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Small Bus Econ (2024) 62:325–352
https://doi.org/10.1007/s11187-023-00769-z
The well‑being of women entrepreneurs: the role of gender
inequality and gender roles
Inessa Love   · Boris Nikolaev · Chandra Dhakal
Accepted: 8 April 2023 / Published online: 8 May 2023
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023
Abstract  The current study presents new evidence
on the well-being of women entrepreneurs using
data from the World Values Survey for 80 countries.
Results indicate that in low- and middle-income
countries, female entrepreneurs have lower wellbeing
than male entrepreneurs, while in high-income
countries, they have higher well-being. Several macro
and micro-level mechanisms– institutional context,
gender roles, and individual characteristics–that
potentially moderate this relationship are explored.
The gender gap in well-being is larger in countries
with higher gender inequality, lower level of financial
development, and stricter adherence to sexist gender
roles. Additionally, women entrepreneurs with lower
education, more children, and risk-averse preferences
are more likely to report lower well-being. The results
suggest several policy mechanisms that can be used to
enhance the well-being of women entrepreneurs.
Plain English Summary  Women entrepreneurs are
less happy than men in low-income countries, while
the opposite holds in high-income  countries. This
negative effect is stronger for less educated women,
for women with children, and in countries with
greater  gender discrimination, low access to financial
resources, and more traditional gender roles. This
study documents a wellbeing gap between female and
male entrepreneurs in countries with different levels
of economic development. In low income countries,
women entrepreneurs report lower subjective wellbeing
relative to men, while in high-income coun-
tries, women entrepreneurs are happier than men. In
low-income countries, women face more obstacles
and constraints to being  an entrepreneur, such as
lower education, lack of childcare options, lack of
access to finance, unfair legal treatment, and  more
sexist gender roles and traditions. The results are consistent
with the proposition that in low-income countries
women  prefer wage employment. When their
labor market outcomes are limited, they are more
likely to be “pushed” into entrepreneurship and derive
lower satisfaction from their entrepreneurial activities.
The primary policy implications should  aim
at equalizing the playing field for men and women
entrepreneurs, improving labor market conditions,
and increasingwage-earning opportunities for women.
Keywords  Well-being · Women entrepreneurs ·
Institutions · Entrepreneurship · Non-economic
outcomes
JEL Classifications  I14 · I31 · L26
I. Love (*) 
University of Hawaii at Manoa, Honolulu, USA
e-mail: ilove@hawaii.edu
B. Nikolaev 
Colorado State University, Fort Collins, USA
C. Dhakal 
Royal Thimphu College, Thimphu, Bhutan
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1 Introduction
Entrepreneurship scholars are increasingly recognizing
“the importance of studying well-being as a
key outcome in entrepreneurship research” (Lerman
et al., 2021; Nikolaev et al., 2020a, 2020b, 2022; Stephan,
2018; Wiklund et al., 2019, p. 580). In fact, an
increasing number of studies document that majority
of people start new ventures not because they look
for financial gain but because they want greater freedom,
more meaningful work, and an outlet for creative
expression (Dellot, 2014; Parker, 2021; Shane,
2010). In turn, numerous recent studies suggest that
engaging in entrepreneurship holds promise in fulfilling
people’s basic psychological needs for autonomy,
competence, meaning, and relatedness, and, in turn,
can lead to higher levels of subjective well-being
(e.g., Andersson, 2008; Benz & Frey, 2004; Binder
& Blankenberg, 2021; Binder & Coad, 2013, 2016;
Blanchflower, 2004; Blanchflower & Oswald, 1998;
Kautonen et  al., 2017; Lindfors et  al., 2007; Ljunggren
& Kolvereid, 1996; Nikolaev et  al., 2020a,
2020b; Nikolova et al., 2023; Przepiorka, 2017; Shir
et al., 2019; Stephan et al., 2020; Taylor, 2004; Wolfe
& Patel, 2018).
Despite that promise, however, there is still lack
of systematic analysis that explores well-being differences
between male and female entrepreneurs in
different institutional and developmental contexts.
Recent meta-analyses (e.g., Lerman et  al., 2021;
Stephan et  al., 2022) and reviews of the literature
(e.g., Stephan, 2018) explore the determinants and
consequences of the health and well-being of entrepreneurs
and identify no papers that focus on gender
differences in well-being. As Stephan et al., (2022, p.
27) conclude: “Unpacking gender effects in entrepreneur
wellbeing is thus another area in need of more
research.”
Yet, understanding gender differences in wellbeing
is important because the rate of entrepreneurship
is markedly lower among women in most developed
and developing countries (Bosma et al., 2018).
In addition, women entrepreneurs are more likely
to be motivated by non-economic outcomes such
as self-empowerment, time flexibility, self-perceptions,
work-life balance, and life satisfaction (Love
et  al., 2023). Thus, if entrepreneurship holds promise
to increase the non-monetary rewards from one’s
work, which women tend to value more, it is critical
to understand what factors drive the well-being of
women entrepreneurs, especially in less developed
economies where gender inequality still persists,
labor market opportunities are scarce, and women
are more likely to face institutional and cultural constraints
(World Economic Forum, 2022).
The present study addresses this gap in the literature
by exploring differences in well-being between
male and female entrepreneurs in a large cross-section
of countries. It further explores several macro
and micro-level mechanisms–economic development,
institutional context, gender roles, and individual
characteristics–that potentially moderate this relationship.
These developments contribute to the entrepreneurship
literature on well-being in several ways.
First, the paper explores gender differences in
the relationship between entrepreneurship and wellbeing,
answering recent calls to examine the heterogeneity
of well-being among different groups of
entrepreneurs (e.g., Nikolaev et  al., 2020a, 2020b;
Stephan, 2018; Wiklund et  al., 2019). It is hypothesized
that while women may derive greater wellbeing
from having a more autonomous working
environment since they value schedule flexibility
and work-life balance more than men (Arai, 2000;
DeMartino et  al., 2006), they are nonetheless more
likely to report lower levels of well-being relative to
men. Compared to men, women are more likely to
enter entrepreneurship out of necessity rather than an
opportunity (GEM, 2019), to have lower endowments
(e.g., assets, education, skills, or networks), and to
face more institutional and cultural constraints (e.g.,
restrictive social norms, unequal legal treatment,
unfair family responsibilities, and financial discrimination),
which in turn can compromise their wellbeing
(Brush et al., 2009; Campos & Gassier, 2017;
Klapper & Parker, 2011; McGowan et al., 2012; Poggesi
et al., 2016).1
Second, the study investigates whether the gap in
well-being between women and men entrepreneurs
1
 It is important to note that a recent study by (Hmieleski &
Sheppard, 2019) explores differences in well-being of men and
women entrepreneurs in a US sample. However, their focus is
primarily on how the masculine characteristics (such as creativity)
and feminine characteristics (such as teamwork) differentially
impact the well-being of men and women. While
broadly related to our study, their focus is narrower both in
scope of their research questions and the sample used.
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is affected by the level of economic development.
Self-employment choices could be driven by different
motives in high- and low-income countries. In highincome
countries, women are more likely to enter
self-employment to realize their creative potential, to
feel more independent, or to have a better work-life
balance. In low-income countries, women often don’t
have any other feasible employment options and thus
are more likely to enter self-employment out of necessity
(Kirkwood, 2009). For example, in Sub-Saharan
Africa, women are 64% more likely to be necessity
entrepreneurs than men (GEM, 2019). Hence, in lowincome
countries, women entrepreneurs are more
likely to experience lower well-being relative to men
(e.g., De Neve et al., 2018).
Third, the paper explores the role of several institutional
factors–regulations that constrain the ease
of doing business, gender inequality, and financial
development–on the well-being gender gap. In many
countries, there is gender discrimination, be it in the
legal sector, financial sector, or in social norms and
traditions. For example, several studies have documented
that women entrepreneurs are more likely
to face financial disadvantages, including high loan
denials, high-interest rates, and additional collateral
requirements (Alesina, 2013; Coleman, 2000; Muravyev
et al., 2009). These constraints can be especially
challenging in countries with burdensome regulations
and low levels of financial development where access
to private capital is scarcer. Similarly, in many societies,
starting and running a business is often viewed
as a male role (Bird & Brush, 2002). Women continue
to face sociocultural biases and gender myths and
are often perceived as less credible than men (Brush
et al., 2009; Minniti & Nardone, 2007). In turn, such
institutional constraints and culturally defined gender
roles can further compromise the well-being of
female entrepreneurs.
Finally, the paper examines the extent to which
three individual-level characteristics–education, the
presence of children, and risk preferences–moderate
the gender gap in well-being. For example, higher
education is considered to be one of the most important
investments in human capital that can provide
many monetary and non-monetary benefits (Card,
1999; B. Nikolaev, 2016; Oreopoulos & Salvanes,
2011). Yet, there are still significant gender gaps in
educational attainment, especially in less developed
countries (UN, 2022). In turn, women with lower
levels of education are likely to experience lower levels
of well-being.
To test these hypotheses, data from the World Values
Survey (WVS), which is a large cross-country
database that includes measures of well-being as well
as other personal characteristics, is used. The WVS
is well-suited for the analysis for several reasons.
First, this dataset has been widely used in the wellbeing
literature (Blanchflower & Oswald, 2004; Hammond
et al., 2011; Peiró, 2006; Salinas-Jiménez et al.,
2013). Second, the WVS includes information on
employment outcomes, including self-employment.
Third, the dataset contains a large set of questions
on people’s values and preferences. Fourth, the WVS
survey has been conducted in a large set of countries
at different income levels, and most countries have
been surveyed more than once. Specifically, the final
dataset contains 80 countries, many of which with
two or more years of data, for a total of about 180,000
individuals.
