Three Pillars and Future Impact
Gender differences in entrepreneurship and intrapreneurship: an empirical analysis
Takanori Adachi . Takanori Hisada
Accepted: 26 July 2016 / Published online: 2 September 2016
� Springer Science+Business Media New York 2016
Abstract This study examines the gender gap in
start-up activities to determine whether it is family
status or employment status that is responsible for the
observed gender gap. We consider independent
entrepreneurship and intrapreneurship as two differ-
ent start-up modes: While intrapreneurship is con-
ducted within an established organization,
independent entrepreneurship is solely an independent
activity. This study focuses on this fundamental
distinction to identify the parameters of our empirical
model. Using nationally representative US data, we
find that the effects of being a part-time worker on the
likelihood of becoming an independent entrepreneur
differ across genders. The obtained results suggest
similar findings for intrapreneurship, but in opposite
directions. Furthermore, our decomposition results
suggest that for both entrepreneurship and
intrapreneurship, the gender differences in the
employment-related variables are more significant
than those in the family-related variables in affecting
the observed gender gap negatively (for entrepreneur-
ship) or positively (for intrapreneurship).
Keywords Gender gap � Entrepreneurship � Intrapreneurship
JEL Classifications J15 � J16 � L26 � M13
1 Introduction
Entrepreneurship is often promoted as an opportunity
for women to improve their working lives, which might
not be easily achieved in the labor market. For instance,
the Small Business Administration (SBA) in the USA
has an Office of Women’s Business Ownership to
promote women entrepreneurs. 1 There are several
reasons for this. First, women may encounter the
proverbial glass ceiling in the workplace (e.g., Cotter
et al. 2001; Elliott and Smith 2004). In addition, it is
also known that women experience wage gaps relative
to men (e.g., Blau and Kahn 2006; Fortin 2008). At the
same time, intrapreneurship, which is essentially
‘‘entrepreneurship within an existing organization’’
(Antoncic 2007, p. 310), also provides women with
opportunities to engage in a start-up activity. 2 It is
T. Adachi (&) School of Economics, Nagoya University, 1 Furo-cho,
Chikusa, Nagoya 464-8601, Japan
e-mail: adachi.t@soec.nagoya-u.ac.jp
T. Hisada
Graduate School of Economics, Osaka University, 1-7
Machikaneyama, Toyonaka, Osaka 560-0043, Japan
e-mail: pge023ht@student.econ.osaka-u.ac.jp
1 See https://www.sba.gov/offices/headquarters/wbo (accessed
July 2016). 2 Parker (2009, p. 31) also states that ‘‘[d]ependent spin-offs are
ventures formed in collaboration with an incumbent firm
(sometimes termed ‘intrapreneurship’), whereas independent
spin-offs are pursued entirely separately from an incumbent
(‘entrepreneurship’).’’ Intrapreneurship is sometimes called
123
Small Bus Econ (2017) 48:447–486
DOI 10.1007/s11187-016-9793-y
increasingly recognized as being equally important as
traditional entrepreneurship since it is crucial to the
established firm’s growth and profitability. 3
However, in the data we use for this study (see
Sect. 3 below for details), women are underrepre-
sented in both modes of start-up activity. They
represent 36 % of nascent entrepreneurs and 30 % of
nascent intrapreneurs—far less than 50 %—and these
gender differences are statistically significant when
compared with the group of uninvolved employees
(see Table 2 below). What are the factors responsible
for this gender gap in start-up activities? While gender
differences in independent entrepreneurship have been
studied extensively (see the next section), there are far
fewer insights when the concept of entrepreneurship is
broadened to include intrapreneurship as well. In this
paper, we examine how gender leads to differences in
the determinants of intrapreneurship as well as those
of independent entrepreneurship. Throughout this
paper, ‘‘entrepreneurship’’ and ‘‘intrapreneurship’’
are considered as two mutually exclusive alternatives,
and we use ‘‘entrepreneurship’’ and ‘‘independent
entrepreneurship’’ interchangeably.
Our main findings are summarized as follows. First,
we find that women are less likely to choose
entrepreneurship presumably because of their aversion
to risk, the existence of credit constraints or discrimi-
nation. Furthermore, marriage, children, and family size
have additional positive effects on women’s
entrepreneurship. However, part-time work has addi-
tional negative effects. We also find that women are less
likelytobecomeintrapreneurs. Thepresenceofchildren
has additional negative effects on intrapreneurship for
women, suggesting that intrapreneurship may deprive
women of time flexibility. We also find that part-time
work is not so disadvantageous for women to become an
intrapreneur. Next, we find that the counterfactual rate
of independent entrepreneurship by women, who
acquire the same (in the distributional sense) observed
characteristics as men, is lower than the actual rate of
men’s independent entrepreneurship. Similarly, the
counterfactual rate of intrapreneurship by women,
who have the same characteristics as men, is also lower
than the actual rate of men’s intrapreneurship. These
two results suggest that women may be in a disadvan-
tageous position when becoming an independent
entrepreneur or an intrapreneur. Lastly, our decompo-
sition results suggest that for both entrepreneurship and
intrapreneurship, the gender differences in the employ-
ment-related variables are more significant than those in
the family-related variables in affecting the observed
gender gap negatively (for entrepreneurship) or posi-
tively (for intrapreneurship).
This paper uses Parker’s (2011) definition of
(nascent) intrapreneurs: Intrapreneurs are those con-
sidering starting a business for their employer. In our
conceptual framework presented below, an individual
first chooses whether to work independently. If he or
she does, he or she is called an independent
entrepreneur. Independent entrepreneurship here is a
broad concept: It includes both self-employment and
business ownership. If the individual does not choose
to be an independent entrepreneur, he or she may
become an intrapreneur. 4 To formalize this conceptual
framework, we employ an empirical model, in which
the structure of these two selections is considered, and
estimate it by using an individual-level survey that is
nationally representative of the USA (Panel Study of
Entrepreneurial Dynamics, II, or PSED II). Our
empirical model of ‘‘double selection’’ is essentially
a bivariate probit with the structure of sample selection
as explained above. It is superior to a nested or
multinomial logit model because the unobserved
variable in the selection of entrepreneurship and
intrapreneurship is found to be negatively correlated
Footnote 2 continued
‘‘corporate entrepreneurship.’’ In this study, we use ‘‘in-
trapreneurship’’ and ‘‘intrapreneurs’’ throughout because we do
not view intrapreneurship as specific to corporations. 3 See, e.g., Miller (1983), Pinchot (1985), Rule and Irwin
(1988), Hisrich (1990), Covin and Slevin (1991), Lumpkin and
Dess (1996), Morris and Sexton (1996), Antoncic and Hisrich
(2001, 2003), Antoncic (2007), Hellmann (2007), and Baruah
and Ward (2015).
