are made. We have argued that investment in human capital through acquiring
entrepreneurial skills and through formal education enhances the individual’s ability to
discover and exploit opportunities. Moreover the socialization effect of education may lead
people with higher education relatively to favour social over commercial entrepreneurship.
But this analysis pertains to each particular national context. When considering social as
against commercial entrepreneurship, the key contextual element is the institutional structure
because, as stressed by North (1990) and Baumol (1990), institutions shape private incentives
by defining individual returns and opportunity costs. Therefore the national institutional
framework affects individual choices about engagement in entrepreneurship, including entry
into social as against commercial entrepreneurship (Baker, Gedajlovic & Lubatkin, 2005;
Estrin et al, 2013a).
Institutional arrangements in a particular country affect the balance of returns from
different occupations. Baumol (1990) analysed this phenomenon by hypothesising that
institutions create incentives which make it more or less attractive for individuals to pursue
14
different forms of entrepreneurship: productive, unproductive and destructive. In many
countries, the main threat to entrepreneurial success is expropriation or graft and a strong rule
of law limits or prevents that (Acemoglu & Johnson, 2005). This implies that the relationship
between both general and specific human capital and entrepreneurial activity may vary
significantly, depending on the strength of the rule of law that in turn leads to security of
property and other economic rights (Epstein, 2011).
In particular, the rule of law influences the extent to which the potential returns from
human capital investments can be captured by the individual and his/her enterprise. For
example in the former Soviet Union, where entrepreneurial activities were largely illegal, the
impact of higher education on incomes through the choice of an entrepreneurial career was
much lower than in countries such as the US, where the rewards to entrepreneurship are
more protected (Aidis, Estrin & Mickiewicz, 2008). In addition, in totalitarian societies, such
as the former Soviet Union, education itself may be biased in such a way as to hinder the
development of entrepreneurial skills such as opportunity recognition (Aidis et al., 2008).
In situations where the rule of law is weaker, so that the threat of expropriation is
greater, the appropriable returns from human capital in entrepreneurship compared to
alternative occupations are skewed against entrepreneurship. Those with access to state
power or means of violence (officials or criminal gangs) can seize the rewards of successful
entrepreneurs because the latter are relatively few in number, easily identified and perhaps
not strongly supported socially (Aidis et al., 2008). It is harder to seize a share of an
individual’s income from employment because their numbers are much greater and because
social cohesion supporting that group will be stronger.
These arguments can be refined to distinguish between the effects of the rule of law
on social as against commercial entrepreneurship. Estrin et al. (2013b) argue that the returns
to entrepreneurship, as against paid employment, may be negatively affected by weak
15
institutions. Building on Williamson’s (2000) hierarchy of institutions, they argue that the
most fundamental layer of formal institutions for commercial entrepreneurs is the
constitutional level, in particular the effective constraints on the executive branch of
government that constitute the rule of law and lead to protection of property and other
economic rights (Epstein, 2011). In contrast, corruption and expropriation probably represent
the greatest threat to entrepreneurial returns in countries in which the rule of law is weaker
(Acemoglu & Johnson, 2005). A weak rule of law raises the costs of doing business and
thereby reduces the returns to be gained from owning and running one’s own venture, as
against the opportunity cost, paid employment. Where the threat of expropriation is higher,
the expected return to entrepreneurship that can be captured privately by the new organisation
is reduced. Hence the rule of law is crucial for commercial entrepreneurs in allowing them to
capture the returns from their own innovations (e.g. Estrin et al., 2013a; 2013b).
The implication of the rule of law may differ for social entrepreneurship. For
commercial entrepreneurship returns are relatively easily identified and subject to
expropriation when the rule of law is weak. In contrast social entrepreneurship produces
positive external effects, but the financial gains to the enterprise may be modest or non-
existent. In consequence, social entrepreneurs may be less prone to expropriation under a
weak rule of law; a surplus is not easy to confiscate if shared and spread thinly across
thousands of micro credit recipients, for example. Thus the deterrence effect of a weak rule of
law may be less binding on social entrepreneurs. Our argument here is consistent with the
view that social entrepreneurship is more likely to be prevalent where institutional voids are
pronounced (Mair & Marti, 2009)7.
7 The meaning of institutional voids differs in the literature that investigates context and social entrepreneurship
sometimes referring to voids related to weak constitutional level institutions (i.e. weak rule of law) and at other
times to weak government provision of services (Estrin et al., 2013a; Hoogendoorn and Hartog, 2011; Mair et
al., 2013; Stephan et al., 2015). These differences are not our focus here. Low scores on our measure of
constraints on the executive (rule of law) are similar to other conceptualisations of constitutional level
institutional voids. In addition, we include a set of country-level control variables that proxy for institutional
voids generated by lack of government provision of services.
16
Variations in the rule of law may help to explain the cross-national differences in the
returns to human capital observed in previous research on commercial entrepreneurship
(Arenius & Minniti, 2005; also Van der Sluis, Van Praag & Vijverberg, 2005; 2008 for
reviews). With a strong rule of law, entrepreneurial success is to a greater extent due to
entrepreneurs’ individual training, skill and effort rather than to external circumstances and
relationships, and thus becomes a relatively more attractive choice for those with more
valuable human capital. Individuals with higher levels of both general and specific human
capital are likely to be in a position to identify, create and exploit business opportunities of
greater value than their less well educated counterparts, because of their greater perceptual
awareness, ability to learn, technical and entrepreneurial skills (e.g., Van Praag, van
Witteloostuijn, & van der Sluis, 2013). A weaker rule of law is likely to constrain the
potential of entrepreneurs characterised by valuable human capital relatively more, because
their upside gains are threatened to a greater extent. Thus, we hypothesize a moderating effect
of the rule of law on the propensity of individuals with more valuable human capital, both
general and specific, to engage in entrepreneurship.
We further posit that the positive moderating effect of the rule of law on the
relationship between human capital and entrepreneurial activity may be stronger for
commercial entrepreneurs. While a weak rule of law reduces both financial and social returns
the threat of expropriation is stronger for successful commercial entrepreneurs. Financial
returns to social entrepreneurs may be smaller and social returns more difficult to identify and
expropriate than the commercial returns captured within a new organisation. These arguments
suggest that weak rule of law may have a greater negative effect on commercial venture
creation by those with more valuable human capital than on social entrepreneurship. We
hypothesize:
17
Hypothesis 2a: Where the rule of law is stronger, the likelihood of commercial
entrepreneurial entry of those with general human capital is greater relative to social
entrepreneurship.