2 Literature review and hypotheses development
This paper seeks to evaluate differences in well-being
between male and female entrepreneurs and investigate
what factors drive these relative differences. The
following sections provide an overview of the literature
and develop the main hypotheses tested in the
study.
2.1 Defining subjective well‑being
Well-being is a multifaceted concept that encompasses
“optimal experience and functioning” (Ryan
& Deci, 2001, p.141). Research on well-being is
informed by two main theoretical traditions: the
hedonic perspective and the eudaimonic perspective.
The hedonic perspective focuses on subjective wellbeing
(SWB) and views well-being as a combination
of positive emotions, the absence of negative emotions,
and favorable life evaluations (e.g., see Diener
et al., 1985). The eudaimonic perspective focuses on
self-realization and meaning and defines well-being
as the extent to which a person is functioning at their
full potential (e.g., Ryff, 1989).
This study follows prior research in the entrepreneurship
literature, which has primarily focused
on subjective well-being. Specifically, overall life
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satisfaction is used as a proxy for well-being (Binder
& Coad, 2013, 2016; Hamilton, 2000; Hundley,
2001).2
Hence, as it is common in the literature, the
terms well-being, happiness, and life satisfaction are
used interchangeably.
Life satisfaction is one of the most widely used
measures of subjective well-being and refers to a person’s
overall life evaluation (Diener et al., 1985). It
reflects a person’s overall satisfaction with circumstances
in their life that they consider most relevant
(e.g., personal, social, or economic). Importantly, life
satisfaction is a global measure of well-being and as
such it can be applied across different cultures and
countries, making it an ideal indicator for cross-country
research (Diener et al., 2013).
One of the key motivations for using life satisfaction
as a measure of well-being in cross-country
research is its widespread availability of data. In the
context of the current study, the World Values Survey
provides a comprehensive coverage across a large
number of countries with different institutional and
development backgrounds. In addition, life satisfaction
is a robust and reliable measure of well-being,
with a high degree of consistency in responses across
different populations (see Diener et al., 2013).
2.2 Relative well‑being differences between men and
women entrepreneurs
There are several reasons to expect differences in
well-being between female and male entrepreneurs.
First, business outcomes are often weaker in womenowned
businesses. For example, women-run businesses
tend to be smaller in size (Bardasi et al., 2011;
Bruhn, 2009) and operate in more crowded, competitive,
and less profitable service sectors (Hisrich
& Brush, 1984; Singh et al., 2001; Storey & Greene,
2010), have lower productivity and profitability
(Aterido et  al., 2011; Hundley, 2001; Islam et  al.,
2020), grow slower (Singh et  al., 2001), and have
lower survival rates (Boden & Nucci, 2000; McPherson,
1995).
A substantial body of research that shows that
income (and economic performance more generally)
is strongly and positively correlated with subjective
well-being, both within and across countries.
This is one of the most well-established relationships
in the cross-country literature on well-being
that has become a stylized fact (e.g., Kahneman &
Deaton, 2010; Killingsworth, 2021; Stevenson &
Wolfers, 2013). Therefore, because of weaker economic
performance, being an entrepreneur may be
less psychologically rewarding for women than it is
for men.
Second, women entrepreneurs are likely to face
more obstacles and constraints, which could, at least
in part, also explain the weaker economic performance
of their businesses. For example, ample evidence
suggests that endowments such as income,
assets, and skills tend to be skewed toward men, especially
in less developed countries (e.g., for a review,
see Love et  al., 2023). Similarly, various external
factors, such as laws that restrict women’s economic
activities, tend to further disadvantage women entrepreneurs
(World Bank, 2018). These challenges may
further increase the well-being gap between men and
women, especially in less developed countries.
Third, women entrepreneurs are more likely to
enter entrepreneurship out of necessity, such as a lack
of other options for gainful employment (DeMartino
et al., 2006; GEM, 2019; McGowan et al., 2012;
Moore et al., 1999). Thus, they are more likely to be
“pushed” into entrepreneurship by necessity rather
than “pulled” by opportunities such as the pursuit of
a creative business idea or a drive for independence.
Economic necessity, such as a lack of jobs or a need
for extra income, is the most prominent push factor
(Eversole, 2004; Holmen et  al., 2011). Gender inequality
in wage and salary earnings may provide an
additional push for women to leave wage employment
for self-employment (Boden, 1996).
The distinction between necessity and opportunity
entrepreneurship is one of the most comparatively
well-researched areas with regard to well-being.
Previous studies consistently show that necessity
entrepreneurs report lower levels of well-being (for
a review, see Binder & Blankenberg, 2021; Stephan,
2018). These results have been validated in British
and German samples (e.g., Binder & Coad, 2013,
2016) as well as in many other countries (e.g.,Larsson
& Thulin, 2019; Zbierowski, 2014). Therefore, if
2
 Respondents are asked to answer: “All things considered,
how satisfied are you with your life as a whole these days?
Using this card on which 1 means you are “completely dissatisfied”
and 10 means you are “completely satisfied” where
would you put your satisfaction with life as a whole?”.
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women are more likely to be “pushed” into selfemployment,
they will derive less satisfaction from
their entrepreneurial activities, and hence their wellbeing
will be lower.
Finally, women may enter into self-employment
for non-economic reasons, such as self-empowerment,
independence, better work-life balance, and
flexibility of schedule to allow them to better care for
their family (Boden, 1999; Kirkwood, 2009). In most
societies, women are still considered to be the primary
housekeepers and caretakers of children (e.g.,
Rubio-Bañón & Esteban-Lloret, 2016). Their businesses
are more often located in their homes, which
makes it easier to juggle business and home demands.
Thus, self-employment may give women important
non-economic benefits (such as schedule flexibility or
proximity to home), which they may value relatively
more than self-employed men.
However, for many self-employed women, greater
freedom and flexibility of running a business is tempered
by more stress and conflicting commitments:
constant work demands, managing the interests of
children and other dependents, and a sense of guilt
for neglecting children and family (Duberley & Carrigan,
2013; McGowan et al., 2012). Previous studies
show that women still face workplace adversity
(Weyer, 2007) that can even undermine the positive
returns from higher educational attainment, even in
developed countries (Heilman & Chen, 2003; Solomon
et al., 2022; Stevenson & Wolfers, 2009). Thus,
it is expected that:
Hypothesis 1:	 Women entrepreneurs have lower
well-being than men entrepreneurs.
Next, the paper examines four boundary conditions–(1)
economic development, (2) institutional factors,
(3) gender roles, and (4) personal characteristics
and attitudes–that can influence the well-being gender
gap.
2.3 The role of economic development
There are several reasons why a country’s income
level may moderate the gender well-being gap.
First, “women are disproportionately more likely
than men to report a necessity motive in most countries”
(GEM, 2019, p. 22). However, women in lowincome
countries are more likely to be “pushed” into
self-employment than women in high-income countries.
Data from the Global Entrepreneurship Monitor
(GEM), for example, reveals that necessity-driven
entrepreneurship for women is highest among lowincome
countries, while opportunity-driven entrepreneurship
for women is highest in high-income
countries. These differences can be striking – for
example, only 9% of women entrepreneurs in North
America started a new business venture out of necessity,
while in sub-Saharan Africa, close to half of all
women reported being pushed into entrepreneurship
out of necessity (GEM, 2019). Sub-Saharan Africa
also shows the largest gender gap in necessity motivations–women
are 64% more likely to be necessity
entrepreneurs relative to men (GEM, 2019).
Women are “pushed” into self-employment when
they face limited job prospects, more discrimination
on the job market, or simply need to supplement their
family income. These factors are likely to be more
pronounced in low-income countries where women
continue to face higher entry barriers in the formal
labor market and often have to resort to entrepreneurship
as a way out of unemployment and poverty
(GEM, 2019; Minniti & Naudé, 2010). Such outcomes
can be further exacerbated because endowments
such as income, assets, and skills tend to be
skewed toward men in less developed countries (e.g.,
for a review, see Love et al., 2023).
Second, while women, in general, have lower productivity
businesses, this productivity gap is much
larger in low-income countries. For example, in
Africa, as well as in many other developing countries,
women entrepreneurs tend to concentrate on sectors
that are more crowded and hence have lower profitability
and growth prospects (Aterido et  al., 2011;
Bardasi et  al., 2011). Some of these differences are
explained by (1) the adverse business environment
women face, (2) access to digital assets, (3) firm-age
disadvantage and lack of access to foreign investment,
and (4) the size of the sector in which women-owned
businesses operate (e.g., see (Islam et al., 2020).
Overall, as rates of necessity entrepreneurs tend to
be much higher in less developed countries, such push
and pull factors tend to be at the heart of the observed
differences in well-being among entrepreneurs across
countries (e.g., see De Neve et  al., 2018). Because
women tend to be disproportionally more likely to be
necessity entrepreneurs and run less-profitable businesses
in less-developed countries, it is expected that:
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Hypothesis 2:	 Women entrepreneurs who live in
less developed societies will experience relatively
lower well-being than men.
2.4 The impact of institutional factors
Women entrepreneurs are also more likely to face
more severe obstacles to running their businesses in
low-income countries due to a range of institutional
constraints–from access to financial resources to gender
discrimination in the labor market (Love et  al.,
2023; Minniti & Naudé, 2010; Wu et  al., 2019). In
turn, such institutional constraints can limit women’s
opportunities even further, both in the labor market
and self-employment, and lead to lower levels
of well-being. The following section discusses three
institutional factors that can potentially influence
the well-being gender gap: (1) the level of financial
development, (2) the ease of doing business, and (3)
gender discrimination.
First, substantial literature suggests that financial
capital is critical to entrepreneurship (Acs & Szerb,
2007; Fairlie & Krashinsky, 2012). For example, bank
loans are a common source of finance for new ventures
(Eddleston et al., 2016), and micro-loans are a
critical resource for creating economic opportunities
and empowering self-employed women, especially in
developing countries (Samineni & Ramesh, 2020).