4 In this study, we do not describe the details of this
organizational decision process. In the conceptual framework
proposed in Sect. 4.1 below, we assume that an individual
chooses one of the three alternatives that give him/her the best
utility. If an individual who wants to be an intrapreneur cannot
become one because of limited capacities, he/she does not
always choose the best alternative. We do not model such
frictions mainly because of data limitations. In some cases, an
employee may be ‘‘ordered’’ to be an intrapreneur within a
company against his/her will. However, De Clercq et al. (2011)
argue that being selected as an intrapreneur is usually financially
rewarding. Thus, we would not lose much validity even if we
assume that an individual chooses the alternative that gives him/
her the highest level of utility.
448 T. Adachi, T. Hisada
123
and statistically significant. 5 We can deal with such an
asymmetrical relationship in the triangularity of
entrepreneurship, intrapreneurship, and other (i.e.,
not being involved in a start-up); this cannot be
accommodated in a nested or multinomial logit model.
In this study, we stress the fundamental difference
between entrepreneurship as an outside-organization
activity and intrapreneurship as a within-organization
activity. In conceptual frameworks of existing studies,
such as those by Parker (2011), Tietz and Parker
(2012), and Martiarena (2013), an individual first
chooses whether to engage in a start-up activity and
then conditional on the choice of start-up activity, he
or she becomes either an entrepreneur or an intrapre-
neur (Parker 2011; Tietz and Parker 2012), or is
indifferent to the two alternatives (Martiarena 2013).
Thus, in these frameworks, individuals do not funda-
mentally distinguish between entrepreneurship and
intrapreneurship: In the former case, a distinction is
made based on whether an individual has a start-up
plan in mind; in the latter, no special distinction is
made among the three alternatives.
However, a decision on whether to work indepen-
dently, and thus whether to be able to access capital and
takerisks,wouldbeasimportant aswhethertoengagein
a start-up activity. Therefore, in this paper, we view
entrepreneurship and intrapreneurship as economically
two different start-up modes. In particular, we note that
many empirical studies find that credit matters signif-
icantly to the individual’s decision to become involved
in independent entrepreneurship. 6 Thus, an important
economic distinction between entrepreneurship and
intrapreneurship lies in the difference in access to
capital and risk-taking. In independent entrepreneur-
ship, entrepreneurs need to raise capital by themselves
and are fully responsible for failures, whereas in
intrapreneurship, almost all the financial burden is on
established organizations. 7 As Knight (1921, p. 299)
claims, ‘‘the entrepreneur ... takes over all the uncer-
tainty of the business along with control over it.’’ Not
only does this economic difference motivate our
empirical model, it also plays an important role in
identifying its parameters (see Sect. 4.1 below).
In contrast, a distinction is made between start-up
activities (including both independent entrepreneur-
ship and intrapreneurship) and doing something else
(including unemployment) in Parker’s (2011) concep-
tual framework. Parker (2011) argues that this decision
is affected by family status. This is because start-up
activities are presumably more intensive, and thus, an
individual would care about his or her family status
when choosing whether to work for a start-up. How-
ever, once he or she decides to involve in a start-up
activity, family status no longer matters to the choice of
independent entrepreneurship or intrapreneurship.
Parker (2011) uses this feature to identify his empirical
model. Unfortunately, because of this identification
strategy, one cannot use Parker’s (2011) framework to
study how family status is related to the two start-up
modes independently. In contrast, our conceptual
framework not only reflects the importance of the
economic distinction between entrepreneurship and
intrapreneurship, but also has a methodological advan-
tage in empirically examining the gender gap in start-
up activities because family status should presumably
not be ignored to study this issue.
However, our conceptual framework is not a
panacea. To estimate our empirical model, individuals
in the second stage, who are either intrapreneurs or
employees, must have the same covariates. As
explained in Sect. 3.1 below, PSED II consists of two
parts: the initial screening process and the follow-up.
The follow-up part of PSED II has detailed informa-
tion, such as Parker’s (2011) ‘‘employer size,’’ on
entrepreneurs and intrapreneurs. The disadvantage of
employing our conceptual framework is that we are not
able to use the follow-up part of PSED II to include a
richer set of covariates than the initial screening
process has. 8 Thus, we focus on the decisions by those
5 See Bethlehem et al. (2011) for an argument of why the
bivariate probit model with sample selection (‘‘double selec-
tion’’ ) is better than other models such as the multinomial logit,
nested logit, and multilevel models. 6 See, e.g., Evans and Leighton (1989a, b), Evans and
Jovanovic (1989), Holtz-Eakin et al. (1994a, b), Hamilton
(2000), Parker (2000), Kawaguchi (2003), Hurst and Lusardi
(2004), Kan and Tsai (2006), Buera (2009), Mondragón-Vélez
(2009), Malchow-Møller et al. (2010), Fairlie and Krashinsky
(2012), and McCann and Folta (2012). Rybczynski (2009)
examines an issue similar to the one central to this study and
finds that a gender gap in self-employment earnings can mostly
be ascribed to liquidity constraints.
7 However, this is not to say that intrapreneurs are not
incentivized; if they fail, it becomes difficult for them to be
promoted or rewarded financially. 8 However, it is possible to use the follow-up part for the
purpose of identifying who actually started a business after
statement in the initial screening process. Our main results do
Gender differences in entrepreneurship and intrapreneurship 449
123
who are currently employed, and thus, the non-
employed is excluded from our sample, whereas in
Parker’s (2011) framework, it is possible to include
non-employed individuals in the initial stage. How-
ever, we recognize the importance of controlling for
the size of the organization that the individual works
for. To do so, we match the March 2005 version of the
Current Population Survey (CPS) with PSED II
because it has information on firm size for each
individual who is currently employed. In short,
Parker’s (2011) conceptual framework and ours com-
plement each other, and the latter reflects our interest in
the gender gap in the two modes of start-up activity.
The rest of the paper is organized as follows.
Section 2 presents our hypotheses by discussing exist-
ing studies that are most closely related to our study.