Hypothesis 2b: Where the rule of law is stronger, the likelihood of commercial
entrepreneurial entry of those with specific human capital is greater relative to social
entrepreneurship.
Figure 1 summarizes our hypotheses.
{Insert Figure 1 here}
3. Methods
3.1 Sample, Measures and Modelling Strategy
We merge Global Entrepreneurship Monitor (GEM) data in 2009 with a large group
of independent country-level institutional indicators and macroeconomic controls from
different sources.8 Though social entrepreneurship is not part of the regular GEM survey, it
was included in 2009 as a special topic, and this provides the basis for our individual level
dataset. We exclude some countries based on quality issues, following a recommendation in
the GEM 2009 report (Terjesen et al., 2012), and opt for a rich model, with low omitted
variable bias, yielding a usable set containing 68,885 observations from 37 countries. Our
hypotheses focus on the differential effect of general and specific human capital on the
likelihood that an individual starts a social or a commercial enterprise. Hence, in our core set
of models, we constrain our sample to those respondents currently in the start-up process.
8 With very few exceptions, the data consist of representative samples of at least 2,000 individuals in each
country. The samples are drawn from the working age population which avoids the potential selectivity bias that
could affect studies which focus on existing entrepreneurs. National datasets are harmonised across all countries
included in the survey (Reynolds et al, 2005).
18
This leaves us with a sample of 6,901. In robustness checks we also use a design where
individual decisions to engage in the two types of entrepreneurial entry are contrasted with
those of people not engaged in start-ups at all, that is based on the full 68,885 observations.
Table 1 provides an overview of definitions and sources for all variables in this study. Table 2
lists the 37 countries and descriptive statistics for the variables central for this study for each
country.
{Insert Tables 1 and 2 here}
3.1.1 Social and Commercial Entrepreneurship
For our core set of results, we apply a multilevel logit model comparing the individual
likelihood of social and commercial entrepreneurial entry respectively. Our baseline category
is commercial entry (coded “0”); social entry is coded “1”. We conduct robustness tests
focussing only on those who undertake start-up activity whilst being in paid employment
(Table 5 columns 2 and 3). As mentioned above, in further robustness checks we utilise the
whole sample and test our hypotheses using multinomial multilevel modelling with three
possible outcomes: no engagement in employment, those engaging in commercial start-up
and those engaged in social start-up. In all these cases our results correspond to those
reported below as the core.
In this study, we define social and commercial entrepreneurial entry in terms of start-
up or nascent activity. To be classified as starting-up or nascent entrepreneurs in GEM,
respondents answered affirmative that (a) they are alone or with others are currently trying to
start a new business, (b) they have actively taken action to start the new business over the
past 12 months, (c) they will at least part-own this business, and (d) they have not paid
wages, salaries, or ‘in kind’ for more than three months. Respondents were asked a
corresponding set of questions about starting and owner-managing “any kind of activity,
organization or initiative that has a particularly social, environmental or community
19
objective” to be identified as social entrepreneurs. Respondents who stated that their social
entrepreneurial activity was the same as their commercial entrepreneurial activity (declared
earlier in the survey) were treated as social entrepreneurs, as in Hoogendoorn and Hartog
(2011).
3.1.2 Individual-Level Predictors (H1): Human Capital
As our preferred measure of general human capital, we consider tertiary (higher)
Education to be the relevant aspect of human capital accumulation. This is because it is to a
significant extent under the control of the individual and has been closely associated with
identification of opportunities in entrepreneurship (e.g., Van Praag et al., 2013). Education is
measured with four categories indicating whether the individual’s highest completed level of
education is tertiary education, secondary education, incomplete secondary, or less than that.
The latter is the reference category in our regressions.
We use Entrepreneurship experience as a proxy for specific human capital, in line
with the conceptualization of specific human capital as skills obtained through relevant,
specific practice (Becker, 1964). We construct this indicator based on two questions in the
GEM survey. We coded respondents as possessing entrepreneurship experience if they
answered affirmatively that (a) they have sold, shut down, discontinued or quit a business in
the past 12 months that they owned and managed, and (b) that this business continued to exist
after the respondent departed from it. Thus, our indicator of specific human capital captures
recent start-up experience where the venture had some degree of success.9 All human capital
measures come from the GEM survey.
3.1.3 Individual-Level Control Variables
9 We add as a control variable whether a respondent is currently running a commercial or social enterprise as
owner-manager. However, we do not use this as our measure of specific human capital as it confounds effects of
skills with opportunity costs when the decision to start another business is taken.
20
We include a set of individual level control variables taken from the GEM adult
population survey. Previous research shows that men, middle-aged, and people in
employment are more likely to start a business (Reynolds et al., 1999; Minniti et al., 2005a,
2005b). We include a dummy variable for gender with female =1 (Female), introduce the
individual’s Age (also in a quadratic form) and employment status (In employment) as control
variables to address these possibilities. We also control for an individual’s commercial and
social entrepreneurship engagement (CE engagement and SE engagement), defined as
currently running a business, measured through currently being a young or established
entrepreneur in the GEM dataset. Young or established entrepreneurs are those whose
business has paid salaries, wages or in kind for three months or longer and they own and
manage that business.
Past research points to the importance of access to capital for potential entrepreneurs
who engage in the start-up process, both commercial (e.g., Ho & Wong, 2006; Korosteleva &
Mickiewicz, 2011) and social (Meyskens et al., 2010). It has also been identified as critical to
include alongside human capital variables, as the two forms of capital are correlated, and
otherwise an omitted variable bias could result. We proxy access to capital first through the
GEM question whether the respondent has been a Business Angel in the past 3 years. In our
robustness checks, we also control for respondents’ household income, measured as being in
the lower, middle or upper third of household incomes in the respondent’s country of
residence. The inclusion of household income reduced the available sample size, and more
importantly, missingness in income is highly correlated with age, gender and employment
status; therefore we do not have the same level of confidence in these tests.
We also control for knowing an entrepreneur as it influences individual’s engagement
in business start-up positively (e.g., Arenius & Minniti, 2005; Wagner & Sternberg, 2004).