However, research has documented several disadvantages
faced by women entrepreneurs, including high
loan denials, high-interest rates, and additional collateral
requirements (Alesina, 2013; Coleman, 2000;
Muravyev et al., 2009).
These constraints may be especially pronounced in
countries with a low level of financial development,
where women are more likely to be excluded from
the formal financial sector (Morsy & Youssef, 2017).
For example, when financial capital is scarce, bankers
may disproportionately lend to male entrepreneurs
(Orser & Riding, 2006). Aidis et  al. (2007) show
that access to funds is a more significant barrier to
the progress of women business owners in Lithuania
and Ukraine than to men. Similarly, Muravyev et al.
(2009) use cross-country data and find that womenmanaged
firms are less likely to obtain a bank loan
and are charged higher interest rates when loan applications
are approved. Women borrowers are also more
likely to pay higher interest rates and have higher collateral
requirements (Coleman, 2000; Riding & Swift,
1990). Finally, women continue to be dramatically
underrepresented in the financial services workforce,
even in developed countries (Ellingrud et al., 2021).
Thus, it is anticipated that women entrepreneurs in
countries with a low level of financial development
will have lower well-being.
Second, business regulations such as licensing
restrictions, administrative requirements, bureaucracy
costs, and tax compliance increase the
cost of doing business (Djankov et al., 2002) and
reduce new venture creation and growth rates (De
Soto & Diaz, 2002; Dean & Meyer, 1996; Djankov
et  al., 2002). Because highly regulated economies
are susceptible to corruption (Holcombe &
Boudreaux, 2015), and women are less likely to
use bribes than men (Swamy et al., 2001), in countries
with more cumbersome regulations, women
may face greater constraints to starting, running,
and growing new ventures. Recent research, for
example, finds that women in countries with more
business regulations have lower early-stage growth
aspirations (Boudreaux & Nikolaev, 2019). Therefore,
when the cost of doing business is high,
women entrepreneurs will experience lower levels
of well-being.
Finally, women entrepreneurs who live in countries
with higher levels of gender inequality may
have have lower well-being. A substantial body of
research documents that a higher level of inequality
at the country level is associated with many
negative outcomes–from lower physical and mental
health to lower levels of trust and cooperation
(e.g., Buttrick & Oishi, 2017). Similarly, numerous
studies suggest that discrimination is strongly associated
with a variety of negative well-being outcomes–from
lack of self-esteem and depression to
anxiety and life dissatisfaction) (e.g., Schmitt et al.,
2014). Therefore:
Hypothesis 3:	 Women entrepreneurs who live in
societies with greater gender discrimination,
higher barriers to starting a business, and less
access to financial resources experience relatively
lower well-being than men.
2.5 The role of cultural gender norms
In addition to formal institutional constraints, many
of the social norms and traditions may affect women
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entrepreneurs differently than men. Social norms
define appropriate behaviors and desirable attributes
for women and men, creating gender roles in realms
outside of the family, such as work (Eagly & Kite,
1987; Williams & Collins, 1995). They include rules
and traditions regarding many relevant aspects of
business, such as property ownership (i.e., whether
or not women are allowed to own assets in their
name), location (i.e., whether or not women have
freedom of movement and location), restrictions on
contact with men who are not their relatives, types
of economic behaviors that are allowed for women,
including their career choices, and social attitudes on
working outside of the home (for a review, see Love
et al, 2023).
In many societies, social norms are more restrictive
toward women, especially when it comes to
gender roles in the labor market and, in particular,
self-employment (e.g., Marques, 2017; Rubio-Bañón
& Esteban-Lloret, 2016). For example, it is by now
well-established that men are more likely to start
businesses (Eagly & Kite, 1987; Langowitz & Minniti,
2007; McKay et al., 2010; Themudo, 2009). One
reason is that social norms and traditions “put women
in the home, doing housework and caring for children
and elderly, while men are responsible for working
and bringing home money to support the family”
(Rubio-Bañón & Esteban-Lloret, 2016, p. 10) Therefore,
starting and running a business is often viewed
as a male role (Bird & Brush, 2002). In this respect,
women continue to face sociocultural biases and are
often perceived as less credible than men (Brush
et al., 2009; Minniti & Nardone, 2007). As a result,
women experience gender discrimination when seeking
start-up capital (Fay & Williams, 1993) and have
a more difficult time exploiting business opportunities
(Carter & Rosa, 1998).
Similarly, women often face more discrimination
in societies where entrepreneurship is viewed as
a male activity (Baughn et al., 2006). In addition, in
developing countries, the views on gender roles may
push women into low-growth sectors (Estrin & Mickiewicz,
2011). Finally, unequal intra-household power
allocation can limit women’s ability to gain the benefits
of their entrepreneurial activities (Kantor, 2002).
In turn, women entrepreneurs who live in societies
that place more importance on traditional values, and
those that subscribe to sexist gender roles, are likely
to find themselves less happy being self-employed.
Hypothesis 4:	 Women entrepreneurs who live in
societies that favor sexist gender roles will experience
relatively lower well-being than men.
2.6 The role of education, children, and risk
preferences
The next hypothesis considers how individual characteristics
and attitudes influence the gender gap in
well-being. Specifically, there are significant gender
differences when it comes to educational attainment,
childcare expectations, and risk preferences
that can influence the well-being of men and women
entrepreneurs.
First, education is widely considered to be one of
the most important investments in human capital that
helps individuals develop a multitude of competencies
that provide many monetary and non-monetary
benefits. Numerous studies show that more educated
people are more likely to have better job opportunities,
greater labor force flexibility, earn higher
incomes and live longer and healthier lives (Card,
1999; B. Nikolaev, 2016; Oreopoulos & Salvanes,
2011). Higher education is also strongly and positively
correlated with subjective well-being–more
educated people view their lives as more meaningful,
experience more positive and less negative emotions,
and are more satisfied with most life domains, including
financial, family, and job satisfaction (Nikolaev,
2016; Nikolaev & Rusakov, 2016).
Education is also an important factor in starting
a business as it expands the owner’s competencies,
cognitive skills, and social networks (Delmar &
Davidsson, 2000; Henley, 2005; Kim & Baylor, 2006;
Parker, 2021; Shane, 2010). For example, higher education
is strongly correlated with high-growth entrepreneurship–the
vast majority of the world’s selfmade
billionaires have professional degrees and are
highly educated (e.g., see Henrekson & Sanandaji,
2014). However, only 12 percent of the world’s billionaires
are women (Frank, 2016; Sandler, 2022).
Overall, women entrepreneurs with higher education
will report higher levels of well-being relative
to their less educated counterparts–they will be
less likely to be pushed into entrepreneurship out of
necessity and more likely to rip the monetary and
non-monetary benefits from their higher education.
However, while transformative gains in women’s
education have unfolded in recent decades, significant
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gender gaps in completion rates still exist, especially
in less developed and rural areas ((UN, 2022).
Women now outnumber men in tertiary education in
some areas of the world (Parker, 2021), but they are a
minority of students in STEM (science, technology,
engineering, and mathematics) and hold only 2 in 10
science, engineering, and communication technology
jobs globally Women are also far less likely to hold
managerial and high executive jobs–only 1 in 3 managers
is a woman (UN, 2022), and women make up
only about 5% of Fortune 500 CEOs (Zarya, 2018).
Thus, higher education may disproportionally benefit
female entrepreneurs.
Second, prior work suggests that women cope with
occupational demands differently than men, especially
as family needs such as childcare emerge (Brett
& Stroh, 2003; Heilman & Chen, 2003). For example,
discontent with corporate life and opportunities
for advancement can push women into entrepreneurship
as an alternative route for professional success
(Heilman & Chen, 2003). In addition, highly educated
women tend to specialize both at home and in
the labor market (Cunningham, 2007), which can create
more stress and lower their job satisfaction even
if they have higher education (Solomon et al., 2022).
In turn, lower job satisfaction may push women into
entrepreneurship (Nikolaev et al., 2020a, 2020b). Previous
studies, for example, document that married
women with young children, especially in less developed
countries, are more likely to enter entrepreneurship
(Minniti & Naudé, 2010). This is likely because
of a lack of suitable wage work options that would
allow them sufficient flexibility in childcare.
In addition, most cross-sectional and longitudinal
studies suggest that having children is associated
with lower levels of subjective well-being (for
a review, see Hansen, 2012). This negative effect is
mostly driven by children living at home, particularly
among women who have low socio-economic status
and live in fewer pronatalist societies (Hansen, 2012).
Thus, women with children may be more likely to be
pushed into entrepreneurship and experience extra
pressure to balance family and business responsibilities,
which will be reflected in lower well-being.
Finally, previous studies suggest that women, on
average, are more risk-averse than men (Croson &
Gneezy, 2009; Eckel & Grossman, 2008; MeyersLevy
& Loken, 2015). However, running a business is
inherently uncertain and risky–most businesses fail,
while the owners of those that do survive are likely
to experience volatile and below-average incomes
(Hamilton, 2000; Parker, 2021; Shane, 2010). In turn,
more risk-averse women entrepreneurs, those who
have a lower propensity for risk and adventure, may
derive less satisfaction from being an entrepreneur.
On the contrary, women who express a preference for
a stimulating and interesting life are more likely to
be “pulled” into entrepreneurship as a form of selfexpression.
In this case, a better person-environment
fit may lead to higher levels of well-being (Markman
& Baron, 2003).
Hypothesis 5:	 Women entrepreneurs with lower
educational attainment, more children, and more
risk-averse preferences will have relatively lower
well-being than men.