After describing the data used for this study in Sect. 3,
wepresentthe empiricalanalysisin Sect. 4. Wenot only
provide parameter estimates of the alternative specifi-
cations, but also compute the actual and counterfactual
probabilities of becoming an entrepreneur or intrapre-
neur and show the decomposition results for gender
differences. Section 5 concludes the paper.
2 Related literature and hypothesis building
We aim to contribute to the understanding of gender
differences in start-up activities by broadening the
concept of start-up to include intrapreneurship as well.
As such, this paper lies at the intersection of the two
strands of the literature: (1) gender differences in
(independent) entrepreneurship and (2) how (inde-
pendent) entrepreneurship and intrapreneurship
differ. 9,10
To the best of our knowledge, Kacperczyk (2015) is
the only study lying at this intersection to examine the
gender gap in entrepreneurship in a broader context
that includes corporate entrepreneurship as an entre-
preneurial activity as well. Using detailed data from
1980 to 2005 of fund managers in the mutual funds
industry, Kacperczyk (2015) finds that women are
more likely to pursue intrapreneurship than start-up
entrepreneurship because they can make use of
maternity benefits, such as maternity leave, within
the firm, while being rewarded financially at the same
time. This balance may not be easily attained when
women pursue start-up entrepreneurship. However,
Kacperczyk (2015) does not support the presumption
that gender differences in risk-taking behavior cause
the observed gender gap in entrepreneurial activities.
In contrast, this paper, by using nationally represen-
tative data and additional information to control for
firm size, suggests that women would find it more
difficult to become intrapreneurs than independent
entrepreneurs, implying that women, on average, may
be facing more solid ‘‘ceilings’’ within established
organizations than in the marketplace. In addition,
women who work in the financial industry may be
relatively homogenous in terms of attitudes toward
risk, as evidenced by Johnson and Powell (1994), who
find that gender differences with respect to risk
attitudes are quite small in the managerial subsample,
whereas in the non-managerial subsample, women
show more risk aversion than men.
To consider how gender differences matter to
entrepreneurship and intrapreneurship, recall first that
entrepreneurship presumably entails risk or uncer-
tainty, as explained in the Introduction. 11
This idea is
seen in existing empirical studies showing that indi-
viduals with lower risk aversion are more likely to
become entrepreneurs (e.g., Ekelund et al. 2005;
Caliendo et al. 2009, 2015; Ahn 2010). In relation to
our interest in both entrepreneurship and intrapreneur-
ship, Douglas and Fitzsimmons (2013) and Martiarena
Footnote 8 continued
not change significantly even if we use the follow-up part. The
details are available upon request. 9 In a different vein, Moriano et al. (2014) examine how
managerial leadership styles affect intrapreneurial behavior and
find that transformative leadership—in which, e.g., a mission is
shared, mentoring is provided, and innovative thinking is
encouraged—is more effective to intrapreneurship than trans-
actional leadership—in which, e.g., employees are extrinsically
incentivized, and job scopes are predetermined. See Honig
(2001), Monsen et al. (2010), and Zhang and Bartol (2010) for
other psychological studies of intrapreneurship. 10
For other studies that compare different groups of start-up
participants, see Sardy and Alon (2007) on franchise and nascent
entrepreneurs, Renko (2013) on social and conventional
Footnote 10 continued
entrepreneurs, Kim et al. (2015) on leisure-based and conven-
tional entrepreneurs, and Parker (2014) on serial and portfolio
entrepreneurs. 11
In this paper, we do not distinguish between risk aversion and
uncertainty aversion, as opposed to Knight’s (1921) emphasis
on this distinction. Skeptical views toward Knight’s (1921)
distinction can be found in, e.g., Schultz (1980), LeRoy and
Singell (1987), Demsetz (1988), and Runde (1998).
450 T. Adachi, T. Hisada
123
(2013) find that intrapreneurs are more risk averse than
entrepreneurs. As for the relationship between risk
aversion and gender, existing studies in experimental
economics have repeatedly found strong evidence
that, controlling for other demographic characteristics
such as age, educational attainment, occupation, and
cultural background, women are on average more risk
averse than men both in the laboratory (usually, in the
context of lottery choices) and in the field (usually, in
the context of investment decisions). 12
At the same time, however, entrepreneurship can
give women greater autonomy, and this especially
benefits them, depending on their family structure
(e.g., Lombard 2001; Edwards and Field-Hendrey
2002). The studies by Macpherson (1988) and Carr
(1996) are among the first to put forth the view that
women with children favor self-employment owing to
the flexibility with respect to time management that it
offers. Hundley (2000) also finds that in the self-
employment sector, the gender gap in earnings is more
sensitive to family structure. In particular, self-
employed women with children spend significantly
less work time than those without children. On the
other hand, Wellington (2006) finds that married
women with more family workload are more likely to
choose self-employment. This tendency is stronger for
more educated women. Furthermore, using data from
several European countries, Noseleit (2014) finds that
the presence of children raises the women’s probabil-
ity of becoming self-employed and establishes the
causal relationship for this; self-employment per se
does not raise fertility. Patrick et al. (2016), using
detailed data on demographics from US metropolitan
areas from 1994 to 2008, also find that household
workload owing to the presence of children is
positively associated with the rate of self-employment
for married women. In contrast, Taniguchi (2002) does
not find a clear effect of children on women’s self-
employment. Similarly, using UK data, Saridakis
et al. (2014) find that household variables are less
significant than economic environments for both men
and women in explaining self-employment choices.
This finding holds in both the short-run and long-run
trends.
Thus, there would be several countervailing factors
working as determinants of independent entrepreneur-
ship by women. Indeed, Fossen (2012) finds that only a
tiny portion of women’s lower rate of entrepreneurial
entry is explained by their higher risk aversion,
suggesting various types of discrimination toward
women entrepreneurs may be the reason. We therefore
establish the following hypothesis on the relationship
between gender and independent entrepreneurship.
Hypothesis 1a Women are more likely to become
entrepreneurs than men are if they highly value the
greater autonomy and flexibility that entrepreneurship
offers. They are less likely to become entrepreneurs if
they strongly avoid the greater risk that entrepreneur-
ship entails or if they face more severe challenges that
make it difficult for them to become entrepreneurs,
such as credit constraints or discrimination.
In contrast, less complex factors would be
involved in the lower rate of women’s intrapreneur-
ship. It is, more or less, a result of an internal process
of organization, and we expect that women are less
likely to become intrapreneurs. This is because, first,
it may still entail greater uncertainty than wage work
does. More importantly, intrapreneurship might
require individuals to devote much time to it. This
effect might be stronger for women with children
than for men with children. In relation to this point,
Becker (1985) argues that married women invest less
in their human capital than married men do even
when they work for the same number of hours,
because women are mainly responsible for childcare
and other household activities. Furthermore, women
may be treated unequally in the workplace, as
discussed in the first paragraph of the Introduction.