Finally we control for the Fear of failure. While recent evidence suggests that risk aversion
21
does not differ between entrepreneurs and non-entrepreneurs; the loss aversion does.
Intuitively, fear of failure captures the idea that the potential loss from entering
entrepreneurship is weighed more heavily than the potential gains, i.e. loss aversion, and this
is the dimension that has been confirmed experimentally as relevant for entrepreneurs
(Koudstaal et al., 2014).
3.1.4 Country-Level Predictor: Rule of Law (H2)
We measure rule of law using the Polity IV indicator of efficient constraints on the
arbitrary power of the executive branch of the government, Constraints on the executive.
Compared to other indicators of institutional quality, this measure has the advantage of
capturing the arguably the key necessary condition of the rule of law (Epstein, 2011), which
in the context of entrepreneurial activity, link in the obvious way with the risk of
expropriation (Acemoglu & Johnson, 2005). As robustness checks we include results which
substitute for these variables with alternative measures of the rule of law, in particular the
Rule of Law indicator from the Freedom House dataset and the Rule of Law indicator drawn
from the World Bank Worldwide Governance Indicator database (as compiled by the
comprehensive Quality of Government database: Dahlberg, Holmberg, Rothstein, Khomenko
& Svensson, 2016)10. However we consider the Polity IV measure superior to the other two:
as argued by political scientists, Polity IV stands out as a highly transparent and robust set of
measurement ranked higher than that constructed by Freedom House (Munck & Verkuilen,
2002). The one offered by World Bank is probably even further away from the rule of law
concept, as it merges a number of disjoint dimensions into one factor. In addition, compared
with the other two measures, Polity IV also has weaker correlation with GDP per capita,
alleviating multicollinearity concerns.
10 The Quality of Government database also contains the rule of law measure from Bertelsmann Stiftung.
Unfortunately this cannot be used in our empirical work because it only covers a subset of countries: developing
economies.
22
3.1.5. Other Country-level Control Variables
Our empirical framework also requires controls at the country level to alleviate
omitted variables bias. Social and commercial entrepreneurship are known to vary with a
country’s level of economic development; we control for this using Per capita GDP at
purchasing power parity (World Development Indicators), in logarithm to allow for the
expected nonlinear relationship. We further control for the level of government activism,
which past research found to impact both social and commercial entrepreneurship (Fogel,
Hawk, Morck and Yeung, 2006; Estrin et al., 2013b), using a measure of the size of the
government based on Wall Street Journal / Heritage Foundation data.11 We also control for the
national level of unemployment to capture for the state of the labour market (and therefore the
opportunity cost of entrepreneurship) and likewise for the share of the working age
population in overall population, to help us to separate the individual age effects from the age
structure in the environment. We lag all these variables by one year to reduce potential
endogeneity. Importantly, this set of country-level control variables also allows us to account
for possible confounding effects of the extent of social needs, i.e. ‘opportunities’ for social
entrepreneurship.
We also control for the level of existing entrepreneurial activity, both social and
commercial, in each country, by including the national rate of social and commercial young
and established enterprise owners respectively. We also include the country level means of
higher education attainment to ensure that the individual effects of general human capital are
isolated more finely. Similarly we add country level prevalence rate of business angels, in
addition to the individual effect.
While our primary interest is in formal institutions, we also control for a potential
impact of informal institutions. For that purpose we follow both Reynolds (2011) and
11 We follow Reynolds (2010) and transform this back to the simple ratio of government expense to GDP.
23
Hechavarria (2015) and include two cultural scales based on World Value Survey: one spans
from “traditional” to “secular-rational” culture, the other from “survival” culture to “self-
expression” culture closely related to postmaterialism values (Stephan et al., 2015).
Definitions of all variables discussed above are reported in Table 1, the correlation
matrix of the individual level data is presented in Table 3, and correlations at the national
level are presented in Table 4.
{insert Tables 3 and 4 here}
3.2 Estimation
We follow Autio and Acs (2010) and Estrin and Mickiewicz (2011) amongst others in
using multilevel modelling within the context of a cross-country, cross-individual
entrepreneurship dataset. Multilevel modelling takes account of the fact that our dataset has a
hierarchical structure in which individuals represent level one and countries represent level
two. This allows us to control for unobserved country level heterogeneity related both to
macro factors that are not directly included in the model and to sub-sample specific
measurement errors. At the same time, we address the problem of unit dependencies, where,
for example, two respondents from the same country in the same year are more likely to
exhibit similar patterns in their behaviour. In this case, the independence assumption does not
hold, and a multi-level, random effects model should be employed (Peterson, Arregle, &
Martin, 2012; Rabe-Hesketh, Skrondal & Pickles, 2005). We test the significance of the
country effects. For the null model, where we only include random country effects, the intra-
class correlation (ICC) is 0.137 and highly significant (p<0.001). This supports the use of
multi-level modelling. Based on Model 1 (with all variables of interest including our
predictors) in Table 5, the intra-class correlation (ICC) decreases to 0.033. Yet it is
24
significantly different from zero (p<0.001): our country level variables still leave some
overall country level variance in dependent variable unexplained.
We estimate a multilevel logit model for social and commercial entrepreneurship,
taking commercial start-up activity as a baseline category and present odds ratios (OR)
instead of coefficients for ease of interpretation. Thus positive effects (OR>1) mean that a
variable has a stronger effect on social as compared to commercial start-up. By comparison,
negative effects (OR<1) mean that a variable has a stronger effect on commercial as opposed
to social start-up. The drawback in using ORs is that as these represent responses to unit
change in independent variables, the values for country prevalence rates will be very high.
Table 5 presents the results per estimation model, where the second column “Model 0”
present a baseline model with all control variables but not predictor variables included.
{insert Table 5 here}
4. Results
4.1 Education and Entry into Social and Commercial Entrepreneurship (H1a, H1b)
Model 0 of Table 5 presents the baseline model containing control variables only.
Model 1 adds our predictor variables based on which we evaluate H1a and H1b. The data
support H1a; we see a positive effect of tertiary education (OR = 1.35, p<0.05) meaning that
it has a stronger positive effect on social compared to commercial entry. In support of H1b,
we observe a negative effect of entrepreneurship experience (OR = 0.63, p<0.01) meaning
that it has a stronger (positive) effect on commercial compared to social entry.