3 Data and methods
3.1 World values survey
The data used for the analysis came from the World
Values Survey (WVS), which is the largest crosscountry
dataset that provides individual-level data
on well-being and values across the globe.3
Data is
available for six successive waves starting in 1980.
For this study, data from waves five (2004–2009) and
six (2010–2014) is used. The WVS interviews nationally
representative samples of adult residents with a
targeted minimum sample size of 1,000 respondents
per country. Data were collected using face-to-face
interviews at the respondent’s homes to make sure
that respondents with no internet or phone connection
were represented in the survey. The WVS is ideal for
the current analysis because it includes individuallevel
data on life satisfaction, age, education, gender,
marital status, other personal characteristics, and,
importantly, a large set of value-based questions.
Well‑being  Well-being is proxied with a measure of
overall life satisfaction. Specifically, respondents are
asked to answer: “All things considered, how satisfied
are you with your life as a whole these days? Using
3
 The data are publicly available and can be downloaded at:
www.​world​value​ssurv​ey.​org
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this card on which 1 means you are “completely dissatisfied”
and 10 means you are “completely satisfied”
where would you put your satisfaction with life
as a whole?”.
The economic literature uses the terms “life satisfaction,”
“happiness,” and “well-being” interchangeably.
These alternative measures are highly
correlated and have similar coverage (Stevenson &
Wolfers, 2009). Large literature supports the use of
life satisfaction as a measure of well-being (Bennett
& Nikolaev, 2017; Howell & Howell, 2008; Layard
& Oparina, 2021; Naudé et al., 2013). For example,
according to the World Happiness Report (2021),
overall life satisfaction provides a broader indication
of human welfare than measures of income, poverty,
health, education, and good governance since it captures
the overall quality of life.
Gender roles  Two measures are used to capture
society’s gender roles. First, the proportion of people
in a country that agree or strongly agree with the following
statement: “On the whole, men make better
business executives than women do.” Thus, this variable
captures the extent to which society accepts sexist
gender roles. There is a huge cross-country variation–in
Egypt, 85 percent of the population agrees
that men make better business executives, while in
the Netherlands and Sweden, less than 10 percent
of the population agrees with the statement, reflecting
more equal gender roles. In addition, a variable
“Tradition” was created that captures the proportion
of the population who answers that the following
statement is “like me” or “very much like me”:
“Tradition is important to this person; to follow the
customs handed down by one’s religion or family.”4
Prior studies show that more traditional values tend
to affect how men and women view family and work
expectations, with more traditional societies expecting
women to spend disproportionally more time
on household chores and taking care of children
(e.g., Cerrato & Cifre, 2018). Appendix 7. provides
detailed descriptions of variable construction.
Individual moderators  Three individual levels of
characteristics are explored as potential moderators–
education, number of children, and risk preferences.
Education captures four different levels of education:
no formal education ‘0’, elementary education
‘1’, secondary education ‘2’, and college education
or higher ‘3’. Similarly, the number of children is
measured with a categorical variable that captures: no
children ‘0’, one child ‘1’, two children ‘2’, and more
than three children ‘3’. To measure risk preferences, a
new variable was created that was equal to 1 if a person
responds that the following statement is “like me”
or “very much like me”: “Adventure and taking risks
are important to this person; to have an exciting life.”
Table 1 reports the average level of well-being broken
down by several individual-level variables–e.g.,
self-employed, employed, unemployed, married,
etc. Table 2 provides summary statistics of all individual-level
variables. Table  3 shows pairwise correlations
for all variables used in the analysis. The
average well-being is 6.8, with a standard deviation
of 2.3. Women represented 52% of the sample, and
most respondents were married (63%). Employed
individuals comprised 42% of the sample, while
self-employed 12%. The average well-being of selfemployed
individuals was 6.6, which is higher than
the unemployed (6.1) but lower than the employed
(7.0). Individuals with higher education, which comprised
58% of the sample, reported higher well-being
(7.0) compared to individuals with basic education
(6.5).
The WVS data does not contain the person’s actual
income, only the decile of the income distribution.
However, relative income shows similar influences
on an individual’s life satisfaction as absolute income
(Salinas-Jiménez et  al., 2013). Almost half of the
sample (48%) contains individuals whose household
income falls in the middle-income category, and 22%
of the sample comes from high-income households.
A monotonic relationship between household income
and average well-being is observed. Individuals from
high-income households have the highest well-being
4
 Survey questions used to measure people’s attitudes are
coded on a 5-point Likert scale that ranges from 1 to 5 representing
a range of possibilities from one extreme to another.
In converting Likert-type scale variables to dichotomous variables,
the goal was to make sure that the two categories (i.e.,
0 and 1) are as close to dividing the sample in half as possible,
which corresponds to a common practice of splitting the sample
at the median. The results are similar if original (ie nondichotomized)
Likert-scale variables are used. In addition, the
use of dichotomous categories avoids the problem with interpreting
the original ordinal variables as cardinal.
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Table 1  Life Satisfaction by Group
Note: Each cell reports the mean life satisfaction, standard deviation an percentage of people in each category. N represents the number
of observations in each category. “Other” employment category includes retired, housewife, students and other. “Other” Marital
status category includes separated, widowed, and divorced
Variables Mean SD Percent N Variables Mean Sd Percent N
Well-being 6.8 2.3 100% 168,725 Marital-Status
Gender Other 6.3 2.5 12% 20,207
Female 6.8 2.3 52% 88,744 Married 6.9 2.3 63% 106,702
Male 6.8 2.3 48% 81,319 Single 6.8 2.2 25% 42,842
Employment Status No. of Children
Other 6.8 2.4 37% 61,227 No Child 6.8 2.2 30% 48,435
Employed 7.0 2.1 42% 68,973 One 6.7 2.3 16% 26,597
Self-employed 6.6 2.3 12% 20,040 Two 6.8 2.3 25% 40,689
Unemployed 6.1 2.5 10% 15,899 3 or More 6.7 2.4 29% 48,168
Education Level Child dummy
No-Formal 5.7 2.6 7% 11,600 Yes 6.7 2.3 70% 115,454
Elementary 6.7 2.4 34% 57,854 No 6.8 2.2 30% 48,435
Secondary 6.8 2.2 35% 59,841
University 7.1 2.1 23% 39,662 Income Level
Education dummy Low 6.1 2.7 30% 48,783
Basic 6.5 2.5 41% 69,454 Middle 6.8 2.1 48% 76,032
Higher 7.0 2.2 59% 99,503 High 7.6 1.9 22% 35,167
Gender roles
Yes 6.5 2.4 42% 65,473 Stimulation
No 7.0 2.2 58% 91,531 Yes 6.8 2.3 57% 88,811
Tradition No 6.8 2.3 43% 66,644
Yes 6.8 2.4 58% 91,033
No 6.8 2.1 42% 66,072
Table 2  Descriptive
Statistics
Variable N Mean Std. Dev Min Max
Life Satisfaction 169,000 6.774 2.305 1 10
Female 170,000 .521 .5 0 1
Self-Employed 170,000 .118 .322 0 1
Unemployed 166,000 .096 .294 0 1
Age 170,000 41.782 16.534 15 99
Married 170,000 .629 .483 0 1
Number of Children 164,000 1.541 1.195 0 3
Education 169,000 1.755 .89 0 3
Income Level 160,000 .915 .719 0 2
Tradition 160,000 .579 .178 .103 .957
Men Better CEOs 164,000 .416 .211 .052 .851
Risk Taking 155,000 .429 .495 0 1
Log GDP 169,000 8.986 1.327 5.607 11.425
Gender Inequality 92,897 .298 .157 .047 .83
Financial Development 160,000 3.744 .871 -.203 5.469
Ease of Doing Business 138,000 -.233 .845 -1.996 1.566
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Table 3  PairwiseCorrelations
*
 Showssignificanceatthe.05level
Variables(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)
(1)LifeSatisfaction1.000
(2)Female0.009*1.000
(3)Self-Employed-0.022*-0.120*1.000
(4)Unemployed-0.089*-0.033*-0.120*1.000
(5)Age-0.017*0.009*-0.008*-0.122*1.000
(6)Married0.051*-0.019*0.071*-0.113*0.227*1.000
(7)NumberofChildren-0.010*0.081*0.075*-0.085*0.501*0.508*1.000
(8)Education0.127*-0.051*-0.097*-0.066*-0.161*-0.064*-0.229*1.000
(9)IncomeLevel0.232*-0.035*-0.001-0.091*-0.102*0.037*-0.084*0.267*1.000
(10)Tradition-0.090*-0.0020.042*0.073*-0.161*0.012*0.065*-0.164*-0.025*1.000
(11)MenbetterCEOs-0.207*-0.008*0.057*0.054*-0.183*0.033*0.036*-0.149*0.015*0.635*1.000
(12)Risk&Stimulation0.018*-0.107*0.045*0.057*-0.220*-0.107*-0.146*0.032*0.090*0.102*0.119*1.000
(13)LogGDP0.199*0.012*-0.157*-0.105*0.226*-0.009*-0.034*0.237*0.068*-0.531*-0.602*-0.127*1.000
(14)GenderInequality-0.039*-0.020*0.110*0.101*-0.218*-0.0030.067*-0.211*-0.029*0.598*0.430*0.116*-0.716*1.000
(15)FinancialDev0.138*0.008*-0.108*-0.067*0.186*0.022*-0.031*0.127*0.060*-0.422*-0.326*-0.090*0.616*-0.630*1.000
(16)EaseDoingBus-0.042*-0.019*0.078*0.051*-0.153*-0.011*0.012*-0.180*-0.099*0.231*0.185*0.054*-0.463*0.442*-0.443*1.000
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of 7.6, which decreases to 6.8 for those with middle
household income and to 6.1 for those with low
household income.
Country‑level moderators  WVS data is combined
with country-level data from various sources.