It may also be that men are in a more advantageous
position for intrapreneurship. Indeed, employers and
co-workers may discriminate against women employ-
ees (e.g., Becker 1957). Employers may also have
prejudices against female workers that they are less
capable or less reliable on average than male workers
(e.g., Phelps 1972). For all these reasons, we have the
following hypothesis on the relationship between
gender and intrapreneurship.
Hypothesis 1b Women are less likely to become
intrapreneurs than men are.
12 Croson and Gneezy (2009) point out the following three
reasons for these gender differences: (1) emotions (according to
psychological studies, women react to uncertain situations more
emotionally and fear adverse outcomes more than men do), (2)
overconfidence (men are more overconfident than women), and
(3) perception of risk as challenges or threats.
Gender differences in entrepreneurship and intrapreneurship 451
123
Based on the argument above, we also presume that
entrepreneurship and intrapreneurship may mean
different things for married individuals with children,
establishing the following hypotheses.
Hypothesis 2a Women with children are more
likely to choose entrepreneurship than their male
counterparts.
Hypothesis 2b In contrast, women with children are
less likely to be an intrapreneur than their male
counterparts.
Finally, we also examine whether part-time work
has different meanings across genders.
Hypothesis 3a Female part-time workers are more
likely to choose entrepreneurship than their male
counterparts.
Hypothesis 3b In contrast, female full-time work-
ers are less likely to be an intrapreneur than their male
counterparts.
To conclude this section, one can think of the two
selection problems as interrelated. In the empirical
model presented below, we consider this possibility by
allowing correlation between unobserved factors in
the choice of entrepreneurship and in the choice of
intrapreneurship. These unobserved factors would
conceivably be related to ‘‘entrepreneurial skills/tal-
ents’’ (Lucas 1978) in general.
3 Data
3.1 Sample construction
The data for this study are constructed from the Panel
Study of Entrepreneurial Dynamics II (PSED II),
provided by the Survey Research Center at the
University of Michigan. 13
PSED II intends to be
nationally representative as a longitudinal dataset that
comprises individuals in the process of business
formation (i.e., nascent entrepreneurs) and is an
improved version of PSED I. From September 2005
to February 2006, an initial screening was conducted
to identify a cohort, and in total, 31,845 individuals
were selected as a nationally representative sample of
the US population. Their age is recorded as a
categorical variable, ranging from ‘‘18 to 20’’ and
‘‘75 and up.’’ Then, follow-up interviews were con-
ducted for these nascent entrepreneurs once a year
until 2010. Thus, in total, there were six waves: 2005,
2006, 2007, 2008, 2009, and 2010.
For our empirical analysis, we use data from the
initial screening process in PSED II. Originally, it has
31,845 individuals, including those who are currently
business owners [that is, those who answer ‘‘yes’’ to
the question, ‘‘Are you, alone or with others, currently
the owner of a business you help manage, including
self-employment or selling any goods or services to
others?’’ (QFF1c)]. They represent 14.4 % of the total
or 4573 individuals. Because our conceptual frame-
work (presented in the next section) targets those who
are currently working for an established organization,
we exclude current business owners, other races than
Blacks, Hispanics, and Whites, retirees, and the non-
employed. That leaves us with 13,724 individuals. To
help us define nascent entrepreneurs and nascent
intrapreneurs, we use the following two questions:
1. ‘‘Are you, alone or with others, currently trying to
start a new business, including any self-employ-
ment or selling any goods or services to others?’’
(QFF1a)
2. ‘‘Are you, alone or with others, currently trying to
start a new business or a new venture for your
employer, an effort that is part of your normal
work?’’ (QFF1b)
If a respondent answers ‘‘yes’’ to QFF1a and ‘‘no’’ to
QFF1b, then he is deemed a nascent entrepreneur (see
Table 1). Nascent intrapreneurs are those who answer
‘‘yes’’ to QFF1b. If a respondent answers ‘‘no’’ to both
Table 1 Categorization of start-up participants
Answer to QFF1b
Yes No
QFF1a
Yes Nascent Nascent
Intrapreneurs Entrepreneurs
No Nascent
Intrapreneurs Uninvolved
13 PSED II is freely downloadable at http://www.psed.isr.
umich.edu/. For general references for PSED II, see Reynolds
and Curtin (2009), Davidsson and Gordon (2012), and Gartner
and Shaver (2012).
452 T. Adachi, T. Hisada
123
questions, he is neither a nascent entrepreneur nor an
intrapreneur. Thus, we have three categories: (1) a
nascent entrepreneur (631 individuals), (2) a nascent
intrapreneur (622 individuals), and (3) not involved in
a start-up (12,471 individuals).
Furthermore, among these nascent entrepreneurs,
only those who answer positively to the following two
questions are deemed real nascent entrepreneurs: (1)
‘‘Over the past 12 months, have you done anything to
help start a new business, such as looking for
equipment or a location, organizing a start-up team,
working on a business plan, beginning to save money,
or any other activity that would help launch a
business?’’ (QFF2) and (2) ‘‘Will you personally
own all, part, or none of this new business?’’ (QFF3).
The number of nascent entrepreneurs is 380. The rest
(251 individuals) are categorized as not being involved
in a start-up.
Next, among those initially categorized as potential
intrapreneurs, only those who answer positively to
QFF2 above are deemed real nascent intrapreneurs.
They do not necessarily have to own a part of the new
business. The number of such individuals is 370, and
the rest (252 individuals) are categorized as not being
not involved in a start-up . Unfortunately, in the
screening process to determine nascent business
starters (entrepreneurs in PSEDII language), informa-
tion on work experience is not collected. Thus, age is
interpreted as a rough measure of work experience. As
for household income, we transform categorical
values into continuous values, ranging from $10,000
to $125,000. We then take the logarithm of these
values. 14
PSED II also misses the size of the firm for which an
individual works. This is important because the
meaning of intrapreneurship would vary across firm
sizes. Thus, we use the method of propensity score
matching to merge the data with the March 2005
version of the Current Population Survey (CPS) to add
these two variables to our constructed sample. 15
We
also add another important piece of information,
which is whether the respondent is US born. This is
because, race, which would presumably be an impor-
tant factor in the context of entrepreneurship and
intrapreneurship, would matter differently if we do or
do not control for whether English is the individual’s
first language. As a result of this merger, the sample
size for estimation is 11,113, with 322 independent
entrepreneurs, 311 intrapreneurs, and 10,480 individ-
uals who are involved in neither activity.