4.2 The Moderating Effect of Institutional Quality (H2a, H2b)
To evaluate the moderating effect of institutional quality on human capital, we first
add the interaction between executive constraints and higher education in Model 2, and next
25
we introduce the interaction between executive constrains and entrepreneurial skills in Model
3. Finally, as the most stringent test of our hypotheses, we include both interactions in Model
4 (all in Table 5).
The results provide support for H2b, but not for H2a. Institutional quality makes a
significant difference to how people use their specific entrepreneurial skills (OR = 0.82,
p<0.05, Model 4), but affects the use of their general human capital less (OR = 0.97 n.s.,
Model 4). This can be seen as fundamentally consistent with Baumol’s (1990) perspective: it
is the use of entrepreneurial talent that is predominantly affected by institutions. We plot the
significant moderating effects in Figure 2 displaying the association of entrepreneurial skills
with the likelihood to choose social over commercial entry. We see that the association of
entrepreneurial skills with commercial entry is stronger in the presence of strong (vs. weak)
rule of law (executive constraints).
4.3 Further Results
Some additional results are noteworthy. First, while the rule of law (executive
constraints) does not have any impact on the choice between social and commercial
entrepreneurship, this should not be interpreted as lack of evidence for a positive net effect of
the rule of law on entry. Additional multilevel multinomial estimations run on the whole
sample, showed a positive effect of the rule of law on entry into both types of
entrepreneurship when contrasted with no entrepreneurial activity.
Second, current engagement in a social enterprise (SE engagement) has a consistent
significant positive effect on the choice to start a new project as a social versus commercial
enterprise, with the corresponding relative odd ratios remaining remarkably stable and high
(OR = 4.66, Model 1 Table 5). This suggests that individuals, who are first attracted to social
entrepreneurship, become serial social entrepreneurs.
26
Third, current engagement in a commercial enterprise (CE engagement) also has a
positive effect on choosing social as opposed to commercial entry (OR = 2.59, Model 1 Table
5). This is very interesting: Estrin (2013a) identified a route leading from social to
commercial projects, but here we also see the evidence for a positive spillover in the opposite
direction – entrepreneurs currently running a commercial business are also more likely to
start a new project as a social compared to a commercial enterprise. However, high rates of
commercial entrepreneurship engagement in a country privilege commercial over social entry
(OR = 0.007, Model 1 Table 5).
Fourth, we also observe a highly significant positive effect of gender on likelihood of
choice of social versus commercial entry (OR = 1.65, Model 1 Table 5): women are relatively
more likely to become social than commercial entrepreneurs supporting Hechavarria et al.
(2012) and Terjesen et al. (2012).This suggests that socially oriented projects could be an
important channel for women to enter into entrepreneurship.
Fifth, being in employment has a negative effect on the choice of social over
commercial entry (OR = 0.649, Model 1 Table 5). This suggests that those not in employment
are more likely to choose social rather than commercial entrepreneurship, and thus that social
entrepreneurship may also be an important entry channel for those currently detached from
the labour market.
4.4 Robustness Tests
We conducted a range of robustness checks with support our findings. The full results
are available from the authors upon request.
First, we replicated the results in Table 5 using the indicator of entrepreneurial skills
based on self-assessment instead of measure of experience with earlier projects. In this
variant, the interaction effect with rule of law for H2b was only significant at p<.10.
27
Second, we repeated all the estimations restricting the sample only to those in
employment, which could be seen as a design that comes closer to the logic of the
occupational choice (Le, 1999). The results for all hypotheses and significance levels are
exactly as in Table 5.
Third, we used alternative measures of the rule of law. Applying the Freedom House
measure instead of Polity IV makes no difference to significance levels for our hypotheses.
Using World Bank measure weakens the results for the rule of law. This may result both from
the fact that the conceptual basis of their measure is weaker being more a catch-all factor
rather than anchored in theory (Langbein & Knack, 2010), and it suffers from very high
multicollinearity with the level of GDP per capita.
Fourth, we replicated our specifications running multilevel multinomial logit models
with two outcomes: entry into commercial and social entrepreneurship, as contrasted with no
entry. Here, to test our hypotheses we relied on post-estimation tests for differences in
coefficients between social and commercial entry (Wald tests). The significance and pattern
of results for the hypotheses was the same as in Table 5.
Finally, we also ran models controlling in addition for household income. This did not
change our results and the income effects were always insignificant. Therefore we did not use
them in our main specifications.
5. Discussion
This multi-level study compared two forms of human capital as drivers of social and
commercial entry whilst simultaneously considering contextual effects of the rule of law. We
found that general human capital is relatively more important for social entrepreneurship
while specific human capital is relatively more important for commercial entrepreneurship.
28
Furthermore the rule of law moderated the effects of specific human capital on
entrepreneurship.
This study advances research on how and why human capital influences
entrepreneurial entry by outlining important contingencies of this relationship related to
heterogeneity amongst entrepreneurs and across national contexts. Past research on human
capital largely focusses on the consequences of human capital for firm performance (e.g.,
Rauch & Rijsdijk, 2013; Unger et al., 2011, van der Sluis, van Praag & Vijvenberg, 2005,
2008; Van Praag et al., 2013), whilst the effects of human capital on entrepreneurial entry are
less well understood. Indeed, previous research generating mixed findings (e.g., Block et al.,
2013; Le; 1999; Parker, 2009). Our study advances the latter in two ways.
First, through the novel application of a human capital lens to the analysis of social
and commercial entrepreneurship, our study advances and broadens the conceptualization of
human capital effects. In particular we include the ability enhancing effects of human capital,
as stressed in economic theory, and the hitherto overlooked socializing effects of education in
terms of (pro-social) preferences. For human capital theory more broadly, our study
highlights the importance of considering heterogeneity in the effects of general human capital
on both individuals’ skills and preferences, which helps to explain why different types of
human capital may lead to different entry modes (social versus commercial).