First, data on Gender Inequality Index (GII) was
used, which came from the United Nations Development
program.5
The data are available as average
annual estimates for 2005–2020. GII reflects genderbased
disadvantage in three dimensions—reproductive
health, empowerment, and the labor market. It
shows the loss in potential human development due
to inequality between women and male achievements
in these dimensions. The GII ranges from 0 (perfect
equality) to 1 (perfect inequality).
Second, the Ease of Doing Business index came
from the World Bank (Wrold Bank, 2019). The data
are available for 2003–2019. Since the goal of this
paper is to investigate the relationship between the
business environment and the well-being of entrepreneurs,
the focus was on a subset of indicators relating
to starting a business. Specifically, three indicators
were used: the time, cost, and the number of procedures
required to start a business. These indicators
were used individually and also as a single index constructed
using Principal Component Analysis (PCA).
Third, data on Financial Development (FD) was
available from the World Bank. The data are available
for all the years covered by WVS in the study sample
(2004–2014). Specifically, a measure of private credit by
deposit money bank to GDP (%) was used, which is the
most commonly used proxy for financial development.
Finally, data on Gross Domestic Product (GDP) per
capita (constant 2010 US$) came from the World Bank,
which is also available for all the years in the sample.
3.2 Empirical methodology
The empirical methodology relied on a standard wellbeing
equation where individuals’ reported well-being
score is regressed on various individuals’ characteristics
(DiTella et al., 2003). Precisely, dependent variable is
the self-reported life satisfaction level with values from
1 (dissatisfied) to 10 (satisfied). A set of personal characteristics
commonly included in well-being regression
was used as control variables: education, age, number
of children, income level, and marital status. A detailed
description of the variables used in this study is provided
in Appendix Table 9. The first model is given by:
where i denotes individuals, c denotes countries, t
denotes time, αct are country-year fixed effects, WB
is well-being, F is a dummy variable equal to one for
females, SE is a dummy for self-employed, X is a vector
of control variables, ­eict is an idiosyncratic error.
The country-year fixed effects capture all common
factors that could affect average well-being in a country
in a year of the survey. The error term is also clustered
at the country-year level to allow for unspecified
correlation between individual-level observations in
each country-year combination.
The first hypothesis evaluates whether there is a
“well-being gap,” i.e., it was tested whether the wellbeing
of self-employed women is different from the
well-being of self-employed men. Formally, the test
examined whether β3 = 0, i.e., the focus was on the
interaction of F (female) and SE (self-employed) dummies.
This model was run first on the full sample, and
then on three subsamples based on the country’s level
of development: low, medium, and high level, based
on the World Bank classification. Thus, it was tested
whether the results hold at different levels of economic
development, which is the first test for hypothesis 2.
The second model was used to test the remainder
of the hypotheses–i.e., the relative effect of a country’s
economic, institutional, and cultural environment as
well as individual characteristics on the “well-being
gap” between men and women. This effect was captured
by the triple interaction of F (female dummy), SE
(self-employed dummy), and the moderating factors
(denoted by M), which are either institutional or individual
factors. Thus, the second model is given by:
This model is an extension of model (1), so for
brevity, here only the main differences are discussed.
(1)
WBict = 𝛽1Fict + 𝛽2SEict + 𝛽3Fict ∗ SEict + 𝛽4Xict + αct + eict
(2)
WBict = 𝛽1Fict + 𝛽2SEict + 𝛽3Fict ∗ SEict
+ 𝛽4M ∗ Fict + 𝛽5M ∗ SEict
+ 𝛽6M ∗ Fict ∗ SEict + 𝛽7Xict
+ 𝛼ct + eict
5
 Data are publicly available and can be downloaded at:
https://​hdr.​undp.​org/​en/​conte​nt/​gender-​inequ​ality-​index-​gii
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Specifically, M was added to represent either country-level
or individual-level moderating factors. The
main interest is in the triple interaction coefficient β6.
Because a triple interaction was added, two additional
double interactions had to be also added that were
captured by the coefficients β4 and β5. The double
interactions capture how moderating factors M affect
all women (coefficient β4) and how moderating factors
M affect all self-employed (coefficient β5). The
main focus, however, is on the coefficient β6, which
captures the effect of moderating factor M on the
relative difference between men and women. In other
words, the triple interaction captures how the moderating
factors M affect the well-being gap.
There are two sets of moderating factors. For testing
hypothesis 2, the moderating factors are countryyear
institutional variables (and hence M will have a
subscript of ct). Four country-year measures were
used: economic development (log GDP per capita),
financial development (private credit), gender inequality
index, and business regulation. For testing hypothesis
3, the moderating factors are individual characteristics
(and hence M will have a subscript of cit). Five
individual characteristics were used: the presence of
young children, education, preference for stimulation,
gender roles, and adherence to tradition. These measures
were discussed in the data section and Appendix
Table 9. When country-year moderating factors were
used, the level of factor M is subsumed into the country-year
fixed effects. When individual moderating
factors were used, M was added as a separate variable
among the control variables given by vector X.
4 Results
4.1 Estimating the well‑being gap
Table  4 presents results that test the main hypothesis
(H1). The results on all control variables are consistent
with the prior literature, which offers reassurance in
the proposed empirical methodology. To streamline the
presentation of the main results, the results for control
variables are discussed in Appendix 7.. The main focus
here is on the interaction of female and self-employed
dummies, given in the model by the β3 coefficient. It is
found that the interaction is significantly negative. In
other words, women entrepreneurs are less happy than
men entrepreneurs, even after controlling for a large
Table 4  Baseline results for the full sample
Note: Dependent variable is well-being. See Table 9 for variable
definitions. All regressions include country-year fixed
effects and the error term is clustered at the country-year level.
P-values are in parenthesis
Standard errors clustered at the country-year are reported in
parenthesis. *** p < 0.01, ** p < 0.05, * p < 0.1
(1)
Well-being
(2)
Well-being
(3)
Well-being
Female 0.10*** 0.11*** 0.11***
(0.02) (0.02) (0.02)
Self-employed *
Female
-0.10** -0.10***
(0.04) (0.03)
Age -0.05*** -0.05*** -0.05***
(0.004) (0.004) (0.004)
Age Squared 0.0005*** 0.0005*** 0.0005***
(0.00) (0.00) (0.00)
Employed 0.05* 0.05** -0.01
(0.03) (0.03) (0.02)
Self-employed -0.01 0.02 -0.01
(0.04) (0.04) (0.03)
Unemployed -0.52*** -0.52*** -0.40***
(0.04) (0.04) (0.004)
Married 0.61*** 0.61*** 0.48***
(0.04) (0.04) (0.04)
Single 0.28*** 0.28*** 0.21***
(0.04) (0.04) (0.04)
One Child -0.08*** -0.08*** -0.04
(0.03) (0.03) (0.03)
Two Children -0.03 -0.03 -0.002
(0.03) (0.03) (0.03)
Three or more Children 0.02 0.02 0.07**
(0.03) (0.03) (0.03)
Elementary Education 0.29*** 0.29*** 0.20***
(0.06) (0.06) (0.06)
Secondary Education 0.49*** 0.49*** 0.28***
(0.08) (0.08) (0.07)
University Education 0.73*** 0.73*** 0.36***
(0.07) (0.07) (0.07)
Middle Income 0.77***
(0.05)
High Income 1.39***
(0.07)
Observations 156,873 156,873 148,205
R-squared 0.17 0.17 0.21
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number of demographic characteristics (column 2),
including income (column 3). This result suggests that
there is a negative well-being gap for self-employed
women, which supports H1. The magnitude of this
effect is relatively small – being a self-employed female
is associated with a 5% standard deviation reduction
in well-being.6
Nevertheless, this effect is equal to the
difference in well-being between non-self-employed
women and men (i.e., the coefficient on the Female
dummy without interaction is a positive 0.1). The selfemployed
dummy, which captures the effect on men
from being self-employed, is not significant. Another
way to interpret these results is that women suffer a
loss in well-being from being self-employed, while
men do not. This difference in well-being between selfemployed
men and women is “the well-being gap.”
4.2 The role of economic development
In Table 5, the most complete model from Table 4 (i.e.
column 3) is replicated on four sub-samples of countries
based on their level of economic development:
low, middle, and high-income countries, as well as a
combined low- and middle-income sample. It is found
that the well-being gap is negative in low- and middleincome
countries and positive in high-income countries.
The magnitude of the gap is larger and more significant
in low-income countries than in middle-income
countries (although the difference in magnitudes of
0.03 is not statistically significant). These results provide
support for H2 – women entrepreneurs experience
lower levels of well-being in low- and middle-income
countries but experience higher levels of well-being in
high-income countries. Both of these effects are relatively
small in magnitude (Cohen, 1988).
4.3 The role of the institutional environment
Table 6 examines the role of the institutional environment
on the well-being gap. The main focus here is on
the triple interaction between the institutional characteristic
M (institutional environment) with F (female)
and SE (self-employed), i.e., the coefficient β6. The
same control variables discussed above are included
but not reported. The results in column 1 confirm the
sample splits results seen earlier in Table 5: countries
with a higher level of GDP per capita have a smaller
well-being gap. The interaction with GDPPC is positive,
meaning higher GDP shrinks the well-being gap
(i.e., making it less negative), and it is significant at the
5% level.
Log GDP is used in these regressions, which
ranges in the sample from 5.6 to 11.4 with an average
and a median of around 9. Therefore, in the country
with the lowest level of economic development
(Ethiopia), the well-being gap is equal to -0.23 (i.e.,
-0.51 + 0.05*5.6). In countries with an average level of
economic development (e.g., Lebanon, Romania, and
South Africa), the well-being gap shrinks to -0.06. In
countries with the highest level of economic development,
the well-being gap is positive and equals about
0.05 for Switzerland and 0.06 for Norway. Thus, the
difference in the well-being gap between the lowest
and the highest income country in the sample is
about 0.3 (i.e., 0.23 + 0.06). While this is still considered
a small effect (Cohen, 1988), this effect is comparable
to the effect of higher education in low- and
middle-income countries (column 4 in Table  5) and
corresponds to a 15% change in standard deviation in
well-being.