3.2 Summary statistics
Table 2 shows the summary statistics of all 11,113
individuals in the entire sample for each (exclusive)
occupational mode. All variables are dummy variables
(taking 0 or 1) except ‘‘household size’’ and ‘‘income’’
(as well as ‘‘unemployment rate,’’ ‘‘homestead exemp-
tion,’’ ‘‘median home price,’’ and three tax rates; we
will explain these variables when we discuss identi-
fication of our model in the next section). As explained
above, age is used as a categorical variable in the
original screening part of PSED II, and its categoriza-
tion is arranged in the same manner as Parker (2011).
Notably, when compared with the uninvolved
individuals, the ratios of women are significantly
smaller for both the entrepreneurship and
intrapreneurship groups. As for family variables, the
number of household members is the highest for the
entrepreneurship and intrapreneurship groups. The
ratio of child presence (under age 11) is also higher in
the entrepreneurship and intrapreneurship groups. The
ratio of married individuals is the highest and the
family size is the lowest in the no-involvement group.
The mean income is the highest among nascent
intrapreneurs ($67,030), followed by non-business
starters ($65,810) and the nascent entrepreneurs
($65,330). This may be consistent with Hamilton’s
(2000) finding that suggests the importance of nonpe-
cuniary benefits from independent entrepreneurship.
Furthermore, this seems to support our hypotheses
(1a) and (1b); individuals may be required to devote
much time to intrapreneurship. As a result, married
women with children may not favor intrapreneurship,
while male counterparts do not. Regarding work
status, the ratio of full-time workers is higher for the
intrapreneurship group. Interestingly, in each group,
30–35 % work for organizations of less than 25
workers, and another 35–40 % work for organizations
of 1000 workers or more. Note also that 40–50 %
14 More specifically, these values take $10,000, $20,000,
$27,500, $32,500, $37,500, $45,000, $55,000, $67,500,
$87,500, and $125,000. 15
This method of ‘‘data fusion’’ is justifiably strengthened by
the fact that PSED II uses the 2005 March CPS to compute the
weight variable, ‘‘WT_SCRN’’ (see page 2 of http://www.psed.
isr.umich.edu/psed/download_node/157).
Gender differences in entrepreneurship and intrapreneurship 453
123
Table 2 Summary statistics: across modes
Uninvolved Entrepreneurship Intrapreneurship
Mean Mean Mean
Female 0.512 0.356*** 0.296***
Family
Married 0.602 0.561 0.531**
Children under age 11 0.344 0.435*** 0.409**
Size 2.993 3.311*** 3.192**
Income 65,815 65,329 67,027
Employment
Work status
Full-time 0.829 0.828 0.858
Part-time 0.171 0.172 0.142
Firm size
Firm size 99 or less 0.435 0.485* 0.431
Firm size 100–999 0.187 0.145** 0.165
Firm size 1000 or more 0.378 0.370 0.404
Race
White 0.743 0.638*** 0.637***
Black 0.122 0.206*** 0.175**
Hispanic 0.135 0.156 0.188**
Foreign born 0.119 0.098 0.153
Age
18–24 0.121 0.148 0.194***
25–34 0.241 0.282 0.295**
35–44 0.263 0.280 0.249
45–54 0.238 0.223 0.194*
55–64 0.107 0.057*** 0.060***
65 and more 0.030 0.010*** 0.007***
Education
HS dropout 0.055 0.076 0.096**
HS graduate 0.272 0.204*** 0.264
Some college 0.270 0.360*** 0.253
Bachelor 0.257 0.251 0.229
Postgraduate 0.146 0.109** 0.158
Internet 0.834 0.921*** 0.873**
Non-metro area 0.233 0.194* 0.213
Unemployment rate 5.084 5.013 5.089
Homestead exemption 128.6 162.1*** 146.5
Median home value 202.0 203.2 205.8
Maximum personal income tax rate 5.552 5.124** 5.323
Maximum corporate income tax rate 6.673 6.372* 6.648
Sales tax rate 5.324 5.289 5.425
N 10,480 322 311
Sample weights are used to calculate the means. The unit is $1000 for homestead exemption and median home value
The three tax rates are in percentage terms
* p\0:1; ** p\0:05; *** p\0:01 when compared with uninvolved
454 T. Adachi, T. Hisada
123
work for organizations of 100 workers or less in each
group.
Next, the ratios of black individuals are higher in
the entrepreneurship and intrapreneurship groups than
in the no-involvement group. This is also true for
Hispanic individuals. In the no-involvement group,
36 % are aged 18–34, whereas 43 % of the nascent
entrepreneurs, and 49 % of the nascent intrapreneurs
are 18–34 years old. On the other hand, 14 % of those
not engaged in start-up activities are aged 55 or older,
whereas the percentages are 7 % for the entrepreneurs
and 7 % for the intrapreneurs. These numbers imply
that the groups of business starters consist of younger
individuals. Regarding education, the ratio of individ-
uals with some college education is particularly high
for entrepreneurship. Both in the uninvolved and the
intrapreneurship groups, college graduates (including
those with postgraduate degrees) account for about
40 %. This percentage is slightly lower for
entrepreneurs.
To look at our estimation sample from a viewpoint
of gender, Table 3 presents the means of variables for
each gender. 16 The average household income of male
interviewees ($68,000) is higher than that of female
interviewees ($63,700). While the ratios of male
entrepreneurship are higher than that of female (4.1
and 2.3 %, respectively), the ratio of male
intrapreneurship (4.5 %) is higher than that of female
intrapreneurship (1.9 %). The ratio of women working
part-time (22 %) is much higher than that of men
(12 %). It is also observed that women tend to work for
a middle-sized firm. Finally, the ratio of women with a
college degree or more among all women (43 %) is
higher than the corresponding ratio for men (37 %).