Our analysis of human capital and social entrepreneurship also illustrates the need for
a broader view of returns to occupational choices. We find that general human capital is of
particular relevance in situations where the external benefits of occupational choices are
greater and add to the purely private returns, as occurs with social entrepreneurship. The
implications extend beyond the analysis of social entrepreneurship. The literature documents
a wide variety of possible motives for entrepreneurship that go beyond the aim of
accumulating monetary returns – for example for open-source, high-tech entrepreneurs,
29
ethnic and immigrant entrepreneurship, for family businesses, and for those pursing
entrepreneurship to realize other opportunities, for instance for greater personal
independence. Greater consideration of this heterogeneity in returns to entrepreneurship can
help future research to establish an even deeper understanding of how human capital relates
to entrepreneurship and entrepreneurial entry, and likely yields more consistent findings.
Second, our research also offers a more contextualized understanding of human
capital and entrepreneurship by integrating predictions from institutional theory and human
capital theory. We outline how national institutions act as an important contingency
influencing the opportunity costs and potential returns from human capital when it comes to
occupational choices for entrepreneurship. This offers an additional explanation for the mixed
findings in past research on human capital and entry mentioned earlier and helps to unpack
the drivers of the repeatedly observed national variation in the effect of human capital on
entrepreneurship (e.g., Arenius & Minniti, 2005; Parker, 2009; van der Sluis et al., 2005,
2008). Specifically, while effective institutions are important for both commercial and social
entrepreneurship (Estrin et al, 2013a), our results show that the moderating impact of the rule
of law on the returns to specific human capital is more central for the former than the latter
type of entrepreneurship. When the rule of law is strong, it ensures that commercial
entrepreneurs have a better chance of keeping the private returns from their venture. This is
consistent with Baumol’s (1990) perspective that it is the use of entrepreneurial human
capital which is particularly sensitive to the quality of institutions in the environment. In turn,
social entrepreneurship is focussed on the generation of positive external effects rather than
private gains (Santos, 2012); these are more widely dispersed and thus more difficult to
expropriate, and therefore the moderating effects of the rule of law is less important for social
entrepreneurs.
30
Moreover, the underlying resources and opportunities may differ in the different
contexts. For example, entrepreneurial human capital may be rarer in countries where
institutions are weaker (Aidis et al., 2008). Institutional weaknesses may also create more
opportunities for social entrepreneurship (Mair & Marti, 2009), offsetting to some extent the
negative incentive effects. Our results therefore help to explain why social entrepreneurship
may play an important positive role in countries characterised by dysfunctional institutions
and therefore we complement qualitative research in this area (e.g. Mair, Marti & Ventresca,
2012). In addition, the absence of the hypothesized significant moderating relationship
between general human capital and the rule of law merits further careful research, particularly
in environments where institutional voids exist.
Finally, for entrepreneurship theory, our findings contribute to a better understanding
of the similarities and differences between social and commercial entrepreneurship, and they
highlight the important role of human capital and ability next to the commonly considered
differences in motivation between the two types of entrepreneurship. We show that social
and commercial entrepreneurship attract different types of individuals, consistent with the
notion that these two forms of entrepreneurship are differentiated by both the type of human
capital they require, and by the objectives and motivation of the entrepreneurs. For social
entrepreneurship, our research highlights the importance of general human capital. This
complements the field’s focus on motivation as the key driver of social entrepreneurship (e.g.
Dacin et al., 2010; Miller et al., 2012). This is not trivial. Prosocial motivation can be realized
through channels other than social entrepreneurship, and it is only when this motivation is
combined with the opportunity-recognition ability associated with higher education that
individuals engage in the relatively more complex endeavour of setting up a social enterprise.
In so doing, we respond to the call by Parker (2008) to provide a simple but theoretically
grounded typology to understand who becomes a social entrepreneur. We also add more
31
generalizable insights, derived from studying population-representative samples across a
variety of countries, to social entrepreneurship research, a field still dominated by conceptual
and case-based analysis (Gras, Moss & Lumpkin, 2014; Dacin, Dacin & Matear, 2010;
Nicholls, 2010, for reviews).
Our additional findings provide renewed support for the notion that social
entrepreneurship attracts different individuals than commercial entrepreneurship. Women, the
highly educated and those who are not currently in employment are more likely to become
social than commercial entrepreneurs. Together with past findings that social
entrepreneurship is a way into commercial entrepreneurship (Estrin et al., 2013a)12, the
former opens up an important channel for valuable human talent to become entrepreneurs and
(re-) enter the labour market.
5.1 Strengths and Limitations
Apart from being able to draw on population representative samples across a wide
range of countries, a further strength of our study is the use of multi-level modelling which
allows us to test individual-level relationships at the same time as country-effects. This
addresses aggregation and disaggregation biases (Peterson et al., 2012), namely that
relationships observed at one level of analysis (e.g. country-level) may not generalize (and
may be different from that equivalent relationship) at a different level of analysis (e.g.
individual-level).
A limitation of the GEM dataset is the cross-sectional nature of the data, which gives
rise to concerns about reverse causality. Our analyses alleviate such concerns somewhat as
we investigated the effects of human capital that individuals obtained in the past (highest
12 We note that our findings on gender are consistent with earlier findings by Hechavarria et al. (2012) and
Terjesen et al., (2012) as well as Estrin et al., (2013a).
32
degree, past experience in commercial entrepreneurship) and used lagged data for country-
level institutions and GDP. There is a need for future research to address all these questions
and findings using longitudinal data (see: Renko, 2013, for a good example).
The use of secondary datasets such as GEM also restricted the measures of human
capital. We focussed on general education, rather than the specific type of subject studied,
and on obtaining a degree rather than years of schooling. This choice of measures was
probably adequate for our purposes; reputation effects of higher education (which determine
opportunity cost of entry) are arguably contingent on obtaining a degree rather than years of
schooling and the use of highest degree obtained also created greater comparability across
countries. Nevertheless, future research could usefully investigate more fine-grained
measures of education and could also help to unpack the mechanisms, including skills,
motivation and confidence, that link higher education to entrepreneurial entry. In line with
past research, we emphasized the socializing function of education, but we could not control
for alternative explanations. For instance, education effects in part reflect differences in
individual persistence, family background and ability. However, a recent analysis by Block et
al. (2013) suggests that controlling for endogeneity strengthens education effects on
entrepreneurial entry, which in turn implies that those are possibly underestimated rather than
overestimated by us.
We also acknowledge that GEM used a specific question for social entrepreneurship
as an initiative, activity or organization with a particular community, social or environmental
objective. The interpretation of such objectives may vary across cultures. Although to help
interpretation GEM gave specific examples for community and social objectives, but not for
environmental objectives. Thus the latter may be underrepresented in the sample of social
entrepreneurs. As of yet, GEM is however the only large scale database on social
entrepreneurship.