In column 2, the Gender Inequality Index is used as
a measure of the institutional environment. The triple
interaction is also significant at 5% despite a significant
loss of observations. Specifically, gender inequality has
a significantly negative impact on the well-being gap. It
is important to note that higher values of the GII indicate
worse outcomes – i.e., higher gender inequality.
Thus, countries with higher gender inequality have a
larger (more negative) well-being gap.
In the sample, the countries with the highest GII
(the worst gender discrimination) are Yemen, India,
Iran, Qatar, and Zimbabwe. For these countries, the
well-being gap ranges between -0.27 and -0.5.7
In the
sample, the countries with the lowest GII (the least
gender discrimination) are Sweden, Netherlands,
Germany, Slovenia, and Singapore. For these countries,
the well-being gap is positive and ranges between6
 The discussion of magnitudes of various coefficients relied
on classification of effect sizes in behavioral sciences proposed
by (Cohen) 1988 The Cohen’s d is calculated as difference in
means scaled by the standard deviation. The values below 0.2
are considered small, above 0.8 are considered large and the
values in-between are medium size.
7
  Obtained as 0.22-0.87*0.57 for low end and 0.22-0.87*0.83
for high end.
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0.15 to 0.18. Thus, the range of the well-being gap is
significantly more pronounced when the GII index
is used. Specifically, the well-being gap range from
the lowest GII to the highest GII country is 0.68. This
difference is equivalent to a 30% of standard deviation
increase in well-being from the least to the most genderequal
country. In terms of Cohen’s (1988) size effect
scale, this is considered a medium-size effect.
Table 5  Sample splits for
countries with different
income levels
Note: See notes to Table 4. Each column represents a subgroup of countries as listed in the
heading. Standard errors clustered at the country-year are reported in parenthesis. *** p < 0.01, **
p < 0.05, * p < 0.1
(1) (2) (3) (4)
Low- Income Middle-Income High-Income Low-Middle Income
Female 0.12*** 0.10** 0.13*** 0.11***
(0.04) (0.04) (0.03) (0.03)
Self-employed*Female -0.17*** -0.13** 0.15* -0.16***
(0.05) (0.06) (0.07) (0.04)
Age -0.03*** -0.05*** -0.05*** -0.04***
(0.01) (0.01) (0.01) (0.01)
Age Squared 0.0003*** 0.0004*** 0.0005*** 0.0004***
(0.0001) (0.0001) (0.0001) (0.0001)
Employed -0.01 0.04 -0.04 0.01
(0.05) (0.04) (0.03) (0.03)
Self-employed 0.04 0.05 -0.07 0.02
(0.06) (0.06) (0.06) (0.04)
Unemployed -0.20*** -0.47*** -0.59*** -0.34***
(0.06) (0.05) (0.07) (0.04)
Married 0.39*** 0.46*** 0.57*** 0.43***
(0.09) (0.05) (0.04) (0.05)
Single 0.31** 0.17*** 0.17*** 0.22***
(0.012) (0.05) (0.04) (0.06)
One Child -0.05 -0.03 -0.05 -0.04
(0.06) (0.04) (0.04) (0.03)
Two Children -0.05 -0.03 0.05 -0.04
(0.07) (0.05) (0.03) (0.041)
Three or more Children 0.06 0.07 0.08** 0.06
(0.06) (0.06) (0.04) (0.04)
Elementary Education 0.13* 0.08 0.23 0.16**
(0.07) (0.11) (0.14) (0.06)
Secondary Education 0.21** 0.16 0.31* 0.23***
(0.10) (0.14) (0.14) (0.08)
University Education 0.36*** 0.21 0.42** 0.31***
(0.09) (0.13) (0.15) (0.07)
Middle 1.08*** 0.63*** 0.61*** 0.84***
(0.08) (0.09) (0.05) (0.07)
High 1.96*** 1.30*** 0.98*** 1.60***
(0.11) (0.13) (0.07) (0.09)
Constant 5.42*** 6.77*** 7.04*** 6.11***
(0.20) (0.20) (0.18) (0.13)
Observations 43,504 54,916 49,785 98,420
R-squared 0.20 0.20 0.13 0.21
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In column 3, financial development is used and the
triple interaction is found to be significant at 5%. Here
the coefficient is positive since higher levels indicate
better financial development. Thus, in countries with
higher levels of financial development, there is a
smaller gap in well-being, i.e., women entrepreneurs
are not as disadvantaged.
Finally, in column 4, the regulatory burden of
starting a new business (DB) is used. However, the
results are not significant. The measure of DB captures
how cumbersome it is to start a new formal
business. One possible explanation for this insignificant
finding is that the DB measure does not capture
the level of inequality between men and women
when it comes to regulations that constrain business
activity. Unfortunately, such data is not available.
Thus, while it is possible that women are disproportionately
affected by more cumbersome business
regulations, the DB measure does not capture such
gender differences.
Overall, these results provide support for H3.
Female entrepreneurs who live in countries with
more discriminatory institutions toward women
and lower levels of financial development experience
lower levels of well-being than men compared
to female entrepreneurs who live in countries
with greater gender equality and better financial
development.
4.4 The role of gender roles
Next, the analysis focuses on the impact of cultural
values and gender roles on the well-being gap. To
do so, an aggregated country-level measures of the
prevalence of gender stereotypes based on individual
responses was created. These results are presented in
Table 7 and suggest that societies in which a greater
proportion of people subscribe to sexist gender values
(column 1) and more traditional values (column
2), the well-being gap is bigger. However, only the
interaction effect with traditional values is significant
(column 2). The results imply, for example, that the
difference in well-being between the least traditional
(Japan) and most traditional society (Qatar) is close to
30% of a standard deviation in well-being, which is a
moderately strong effect.
Table 6  Interactions with institutional characteristics
Note: All regressions include the same control variables used in Table 5. Only the interaction terms and related controls are included.
See notes in Table 4. Each column reports interactions with a different institutional characteristic, which is given in the column heading.
Dependent variable is well-being. GII = Gender Inequality Index, FD = Financial Development, DB = Ease of Doing Business.
Standard errors clustered at the country-year are reported in parenthesis. *** p < 0.01, ** p < 0.05, * p < 0.1
(1) (2) (3) (4)
Institution Variable GDP GII FD DB
Female -0.08 0.14 0.02 0.11***
(0.15) (0.09) (0.12) (0.001)
Self-employed*Female -0.51** 0.22 -0.35*** -0.11***
(0.023) (0.13) (0.13) (0.04)
Self-employed -0.17 -0.11 -0.34*** 0.01
(0.21) (0.10) (0.10) (0.04)
Female *Institution 0.02 -0.06 0.02 -0.05**
(0.02) (0.34) (0.03) (0.02)
Self-employed *Institution 0.02 0.37 0.09*** 0.01
(0.02) (0.29) (0.03) (0.04)
Female*Self-Employed*Institution 0.05* -0.87** 0.07** 0.001
(0.03) (0.35) (0.04) (0.04)
Constant 6.46*** 6.69*** 6.46*** 6.40***
(0.011) (0.016) (0.011) (0.12)
Observations 147,147 82,311 138,741 120,867
R-squared 0.21 0.19 0.21 0.21
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4.5 The role of education, children, and risk
preferences
Finally, the role of education, children, and risk preferences
on the well-being gap is examined in Table  8.
Again, the focus here is on the triple interaction coefficient,
in this case, of an individual characteristic with F
(Female) and SE (Self-employed) dummies. The findings
suggest that women entrepreneurs with higher education
have higher well-being (i.e., lower well-being gap)
(column 1). These results imply that it is mostly uneducated
women entrepreneurs who suffer a loss in wellbeing.
Second, women with children suffer a greater loss
in well-being (column 2). Finally, women entrepreneurs
with a higher preference for risk and stimulation experience
a smaller loss in well-being (column 3). These
effects are moderate in size. For example, compared to
women who had no formal education, female entrepreneurs
with a college education experience a well-being
boost close to 25% of a standard deviation in well-being.
Similarly, compared to women with no kids, women who
have three or more kids experience a well-being penalty
that is close to 22% of a standard deviation in well-being.
5 Discussion
The current study presents new evidence on the
relative well-being of men and women entrepreneurs
and evaluate how gender differences in well-being
are affected by economic, institutional, cultural, and
individual factors. The results suggest that women
entrepreneurs have lower well-being in low- and
middle-income countries but a higher level of wellbeing
in high-income countries. In other words, a
negative “well-being gap” in low- and middleincome
countries and a small but positive gap in
high-income countries is documented. The study
provides a further exploration of how institutional,
cultural, and individual factors moderate the wellbeing
gap. The findings suggest that greater gender
inequality, lower levels of financial development,
and more traditional cultural values increase the
well-being gap, with gender inequality having the
largest negative effect. At the same time, higher
levels of education, fewer children, and greater
preferences for risk and stimulation reduce the
gender gap in well-being.
Table 7  Interactions with cultural values
Note: All regressions include the same control variables used in Table 5. Only the interaction terms and related controls are included.
See notes in Table 4. Each column reports interactions with a different individual characteristic, which is given in the column heading.