Table 3 Summary statistics: across genders
Male Female
Mean Mean
Uninvolved 0.914 0.958***
Entrepreneurship 0.041 0.023***
Intrapreneurship 0.045 0.019***
Family
Married 0.614 0.584***
Children under age 11 0.355 0.343
Size 3.057 2.961***
Income 67,962 63,714***
Employment
Work status
Full-time 0.881 0.779***
Part-time 0.119 0.221***
Firm size
Firm size 99 or less 0.444 0.430
Firm size 100–999 0.173 0.197***
Firm size 1000 or more 0.383 0.373
Race
White 0.741 0.732
Black 0.103 0.149***
Hispanic 0.156 0.119***
Foreign born 0.118 0.121
Age
18–24 0.135 0.113***
25–34 0.231 0.256***
35–44 0.279 0.247***
45–54 0.225 0.247***
55–64 0.104 0.104
65 and more 0.024 0.033***
Education
HS dropout 0.068 0.046***
HS graduate 0.287 0.253***
Some college 0.272 0.272
Bachelor 0.239 0.273***
Postgraduate 0.134 0.156***
Internet 0.821 0.855***
Non-metro area 0.237 0.226
Unemployment rate 5.083 5.081
Homestead exemption 129.3 131.0
Median home value 200.3 204.0*
Maximum personal income
tax rate
5.486 5.575
Maximum corporate income
tax rate
6.662 6.663
Sales tax rate 5.363 5.288***
Table 3 continued
Male Female
Mean Mean
N 5874 5239
Sample weights are used to calculate the means. The unit is
$1000 for homestead exemption and median home value. The
three tax rates are in percentage terms
* p\0:1; ** p\0:05; *** p\0:01 when compared with male
16 The reason for statistical significances in the age groups
would be ascribed to the fact that on average women live longer
than men do.
Gender differences in entrepreneurship and intrapreneurship 455
123
4 Empirical analysis
In this section, we first explain our bivariate probit
model with sample selection. Then, we show the
estimates of the model with different specifications.
Finally, we show decomposition results to argue how
much gender differences matter to the choices of
entrepreneurship and intrapreneurship, with focus on
one’s family and employment status.
4.1 Estimates of the bivariate probit model
with sample selection
We now propose and estimate a sample selection
model based on the following conceptual framework.
First, an individual chooses whether to work indepen-
dently. If he chooses this option, he is called an
entrepreneur. 17
If he does not become an entrepre-
neur, then he chooses whether to become an in-
trapreneur. The individual chooses one of the three
alternatives that gives him the best utility (see
Footnote 4 above).
More formally, let di 2 f0; 1g, where di ¼ 1 indi- cates individual i choosing to opt out from working
independently, and di ¼ 0 indicates i becoming an entrepreneur, and let li 2 f0; 1g denote whether individual i, conditional on di ¼ 1, becomes an intrapreneur (li ¼ 1) or not (li ¼ 0). If individual i chooses di ¼ 0, then his utility is written as
ui ¼ a0 þ a1femalei þ x0ia þ �1i; ð1Þ
where femalei is a dummy variable that indicates
individual i’s gender, and xi and �1i include other
control variables and all unobservable factors, respec-
tively. Similarly, individual i’s utility as an intrapre-
neur is written as
vi ¼ b0 þ b1femalei þ z 0 ib þ �2i; ð2Þ
where zi indicates control variables, and �2i collects all
unobserved factors, while he obtains (normalized)
zero utility from li ¼ 0. Thus, individual i, conditional on di ¼ 1, becomes an intrapreneur (i.e., li ¼ 1) if and only if vi � 0. Knowing this order structure, individual
i first chooses entrepreneurship (i.e., di ¼ 0) if and only if ui � vi.
For identification of the parameters, it must be that
xi 6¼ zi (i.e., the exclusion restriction). In this study, we assume that zi � xi and that ðxi � ziÞ contains variables that are considered related to individual i’s
personal wealth. In particular, ‘‘bankruptcy exemption
in 2005’’ and ‘‘median home value in 2005’’ are
included in ðxi � ziÞ. These two variables vary across states and are assumed to provide exogenous varia-
tions. The existing studies stress that capital con-
straints would prevent potential entrepreneurs from
start-up activities. 18
However, capital constraints
would be much less relevant when an individual does
not work independently. This is the economic justifi-
cation for excluding these two variables from zi.
Additionally, we also include the 2005 annually
averaged state-specific unemployment rate, as well
as taxes for individual income, corporate income, and
sales. See Appendix for more details on these
variables. 19
We further assume that ð�1i; �2iÞ is distributed identically and independently across individuals and
is independent of ðfemalei, xiÞ and ðfemalei, zi).20 The distribution is bivariate normal with mean (0, 0), and
we allow for correlation between �1i and �2i, with the
correlation coefficient denoted by q. The parameters of Eqs. (1) and (2) are jointly estimated by the
maximum likelihood method.
4.1.1 Selection of entrepreneurship (Eq. 1)
The estimation results of Eq. (1) are presented in
Table 4. These are average (for discrete variables) and
marginal (for continuous variables) effects (the
parameter estimates are available upon request). We
consider five specifications. In Specification 1, no
17 In line with our conceptual framework described here, our
empirical analysis does not make a distinction between the self-
employed and business owners and treats them as entrepreneurs.
In addition, the qualification ‘‘nascent’’ is dropped for simpler
expressions.
18 See the references in Footnote 6 above, as well as, e.g., Fan
and White (2003), Berkowitz and White (2004), Paik (2013),
Rohlin and Ross (2016), and Cerqueiro and Penas (2016) for
bankruptcy exemption and entrepreneurship, and Blanchflower
and Oswald (1998), Taylor (2001), Adelino et al. (2015), and
Schmalz et al. (2016) for housing and entrepreneurship. 19
This additional state-level information was merged with the
original PSED II at the Institute of Social Research, University
of Michigan, as per our request. See http://www.psed.isr.umich.
edu/psed/home for a procedure (accessed July 2016). 20
We do not use household income as an explanatory variable
in fear of its possible correlation with �1i or �2i.