33
Apart from the directions we highlighted already, future research could explore the
moderating effects of “lower level” (in the sense of Williamson, 2000) regulatory national
institutions and policies, which were captured by a control variable (government spending) in
our analyses. Whilst our focus was on the rule of law, informal institutions including culture
and social capital may also influence the higher education – entrepreneurship link, as
suggested by Stephan et al. (2015).
5.2 Policy and Practical Implications
Policy makers, for example in the European Commission, have adopted as an
objective the creation of a favorable environment for the development of social businesses,
because such firms are argued to “contribute to social cohesion, employment and the
reduction of inequalities” (European Commission, 2013). Policy interest in social
entrepreneurship stems from doubts about how much can be achieved towards social goals
from for-profit motivation, and from scepticism about the effectiveness of bureaucratic and
centralised political interventions. In contrast to interventions by the state, social
entrepreneurship generates highly decentralised modes of action, focused on resolving social
problems at a local or relatively small-scale level. Social interventions are thereby
differentiated, drawing from local knowledge, competing and subject to continuous
innovative pressure. Our theoretical framework also emphasizes the joint supply
characteristics of the social enterprise business model, and the commercial logic combined
with focus on realisation of positive external effects, which can make social entrepreneurship
a superior mode of action compared to the civil society charitable action, at least for some
categories of social problems.
Our findings on the relationship between different forms of human capital and of
entrepreneurial entry provides the basis for improved targeting of business policies, as well as
34
of policies related to education including higher education. Our findings suggest that social
enterprises are more likely to be started by those with higher education. For educators,
especially in higher education, this suggests that a greater awareness and support of social
entrepreneurial activities amongst students may be warranted.
We also find beneficial effects from specific entrepreneurial human capital for social
entrepreneurship. This reinforces the case for programmes developing entrepreneurial skills
among all types of students whilst emphasizing that entrepreneurial skills are useful for the
realization of multiple objectives including social objectives. This contrasts with our
experience in which entrepreneurship training and courses primarily focus on commercial
entrepreneurship and on business and engineering students. Although further research is
necessary, our findings seem to imply that those studying social sciences, medicine or
humanities are well positioned to take up social entrepreneurial projects. In turn, those who
build entrepreneurial teams for social objectives should be well aware of the need for
different human capital, as applicable in both commercial and social entrepreneurship.
Moreover, we found that social entrepreneurship attracts new categories of people
into entrepreneurial activity; for example women or the more highly generally educated
(Estrin et al., 2013a; Hechevarria et al., 2012 and Terjesen et al., 2012). Policymakers who
seek to reduce gender discrimination in occupational choice may therefore be interested in
promoting social entrepreneurship. This conclusion is reinforced by the phenomenon of
“crowding in” (Estrin et al., 2013a), i.e. social entrepreneurship helps in developing
entrepreneurial skills with broader applicability. This is an important finding for business
support policies, highlighting that supporting social entrepreneurship may generate positive
externalities such as stimulating commercial entrepreneurship.
Last but not least, our results can be interpreted as offering some predictions about the
role of social entrepreneurship in different institutional environments. We have found that
35
social entrepreneurship projects have a comparative advantage, relative to commercial entry,
in weaker institutional environments. This has important implications for policy design
supporting human development across nations. In particular, while commercial projects may
be risky where rule of law is weak, social entrepreneurship projects are less so. Thus, there
may be deeper reasons why the organisational form adopted for example by Grameen Bank
(Yunus, 2003) makes a good fit with the local environment, and why social entrepreneurship
may be even more efficient in developing countries than it is in the developed countries.
5.3 Conclusion
Our study reinforces the importance of human capital – abilities and skills – for entry
into entrepreneurship. It extends human capital theory to social entrepreneurship and across
national contexts; in doing so we find that national institutions act as important contingencies.
Our findings also enrich existing research on social entrepreneurship and the differences
between social and commercial entrepreneurship. While past research has mainly focussed on
differences in motivation, this paper highlights the importance of taking abilities and skills
into account to understand who is likely to become a social entrepreneur.
36
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Table 1
Definitions of Variables and Descriptive Statistics
Variable Definition Mean S.D.
Dependent variable (baseline category for logit model: engaged in commercial start-up)
Social enterprise Respondent actively involved in social start-up (1, 0
otherwise)
0.42 0.49
Dependent variable (baseline category for multinomial model: not engaged in start-up)
Social enterprise
(SE) start-up
Respondent actively involved in social start-up (1, 0
otherwise)
.027 .
Commercial
enterprise (CE)
start-up
Respondent involved in commercial start-up (1, 0
otherwise)
.037 .
Individual-level variables
Education: Highest level of education (dummy coded, baseline: no education
beyond primary)
Some secondary
Secondary
Tertiary
Respondent has incomplete secondary education
Respondent has completed secondary education
Respondent has completed tertiary education
.308
.348
.344
.
.
.
Entrepreneurship
experience
Respondent sold, shut down, discontinued or quite a
business in the past 12 months that he owned and
managed, and this business continued its activities
after the entrepreneur disengaged
.036 .186
Female Female (1, 0 male) .527 .499
Social entrepreneur
(SE) engagement
Respondent is currently owner-managing a young or
established social enterprise (1, 0 otherwise)
.027 .161
Commercial
entrepreneur (CE)
engagement
Respondent is currently owner-managing a young or
established commercial enterprise (1, 0 otherwise)
.106 .308
Business angel in
last 3 years
“Have you, in the past three years, personally
provided funds for a new business started by
someone else, excluding any purchases of stocks or
mutual funds?” (1- yes, 0 no)
.032 .177
Know an
entrepreneur
Respondent knows an entrepreneur (1, 0 otherwise):
“Do you know someone personally who started a
business in the past 2 years?”. (1= yes, 0 no)
.378 .485
Fair of failure Respondent would not start a business out of fear of
failure (1, 0 otherwise)
.365 .481
Age Age of respondent between 15 and 64 (inclusive) 40.80 13.16
In employment Respondent is currently in full or part time
employment (1, 0 not in employment)
.575 .494
46
Table 1 continued
Variable Definition Mean S.D.