Dependent variable is well-being. Standard errors clustered at the country-year are reported in parenthesis
***
 p < 0.01, ** p < 0.05, * p < 0.1
(1) (2)
Cultural Value Men Better CEOs Traditional Values
Female 0.080* 0.132*
(0.044) (0.070)
Female self-employed -0.037 0.324**
(0.094) (0.141)
Self-employed 0.058 -0.058
(0.063) (0.119)
Female * Cultural Value 0.074 -0.035
(0.120) (0.133)
Self-employed * Cultural Value -0.102 0.108
(0.145) (0.182)
Female * Self-Employed * Cultural Value -0.180 -0.741***
(0.203) (0.237)
Constant 6.470*** 6.465***
(0.111) (0.111)
Observations 142,971 142,321
R-squared 0.198 0.198
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5.1 Theoretical implications
An increasing number of studies have documented
that engaging in entrepreneurship can lead to higher
levels of subjective well-being by fulfilling people’s
basic psychological needs for autonomy, competence,
and relatedness (e.g., Benz & Frey, 2004;
Nikolaev et al., 2020a, 2020b; Stephan et al., 2020).
Despite this growing literature, however, there is still
lack of systematic analysis that explores well-being
differences between male and female entrepreneurs
(Stephan, 2018). This study advances this growing
literature by answering recent calls to examine
the heterogeneity of well-being of male and female
entrepreneurs in different institutional, cultural, and
individual contexts (Stephan, 2018; Wiklund et  al.,
2019).
Consistent with previous studies, the results suggest
that entrepreneurship can lead to higher levels
of well-being, but this is highly dependent on the
developmental, institutional, and cultural context
within which entrepreneurs operate. However, ours is
a first study to show that female entrepreneurs tend
to experience significant well-being disadvantages,
especially in countries with lower levels of economic
development, high gender inequality, and more traditional
cultural values. The well-being gap is also
larger for less educated female entrepreneurs who
also have more kids.
These results are consistent with the idea that
in low- and middle-income countries, women are
more likely to be “pushed” into entrepreneurship
by necessity, while in high-income countries,
where they have more opportunities in the
labor market, they are more likely to be “pulled”
by opportunity. In addition, in low- and middleincome
countries, women entrepreneurs are likely
to face more severe obstacles and constraints than
their male counterparts. These constraints could
be in the form of restrictive social norms and
traditions and legal, financial, and labor market
discrimination.
Table 8  Interactions with individual characteristics and attitudes
Note: All regressions include the same control variables used in Table 5. Only the interaction terms and related controls are included.
See notes in Table 4. Each column reports interactions with a different individual characteristic, which is given in the column heading.
Dependent variable is well-being. Standard errors clustered around country-year are reported in parenthesis
***
 p < 0.01, ** p < 0.05, * p < 0.1
Individual Characteristic: (1)
Education
(2)
N Children
(3)
Risk & Stimulation
Female 0.13*** 0.11*** 0.15***
(0.03) (0.03) (0.02)
Self-employed * Female -0.20*** 0.03 -0.19***
(0.06) (0.07) (0.05)
Self-employed 0.07 -0.06 0.07*
(0.05) (0.05) (0.04)
Individual Characteristic 0.14*** -0.005 0.13***
(0.001) (0.02) (0.03)
Female*Individual -0.02 0.005 -0.06**
(0.03) (0.03) (0.03)
Self-employed*Individual -0.15*** 0.08 -0.12**
(0.05) (0.06) (0.05)
Female* Self-employed* Individual 0.19*** -0.17** 0.15**
(0.07) (0.08) (0.07)
Constant 6.44*** 6.46*** 6.44***
(0.11) (0.11) (0.11)
Observations 148,205 148,205 138,844
R-squared 0.21 0.21 0.20
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Finally, the well-being gap is highly dependent on
several individual characteristics. Specifically, women
entrepreneurs have higher well-being if they are more
educated, have no children, and have a stronger preference
for risk and stimulation. In this respect, higher
education holds significant promise in reducing the
negative well-being gap.
Overall, these results suggest that the well-documented
well-being premium from entrepreneurship
is highly contingent on the institutional and cultural
environment as well as the individual characteristics
of entrepreneurs. For example, the negative wellbeing
gap is significantly higher (close to 30% standard
deviation in well-being) between the least and
most gender-unequal countries. This effect is larger
than the negative well-being effect documented from
unemployment, which has been consistently found
to “depress mental well-being and lower life satisfaction
… more than any other single characteristic”
(Powdthavee & Vernoit, 2013).
5.2 Policy implications
The results have several policy implications. First, economic
development plays a critical role in promoting
well-being equality, especially when it comes to the wellbeing
of male and female entrepreneurs. While in the
least developed countries, women entrepreneurs experience
significantly lower well-being than their male counterparts,
in the most developed countries, the gender gap
is non-existent or even reversed.
However, achieving equality requires more than
economic development and is also contingent on the
cultural and institutional environment. Even in the
most developed countries, women who face more sexist
and traditional gender roles and greater discrimination
are more likely to experience significant gaps
in well-being. Therefore, policies that aim to equalize
the playing field for men and women by reducing
gender inequality also hold significant promise in
reducing gender inequalities in well-being.
Similarly, policies that promote equality in educational
outcomes between men and women are also
likely to reduce the gender gap in well-being. In this
respect, while transformative gains in women’s education
have unfolded in recent decades, significant
gender gaps still remain (UN, 2022). For example,
women are still a minority of students in STEM (science,
technology, engineering, and mathematics) and
hold only 2 in 10 science, engineering, and communication
technology jobs globally (UN, 2022). The
results suggest that education is a powerful tool for
empowering women entrepreneurs across the world.
Thus, promoting equal access to education and
increasing the number of women in STEM fields may
significantly reduce the well-being gender gap.
The results also suggest that women with children
are more likely to be pushed into self-employment
than those without children (and hence experience
lower well-being from their activities). Or, even if
they engage in entrepreneurship to pursue opportunities,
women may experience more stress due to juggling
both work and family responsibilities. In this
respect, policies should make it easier for women
with children to work outside of the home. For example,
better options for childcare and more flexible
hours might support not only women entrepreneurs
but wage earners as well, who will be less likely to be
pushed into entrepreneurship.
More generally, many traditional values continue
to be unfair and discriminating toward women (UN,
2022), so naturally, those who feel more bound by
these traditions will experience more challenges in
their business endeavors. For example, given the
endurance of distinct gender roles even in developed
countries (e.g., Bianchi et  al., 2012; Grunow et  al.,
2012), women are more likely to aspire to excel
both at home and in the labor market (Cunningham,
2007; Yavorsky et al., 2015). However, family duties
can often lead to conflict with career development
(Phillips & Imhoff, 1997; Stroh & Reilly, 1999). In
this respect, the division of household labor typically
disadvantages women even in developed societies
(Bianchi et al., 2012; Kamp Dush et al., 2018),
regardless of whether women earn more or less than
their male partners (Bittman et  al., 2003; Solomon
et al., 2022). In this respect, policies associated with
eliminating forced marriages, working to end the
exploitation of women, valuing unpaid childcare, promoting
shared domestic responsibilities, having universal
access to reproductive rights and health, and,
more generally, strengthening policies that promote
gender equality through legislation will likely to continue
to reduce the gender gap in well-being.
Finally, it is important to emphasize that the results
are consistent with the notion that women prefer
wage employment and only when it is not available
they are likely to be pushed into entrepreneurship.
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While no direct test for this effect is provided, it is
shown that more educated women, who are likely to
have more opportunities for wage employment, have
higher well-being from being an entrepreneur likely
because they are pulled into entrepreneurship rather
then pushed. The results on women with children are
consistent with this proposition as well: since women
with children are often discriminated in the labor
market, especially in low-income countries, they are
more likely to be pushed into entrepreneurship.
In conclusion, the results are consistent with the
UN’s Developmental Goals–reducing poverty (goal
1), quality education (goal 3), and gender equality
(goal 5) can significantly enhance the relative
well-being of women entrepreneurs (UN, 2022). As
societies make progress with these goals, this may
encourage more women to enter entrepreneurship by
choice rather than by necessity, which could not only
enhance their personal well-being but result in positive
societal gains. Finally, the results call attention to
focusing on non-economic outcomes of entrepreneurship,
such as well-being, and reducing the emphasis
on profits and growth as the main metrics of success.
5.3 Limitations
Although the WVS provides a large sample size and
inclusion of countries and individuals with different levels
of income and values, there are several limitations
to the study. First, the time gap between different waves
is, on average, five years, and each wave covers a different
set of countries surveyed in different years. While
some countries appear in both waves, others appear
only in wave five, and some only in wave 6. See Appendix
Table 10 for a detailed list of countries and years
included in each wave.
Second, since the data set is not a panel, there is
no possibility for a longitudinal analysis or including
individual fixed effects (DiTella et  al., 2003; Kruse
et al., 2017). Thus, it is difficult to identify the direction
of causality. Without such data, the results should
be interpreted as correlational and not causal. Nevertheless,
the focus of the paper is on the well-being gap
between men and women and the interaction effects,
which should not suffer from serious endogeneity.
Finally, several of the variables used are single-item
measures. For example, well-being is proxied with a
single-item life satisfaction measure. Previous studies
suggest that single-item life satisfaction measures
perform very similarly compared to multi-item measures,
providing virtually identical answers to substantive
questions (Cheung & Lucas, 2014). At the same
time, other variables used in the study, such as risk
preferences or gender roles and traditional values,
have not been validated. Therefore, readers should use
caution when interpreting these results.
Data Availability  WVS data are publicly
available on https://​www.​world​value​ssurv​ey.​org/.
Appendix A 1. Discussion of results for control
variables
Results on control variables for the whole sample
Here we briefly describe the results for our control
variables based on Table 4. We find predictable patterns
for all our control variables. Age has a non-linear
U-shaped relationship with well-being. women have
slightly higher well-being then men, but the magnitude
of the difference is small: the coefficient is equal to 0.1,
which is small according to Cohen’s metric. Employed
have slightly higher well-being than those out of the
labor force (the omitted category), while unemployed
have a significantly lower well-being. The magnitude is
equal to 0.5, i.e. almost half a point difference in wellbeing,
however, according to Cohen’s metric it is still
considered small.