456 T. Adachi, T. Hisada
123
T a b le
4 E n tr e p re n e u rs h ip
(E q . 1 )
S p e c ifi c a ti o n 1
S p e c ifi c a ti o n 2
S p e c ifi c a ti o n 3
S p e c ifi c a ti o n 4
S p e c ifi c a ti o n 5
D e p e n d e n t v a ri a b le
1 =
S ta y in g in
0 =
G o in g o u t (e n tr e p re n e u rs h ip )
F e m a le
0 .0 1 6 * * *
(0 .0 0 3 )
0 .0 2 4 * * *
(0 .0 0 8 )
0 .0 1 5 *
(0 .0 0 8 )
0 .0 2 3 * *
(0 .0 1 1 )
0 .0 4 8 * * *
(0 .0 1 6 )
F a m il y
M a rr ie d
0 .0 0 6
(0 .0 0 4 )
0 .0 1 1 * *
(0 .0 0 5 )
0 .0 0 5
(0 .0 0 4 )
0 .0 1 0 * *
(0 .0 0 5 )
0 .0 0 6
(0 .0 0 4 )
C h il d re n u n d e r a g e 1 1
- 0 .0 0 4
(0 .0 0 4 )
- 0 .0 0 4
(0 .0 0 5 )
- 0 .0 0 4
(0 .0 0 4 )
- 0 .0 0 4
(0 .0 0 5 )
- 0 .0 0 4
(0 .0 0 4 )
S iz e
- 0 .0 0 2
(0 .0 0 1 )
- 0 .0 0 2
(0 .0 0 2 )
- 0 .0 0 2
(0 .0 0 1 )
- 0 .0 0 2
(0 .0 0 2 )
- 0 .0 0 2
(0 .0 0 1 )
M a rr ie d 9
fe m a le
- 0 .0 1 2 *
(0 .0 0 7 )
- 0 .0 1 1
(0 .0 0 7 )
C h il d re n u n d e r a g e 1 1 9
fe m a le
(� 1 0 � 1 )
- 0 .0 1 0
(0 .0 8 3 )
- 0 .0 0 5
(0 .0 8 3 )
S iz e 9
fe m a le
(� 1 0 � 1 )
- 0 .0 0 1
(0 .0 2 8 )
- 0 .0 0 4
(0 .0 2 8 )
E m p lo y m e n t
W o rk
st a tu s: R e f =
fu ll -t im
e
P a rt -t im
e - 0 .0 0 8 *
(0 .0 0 5 )
- 0 .0 0 7
(0 .0 0 5 )
- 0 .0 1 5 * *
(0 .0 0 7 )
- 0 .0 1 3 *
(0 .0 0 7 )
- 0 .0 0 8 *
(0 .0 0 5 )
P a rt -t im
e 9
fe m a le
0 .0 1 2
(0 .0 0 9 )
0 .0 1 1
(0 .0 0 9 )
F ir m
si z e : R e f = F ir m
si z e 1 0 0 – 9 9 9
F ir m
si z e 9 9 o r le ss
- 0 .0 0 3
(0 .0 0 5 )
- 0 .0 0 3
(0 .0 0 4 )
- 0 .0 0 4
(0 .0 0 6 )
- 0 .0 0 4
(0 .0 0 6 )
- 0 .0 0 4
(0 .0 0 5 )
F ir m
si z e 1 0 0 0 o r m o re
(� 1 0 � 1 )
- 0 .0 0 9
(0 .0 4 6 )
- 0 .0 1 2
(0 .0 4 6 )
0 .0 0 7
(0 .0 6 0 )
0 .0 0 3
(0 .0 6 0 )
- 0 .0 0 7
(0 .0 4 6 )
F ir m
si z e 9 9 o r le ss
9 fe m a le
0 .0 0 1
(0 .0 0 9 )
0 .0 0 1
(0 .0 0 9 )
F ir m
si z e 1 0 0 0 o r m o re
9 fe m a le
- 0 .0 0 4
(0 .0 0 9 )
- 0 .0 0 4
(0 .0 0 9 )
Gender differences in entrepreneurship and intrapreneurship 457
123
T a b le
4 c o n ti n u e d
S p e c ifi c a ti o n 1
S p e c ifi c a ti o n 2
S p e c ifi c a ti o n 3
S p e c ifi c a ti o n 4
S p e c ifi c a ti o n 5
R a c e : R e f =
w h it e
B la c k
- 0 .0 2 5 * * *
(0 .0 0 5 )
- 0 .0 2 6 * * *
(0 .0 0 5 )
- 0 .0 2 5 * * *
(0 .0 0 5 )
- 0 .0 2 5 * * *
(0 .0 0 5 )
- 0 .0 2 5 * * *
(0 .0 0 5 )
H is p a n ic
- 0 .0 0 4
(0 .0 0 6 )
- 0 .0 0 4
(0 .0 0 6 )
- 0 .0 0 4
(0 .0 0 6 )
- 0 .0 0 4
(0 .0 0 6 )
- 0 .0 0 4
(0 .0 0 6 )
F o re ig n b o rn
0 .0 1 4 * *
(0 .0 0 6 )
0 .0 1 5 * *
(0 .0 0 6 )
0 .0 1 4 * *
(0 .0 0 6 )
0 .0 1 4 * *
(0 .0 0 6 )
0 .0 1 4 * *
(0 .0 0 6 )
A g e : R e f =
5 5 a n d m o re
1 8 – 2 4
- 0 .0 0 2
(0 .0 0 7 )
- 0 .0 0 1
(0 .0 0 8 )
- 0 .0 0 1
(0 .0 0 7 )
- 0 .0 0 0
(0 .0 0 8 )
- 0 .0 0 2
(0 .0 0 7 )
2 5 – 3 4
- 0 .0 1 3 * *
(0 .0 0 6 )
- 0 .0 1 3 * *
(0 .0 0 6 )
- 0 .0 1 4 * *
(0 .0 0 6 )
- 0 .0 1 3 * *
(0 .0 0 6 )
- 0 .0 1 3 * *
(0 .0 0 6 )
3 5 – 4 4
- 0 .0 1 4 * *
(0 .0 0 6 )
- 0 .0 1 4 * *
(0 .0 0 6 )
- 0 .0 1 5 * *
(0 .0 0 6 )
- 0 .0 1 4 * *
(0 .0 0 6 )
- 0 .0 1 4 * *
(0 .0 0 6 )
4 5 – 5 4
- 0 .0 1 2 * *
(0 .0 0 5 )
- 0 .0 1 1 * *
(0 .0 0 5 )
- 0 .0 1 2 * *
(0 .0 0 5 )
- 0 .0 1 1 * *
(0 .0 0 5 )
- 0 .0 1 2 * *
(0 .0 0 5 )
E d u c a ti o n : R e f =
H S d ro p o u t
H S g ra d u a te
0 .0 2 3 * * *
(0 .0 0 7 )
0 .0 2 4 * * *
(0 .0 0 7 )
0 .0 2 3 * * *
(0 .0 0 7 )
0 .0 2 3 * * *
(0 .0 0 7 )
0 .0 3 4 * * *
(0 .0 0 8 )
S o m e c o ll e g e
0 .0 0 9
(0 .0 0 7 )
0 .0 0 9
(0 .0 0 7 )
0 .0 0 8
(0 .0 0 7 )
0 .0 0 8
(0 .0 0 7 )
0 .0 1 4 *
(0 .0 0 8 )
B a c h e lo r
0 .0 1 8 * *
(0 .0 0 7 )
0 .0 1 8 * *
(0 .0 0 7 )
0 .0 1 8 * *
(0 .0 0 7 )
0 .0 1 8 * *
(0 .0 0 7 )
0 .0 2 6 * * *
(0 .0 0 9 )
P o st g ra d u a te
0 .0 1 8 * *
(0 .0 0 8 )
0 .0 1 8 * *
(0 .0 0 8 )
0 .0 1 8 * *
(0 .0 0 8 )
0 .0 1 8 * *
(0 .0 0 8 )
0 .0 2 6 * * *
(0 .0 0 9 )
H S g ra d u a te
9 fe m a le
- 0 .0 4 0 * *