Country-level variables
Executive constraints
(t-1)
Polity IV ‘Executive Constraints’; scores from
1=”unlimited authority” to 7=”executive parity or
subordination”; higher value: less arbitrariness
6.17 1.59
Government
spending (t-1)
Government spending / GDP (authors’ calculations,
based on Heritage Foundation data)
36.67 9.98
% Working Age
Population (t-1)
Percentage of working age population in total
population (based on World Bank)
66.34 4.28
% Unemployment
(t-1)
Percentage share of unemployed in economically
active population in 2008
7.78 3.66
Survival vs. Self-
Expression
Survival vs. self-expression culture, averaged scores
from 1999/2000 and 2005/2008 World Values
Survey
.536 .964
Traditional versus
Rational culture
Traditional vs. rational culture, averaged scores from
1999/2000 and 2005/2008 World values survey
-.031 .803
GDP Gross Domestic Product (GDP) per capita in
purchase power parity (in natural logarithm)
9.84 0.78
Robustness checks
Start-up skill Respondent believes has skills for start-up (1-yes, 0-
no skill)
.527 .499
Household income Head of household’s income, dummy coded
categorised into three groups of equal number of
respondents for each country (baseline: bottom third)
.33
each
Rule of Law
Freedom House
Rule of law as measured in the Freedom House data
base
10.408 4.436
Rule of Law
Freedom House
Rule of law as measured in the World Bank
Worldwide Governance Indicators data base
.644 .216
Source: GEM 2009 unless specified otherwise. (t-1) indicates lagged variables. Institutional variables:
values before mean-centring.
47
Table 2
Country Descriptive Statistics for Main Variables
Country Social relative
to commercial
startup
Entrepre-
-nerial
experience
Some
secondary
education
Secondary
education
Tertiary
education
Executive
constraints
Algeria 0.45 0.02 0.31 0.35 0.28 5
Argentina 0.35 0.06 0.13 0.29 0.40 6
Belgium 0.51 0.05 0.11 0.41 0.41 7
Brazil 0.12 0.09 0.10 0.40 0.11 6
Chile 0.49 0.04 0.11 0.46 0.28 7
China 0.31 0.04 0.32 0.35 0.21 3
Colombia 0.42 0.06 0.14 0.38 0.29 6
Croatia 0.67 0.04 0.11 0.61 0.26 7
Finland 0.45 0.06 0.08 0.42 0.42 7
Germany 0.32 0.01 0.18 0.33 0.49 7
Guatemala 0.20 0.02 0.16 0.21 0.02 6
Hungary 0.49 0.02 0.27 0.30 0.30 7
Iran 0.48 0.06 0.14 0.32 0.30 2
Israel 0.45 0.01 0.07 0.38 0.51 7
Italy 0.54 0.08 0.26 0.49 0.19 7
Jordan 0.36 0.07 0.34 0.32 0.22 3
Korea 0.43 0.04 0.09 0.32 0.58 6
Latvia 0.36 0.03 0.07 0.56 0.37 7
Malaysia 0.02 0.02 0.17 0.30 0.24 5
Netherlands 0.35 0.01 0.08 0.63 0.29 7
Norway 0.50 0.05 0.00 0.40 0.52 7
Peru 0.35 0.06 0.15 0.41 0.20 7
Romania 0.73 0.02 0.14 0.49 0.21 7
Russia 0.44 0.11 0.07 0.07 0.86 4
Saudi Arabia 0.16 0.07 0.15 0.45 0.30 1
Slovenia 0.42 0.03 0.00 0.57 0.37 7
South Africa 0.49 0.03 0.38 0.41 0.07 7
Spain 0.31 0.04 0.31 0.16 0.42 7
Switzerland 0.43 0.03 0.06 0.59 0.32 7
Uganda 0.17 0.06 0.22 0.04 0.07 3
UK 0.32 0.02 0.18 0.42 0.39 7
United States 0.54 0.05 0.10 0.24 0.63 7
Uruguay 0.28 0.04 0.42 0.33 0.17 7
48
Table 3
Correlations among Individual-level Variables; (N=68,885 individuals)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
1 Social relative to
commercial start-up
2
Incomplete secondary
education -0.03
3 Secondary education -0.00 -0.36
4 Tertiary education 0.07 -0.35 -0.55
5Entrepreneurial
experience -0.04 0.02 0.01 -0.02
6 Female 0.06 -0.01 0.01 -0.02 -0.01
7 Age 0.03 0.04 -0.10 0.01 -0.01 0.00
8 Age squared 0.04 0.04 -0.09 -0.01 -0.01 0.00 0.99
9
Existing social
business 0.21 -0.04 0.00 0.05 0.01 -0.01 0.02 0.01
10
Existing commercial
business 0.10 0.01 -0.03 -0.01 -0.04 -0.11 0.03 0.02 -0.01
11
Household income –
middle third 0.01 0.05 0.04 -0.11 0.06 0.03 -0.01 0.00 -0.01 -0.04
12
Household income –
top third -0.00 -0.12 -0.04 0.23 -0.03 -0.09 -0.05 -0.06 0.03 0.07 -0.64
13 Business angel -0.02 -0.02 -0.01 0.03 0.02 -0.06 -0.03 -0.03 0.04 0.10 -0.04 0.05
14 Knows entrepreneurs -0.02 -0.05 0.01 0.05 0.01 -0.12 -0.16 -0.16 0.07 0.12 -0.03 0.11 0.16
15 Fear of failure 0.05 0.02 -0.02 -0.01 -0.02 0.07 0.01 0.00 -0.02 -0.08 0.02 -0.04 -0.03 -0.03
16 In employment -0.07 -0.11 -0.02 0.18 0.07 -0.20 0.00 -0.05 0.06 0.22 -0.04 0.19 0.05 0.10 -0.03
Note. See Table 1 for variable definitions and reference categories.