When we add income level dummies in model 3 the
employed dummy is no longer significant, while the
magnitude of unemployed dummy drops to 0.4. This
means that only some of the negative impact of unemployment
is due to the pure income effect. Interestingly,
the self-employed dummy is not significant. This might
be because the positive and negative influences of selfemployed
on well-being discussed earlier cancel out.
Among different relationship status categories, married
people are the happiest, followed by single, and the
least happy are divorced or separated (the omitted category).
People with one child are slightly less happy,
while those with two, three or more children are not
significantly different in well-being from those with no
children (the omitted category). Education has a monotonically
positive relationship with well-being (the omitted
category is “no formal education”).
In model 3 we add two income level dummies for
middle and high income (the omitted category is low
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Table 9  Variabledefinitions
VariableDefinitionDataSource/SurveyQuestions
SectionA:IndividualCharacteristics
WBSelf-declaredlife-satisfactionlevelfrom1(notatallsatisfied)to10
(verysatisfied)
(WVS):Allthingsconsidered,howsatisfiedareyouwithyourlife
asawholethesedays?Usingthiscardonwhich1meansyou
are“completelydissatisfied”and10meansyouare“completely
satisfied”wherewouldyouputyoursatisfactionwithyourlifeasa
whole?(Codeonenumber):
AgeAgeoftherespondent(WVS):cantellmeyouryearofbirth,please?
FemaleDVwhichtakesthevalue1iftherespondentisfemale,0otherwise(WVS):Coderespondent’ssexbyobservation
EducationlevelCategoricalvariablewhichtakesvalueof0,1,2,3forno-formal
education,elementary,secondaryanduniversityeducationlevel
respectively.
(WVS):Whatisthehighesteducationallevelthatyouhaveattained?
EducationdummyDVwhichtakesthevalueof1(highereducation)iftherespond-
enthassecondaryoruniversitylevelofeducation,and0(basic
education)otherwise.
(WVS):Whatisthehighesteducationallevelthatyouhaveattained?
Marital-statusCategoricalvariablewhichtakesvalueof0,1and2forother,mar-
riedandsinglerespectively.Othercategoryincludesdivorced,
separatedandwidowed.
(WVS):Areyoucurrently(readoutandcodeoneansweronly).
EmploymentStatusCategoricalvariablewhichtakesvalueof0,1,2and3forother,
employed,self-employedandunemployedrespectively.Other
categoryincludesretired,housewife,students,andothers.
(WVS):Areyouemployednowornot?(codeponeanswer)
No.ofChildrenCategoricalvariablewhichtakesvalueof0,1,2and3forno-child,
one-child,two-child,andthreeormorechildrespectively.
(WVS):Haveyouhadanychildren?(code0ifno,andrespective
numberifyes).
ChilddummyDVwhichtakesthevalue1(yes)ifrespondenthasanychildrenand
0(no)otherwise.
(WVS):Haveyouhadanychildren?(code0ifno,andrespective
numberifyes).
IncomelevelCategoricalvariablewhichtakesvalueof0,1and2forlow,middle-
andhigh-incomelevelrespectively.Lowincludesresponses(1-3),
middleincludesresponses(4-6)andhighincludesresponses
(7-10).
(WVS):Wewouldliketoknowinwhatgroupyourhouseholdis(1
indicatesthelowestincomegroupand10thehighestincomegroup
inyourcountry).Please,specifytheappropriatenumber,counting
allwages,salaries,pensionsandotherincomesthatcomein.(Code
onenumber)
GenderRolesDVwhichtakesthevalue1(yes)iftherespondentanswersagreeor
stronglyagreewiththestatement(ontheright),0(no)otherwise.
(WVS):Foreachofthefollowingstatements,canyoutellmehow
stronglyyouagreeordisagreewitheach.Doyoustronglyagree,
agree,disagree,orstronglydisagree?“Onthewhole,menmake
betterbusinessexecutivesthanwomendo.”
TraditionDVwhichtakesthatvalue1(yes)iftherespondentanswersvery
muchlikemeorlikemetothestatement(ontheright),0(no)
otherwise
(WVS):Foreachdescription,pleaseindicatewhetherthatperson
(brieflydescribesomepeople)isverymuchlikeyou,likeyou,
somewhatlikeyou,notlikeyou,ornotatalllikeyou?“Tradition
isimportanttothisperson;tofollowthecustomshandeddownby
one’sreligionorfamily”.
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income). The income dummies are highly significant
and have meaningful magnitudes. Having a self-declared
high income (note that the income responses are in the
form of the decile relative to the rest of the population), is
associated with 1.4 higher well-being than declaring low
income. According to Cohen’s metric this is a medium
size effect. People declaring that they have a middleincome
level have on average about 0.8 higher well-being
than those with low income.
Results on control variables in samples splits
These results are based on Table 5. The income effect
is much stronger in low- and middle-income countries.
The coefficient on high income dummy is almost twice
as large in low-income countries as it is in high income
countries: it equals to 1.96 in low income countries and
0.97 in high income countries (note that low income
is the omitted category). According to Cohen’s metric,
the impact in low-income countries can be considered
large. The coefficient on middle income dummy is
about half of the high-income dummy. Our results show
that poor are more miserable in low-income countries.
This could be due to lack of social safety net which is
present in high income countries.
On the flip side, the loss of well-being due to unemployment
is larger in a high-income country. Note that
since we control for income, this coefficient captures
non-pecuniary effects of unemployment such as loss of
meaning, connections, self-esteem and other psychological
effects. This larger well-being loss could be due to
less prevalent unemployment in high income countries:
in our sample the unemployment rate is about 6% in high
income countries, while it is 12.6% in low-income countries.
When unemployment is a common occurrence,
i.e. in low income countries, people are more likely to
adapt to being unemployed, which will result in smaller
loss of well-being. In addition, if more people around are
unemployed, the unemployment is less psychologically
stinging.
The well-being relationship with education has about
the same magnitude in all three sets of countries, although
results are more statistically significant in low-income
countries. This is likely because there are very few people
with “no formal education,” which is our omitted
category, in high income courtiers. There is a higher
well-being advantage to being married in a middle- and
high-income countries, which there is almost no difference
to being single or married in low income countries.
Table 9  (continued)
VariableDefinitionDataSource/SurveyQuestions
StimulationDVwhichtakesthevalue1(yes)iftherespondentanswersvery
muchlikeme,orlikeme,orsomewhatlikemetothestatement
(ontheright),0(no)otherwise.
(WVS):Foreachdescription,pleaseindicatewhetherthatperson
(brieflydescribesomepeople)isverymuchlikeyou,likeyou,
somewhatlikeyou,notlikeyou,ornotalllikeyou?“Adventure
andtakingrisksareimportanttothisperson;tohaveanexciting
life.”
SectionB:countrylevelvariables
GDPGrossDomesticProduct,GDPpercapita(constant2010US$)WorldBank
FDFinancialDevelopment,measuredbyprivatecreditbydeposit
moneybanktoGDP(%)
WorldBank,InternationalMonetaryfund,
DBIndexofstartingabusiness,constructedusingweightsgenerated
bythePrincipalComponentAnalysis(PCA)ofthreevariables:
numberofprocedures,costandtimerequiredtostartabusiness.
WorldBankandauthor’scalculations
GIIGenderInequalityIndexshowsthegender-baseddisadvantagein
threedimensions:reproductivehealth,empowermentandlabor
market.
UnitedNationDevelopmentProgramme(UNDP)
DVstandsfor“dummyvariable”
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Table 10  List of countries
and years in WVS data
Country Wave 5 Wave 6 Country Wave 5 Wave 6
Algeria 2013 Libya 2014
Andorra 2005 Malaysia 2006 2012
Argentina 2006 Mali 2007
Armenia 2011 Mexico 2005 2011
Australia 2005 2012 Moldova 2006
Azerbaijan 2011 Morocco 2007 2011
Bahrain 2014 Netherlands 2007 2012
Belarus 2011 New Zealand 2004 2011
Brazil 2006 2014 Nigeria 2011
Bulgaria 2005 Norway 2007
Burkina Faso 2007 Pakistan 2012
Canada 2006 Palestine 2013
Chile 2006 2011 Peru 2007 2012
China 2007 2012 Philippines 2012
Colombia 2005 2012 Poland 2005 2012
Cyprus 2006 2011 Qatar 2010
Ecuador 2013 Romania 2005 2012
Egypt 2013 Russia 2006 2011
Estonia 2011 Rwanda 2007 2012
Ethiopia 2007 Serbia and Montenegro 2005
Finland 2005 Singapore 2012
France 2006 Slovenia 2005 2011
Georgia 2009 2014 South Africa 2006 2013
Germany 2006 2013 South Korea 2005 2010
Ghana 2007 2012 Spain 2007 2011
Great Britain 2005 Sweden 2006 2011
Guatemala 2004 Switzerland 2007
Hong Kong 2005 2013 Taiwan 2006 2012
Hungary 2009 Thailand 2007 2013
India 2006 2014 Trinidad and Tobago 2006 2011
Indonesia 2006 Tunisia 2013
Iran 2007 Turkey 2007 2011
Iraq 2006 2012 Ukraine 2006 2011
Italy 2005 United States 2006 2011
Japan 2005 2010 Uruguay 2006 2011
Jordan 2007 2014 Uzbekistan 2011
Kazakhstan 2011 Viet Nam 2006
Kuwait 2014 Yemen 2014
Kyrgyzstan 2011 Zambia 2007
Lebanon 2013 Zimbabwe 2012
348	 I. Love et al.
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