49
Table 4
Correlations among Country-Level Variables (N=37 countries)
12345678910
1 Executive constraints
2 Prevalence of social start-up 0.20
3 Prevalence of commercial start-up. -0.34 0.04
4 Prevalence of tertiary education. 0.35 0.26 -0.46
5 Prevalence of business angels -0.33 0.08 0.58 -0.34
6 GDP (natural logarithm) 0.59 0.01 -0.54 0.69 -0.47
7 Government spending 0.44 0.24 -0.46 0.51 -0.51 0.59
8 Working age population 0.10 0.02 -0.19 0.50 -0.15 0.56 0.08
9 Unemployment -0.02 -0.32 -0.15 -0.38 -0.10 -0.24 -0.04 -0.11
10 Survival values 0.49 0.43 -0.32 0.40 -0.37 0.68 0.53 -0.09 -0.22
11 Traditional values 0.38 0.27 -0.16 0.56 -0.28 0.58 0.52 0.47 -0.28 0.18
50
Table 5
Multilevel Logistic Regressions on the Likelihood of Social against Commercial Start-up
Model 0 Model 1 Model 2 Model 3 Model 4
Constant 0.548 1.070 1.024 1.085 0.436
(1.437) (2.790) (2.675) (2.841) (1.117)
Individual level control variables
Age 0.966* 0.962* 0.962* 0.963* 0.959*
(0.015) (0.015) (0.015) (0.015) (0.016)
Age squared 1.000* 1.000* 1.000* 1.000* 1.001**
(0.0002) (0.0002) (0.0002) (0.0002) (0.0002)
SE engagement 4.667*** 4.656*** 4.658*** 4.699*** 4.803***
(0.444) (0.444) (0.445) (0.449) (0.504)
CE engagement 2.589*** 2.595*** 2.597*** 2.595*** 2.573***
(0.210) (0.211) (0.211) (0.211) (0.228)
Business angel 0.830* 0.839+ 0.838+ 0.832* 0.871
(0.075) (0.076) (0.076) (0.076) (0.086)
Know entrepreneur 0.931 0.923 0.924 0.925 0.913
(0.054) (0.054) (0.054) (0.054) (0.058)
Fear of failure 1.333*** 1.343*** 1.343*** 1.341*** 1.324***
(0.081) (0.082) (0.082) (0.082) (0.090)
Female 1.271*** 1.265*** 1.267*** 1.268*** 1.303***
(0.071) (0.070) (0.071) (0.071) (0.081)
In employment 0.669*** 0.649*** 0.650*** 0.647*** 0.677***
(0.043) (0.042) (0.042) (0.042) (0.049)
Country(level control variables
Rate young &. 2,422* 1,432+ 1,479+ 1,562+ 830.9+
established SE (9,432) (5,529) (5,718) (6,056) (3,152)
Rate young &. 0.006*** 0.007** 0.008** 0.008** 0.010**
established CE (0.008) (0.011) (0.012) (0.011) (0.015)
Education sec. 3.867+ 3.617+ 3.526+ 3.561+ 2.241
or higher (2.735) (2.543) (2.485) (2.514) (1.606)
Business angel 1,411* 841.3* 889.1* 854.5* 971.4*
(4,269) (2,529) (2,677) (2,579) (2,891)
Logarithm of 0.710 0.629 0.631 0.622 0.675
GDP p.c. (0.210) (0.186) (0.187) (0.185) (0.195)
Government 1.007 1.008 1.009 1.008 1.014
spending (0.010) (0.010) (0.010) (0.010) (0.010)
Working age 1.039 1.048+ 1.048+ 1.049+ 1.054*
population (0.026) (0.027) (0.027) (0.027) (0.027)
Unemployment 1.043+ 1.036 1.036 1.035 1.027
rate (0.024) (0.024) (0.024) (0.024) (0.023)
Survival vs. 1.067 1.078 1.078 1.081 1.038
self-expression (0.141) (0.142) (0.142) (0.143) (0.134)
Traditional vs. 1.016 0.978 0.976 0.976 0.940
rational culture (0.127) (0.126) (0.126) (0.126) (0.119)
51
Table 5 continued
Model 0 Model 1 Model 2 Model 3 Model 4
Effect. constr. 1.065 1.078 1.080 1.083
on executive (0.056) (0.059) (0.057) (0.058)
Individual(level human capital (H1)
Some secondary 0.979 0.978 0.979 1.160
education (0.125) (0.125) (0.125) (0.161)
Secondary 1.048 1.041 1.052 1.170
education (0.124) (0.124) (0.125) (0.150)
Tertiary 1.360** 1.350* 1.365** 1.444**
education (0.162) (0.162) (0.163) (0.186)
Entrepreneurship 0.631** 0.631** 0.559*** 0.541***
experience (0.095) (0.095) (0.089) (0.092)
Cross(level interaction effects (H2)
Tertiary educ.* 0.970 0.966
Exec.constr. (0.035) (0.038)
Entrepr.exper.* 0.781** 0.816*
Exec.constr. (0.062) (0.070)
Log variance of 0.116*** 0.113*** 0.113*** 0.114*** 0.100***
random effect (0.042) (0.040) (0.041) (0.041) (0.041)
Log likelihood -4138.051 -4120.338 -4119.974 -4115.622 -3381.151
Intra-class 0.034 0.033 0.033 0.033 0.029
Correlation1(0.012) (0.011) (0.011) (0.011) (0.012)
Note. Number of observations: 6,901; number of countries: 37. Relative odds ratios (OR), OR>1 indicates positive
effect, OR<1 indicates negative effect. Standard errors in parenthesis; CE – commercial enterprise, SE – social
enterprise; *** p<0.001, ** p<0.01, * p<0.05, + p<0.10; the likelihood ratio test comparing Model 0 (without the
hypotheses-related predictors) with Model 1 renders χ2(5) = 26.63, which is significant at p<0.001. 1 The intra-class
correlation is a measure of residual, i.e. unexplained country-level variation.
52
Figure 1
Multi-level Research Framework and Hypotheses
Country level
Individual level
53
Constitutional level
institutional quality
Country-level
controls
H2a
H2b
General human
capital (tertiary
education)
Entry into
social
entrepreneurship
vs.
commercial
entrepreneurship
H1a
Specific human
capital
(entrepreneurial
experience) H1b
Individual-level
controls
Figure 2
Interaction Effect Specific Human Capital (Entrepreneurial Experience) and Constraints
on the Executive (Institutional Quality) (H2b, Table 5, Model 3)
Low entrepreneurial experience
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
Low rule of
law
Weak rule
of law
(executive
constraints
at 10th
percentile)
Strong rule
of law
(executive
constraints
at 90th
percentile)
P ro b a b i lity o f c h o o s in g s o c ia l c o m p a re d to c o m m e rc i a l e n try
54