(0 .0 1 7 )
S o m e c o ll e g e 9
fe m a le
- 0 .0 2 9 *
(0 .0 1 7 )
B a c h e lo r 9
fe m a le
- 0 .0 3 4 * *
(0 .0 1 7 )
P o st g ra d u a te
9 fe m a le
- 0 .0 3 4 *
(0 .0 1 8 )
458 T. Adachi, T. Hisada
123
T a b le
4 c o n ti n u e d
S p e c ifi c a ti o n 1
S p e c ifi c a ti o n 2
S p e c ifi c a ti o n 3
S p e c ifi c a ti o n 4
S p e c ifi c a ti o n 5
In te rn e t
- 0 .0 2 3 * * *
(0 .0 0 5 )
- 0 .0 2 3 * * *
(0 .0 0 5 )
- 0 .0 2 3 * * *
(0 .0 0 5 )
- 0 .0 2 3 * * *
(0 .0 0 5 )
- 0 .0 2 3 * * *
(0 .0 0 5 )
N o n -m
e tr o a re a
- 0 .0 0 1
(0 .0 0 4 )
- 0 .0 0 1
(0 .0 0 4 )
- 0 .0 0 1
(0 .0 0 4 )
- 0 .0 0 1
(0 .0 0 4 )
- 0 .0 0 1
(0 .0 0 4 )
U n e m p lo y m e n t ra te
(� 1 0 � 2 )
- 0 .0 0 6
(0 .2 1 0 )
- 0 .0 0 2
(0 .2 0 9 )
- 0 .0 0 5
(0 .2 1 0 )
- 0 .0 0 2
(0 .2 0 9 )
- 0 .0 1 2
(0 .2 1 1 )
H o m e st e a d e x e m p ti o n (�
1 0 � 2 )
- 0 .0 0 2 *
(0 .0 0 1 )
- 0 .0 0 2 *
(0 .0 0 1 )
- 0 .0 0 2 *
(0 .0 0 1 )
- 0 .0 0 2 *
(0 .0 0 1 )
- 0 .0 0 2 *
(0 .0 0 1 )
M e d ia n h o m e v a lu e (�
1 0 � 2 )
- 0 .0 0 3 *
(0 .0 0 2 )
- 0 .0 0 3 *
(0 .0 0 2 )
- 0 .0 0 3 *
(0 .0 0 2 )
- 0 .0 0 3 *
(0 .0 0 2 )
- 0 .0 0 3 *
(0 .0 0 2 )
M a x im
u m
p e rs o n a l in c o m e ta x ra te
0 .0 0 1
(0 .0 0 1 )
0 .0 0 1
(0 .0 0 1 )
0 .0 0 1
(0 .0 0 1 )
0 .0 0 1
(0 .0 0 1 )
0 .0 0 1
(0 .0 0 1 )
M a x im
u m
c o rp o ra te
in c o m e ta x ra te
(� 1 0 � 1 )
0 .0 0 3
(0 .0 0 7 )
0 .0 0 4
(0 .0 0 7 )
0 .0 0 3
(0 .0 0 7 )
0 .0 0 4
(0 .0 0 7 )
0 .0 0 3
(0 .0 0 7 )
S a le s ta x ra te
0 .0 0 2
(0 .0 0 1 )
0 .0 0 2
(0 .0 0 1 )
0 .0 0 2
(0 .0 0 1 )
0 .0 0 2
(0 .0 0 1 )
0 .0 0 2
(0 .0 0 1 )
N 1 1 ,1 1 3
1 1 ,1 1 3
1 1 ,1 1 3
1 1 ,1 1 3
1 1 ,1 1 3
A v e ra g e (f o r d is c re te
v a ri a b le s)
a n d m a rg in a l (f o r c o n ti n u o u s v a ri a b le s)
e ff e c ts
R o b u st
st a n d a rd
e rr o rs
a re
in p a re n th e se s
* p \
0 :1 ; * * p \
0 :0 5 ; * * * p \
0 :0 1
Gender differences in entrepreneurship and intrapreneurship 459
123
interactions of gender with other variables are con-
sidered. In Specification 2, we consider interactions of
gender with family-related variables (marriage, chil-
dren, and size). Specification 3 has interactions of
gender with employment-related variables (work
status and firm size), and Specification 4 considers
both types of interactions. Finally, interactions with
education are considered in Specification 5. Note that,
by construction, positive signs of coefficients mean
negative effects on entrepreneurship and vice versa.
First, Specification 1 shows that the effect of being
a woman is positive with 1 % statistical significance,
meaning that women are, ceteris paribus, less likely to
become entrepreneurs. This result supports the idea in
Hypothesis 1a that women are eager to avoid
entrepreneurial risk or face more severe credit con-
straints or discrimination. In other words, the benefits
from autonomy and flexibility do not outweigh these
costs and inefficiency losses. This part is statistically
significant for all of the other four specifications. In
contrast to our prior expectation, family size and the
presence of children have no such statistically signif-
icant effects, although, as expected, they have positive
effects entrepreneurship in all of the specifications. 21
Marriage, in contrast, has negative effects, and in
Specifications 2 and 4, the effect is statistically
significant. Regarding employment-related variables,
the size of the firm that the individual currently works
for has no statistically significant effects. In Specifi-
cations 1, 2, and 5, where no interactions of gender
with firm size are considered, it is observed that being
in a large firm has a stronger positive effect on
entrepreneurship than being in a small firm. Being a
part-time employee also has a positive effect, and
except in Specification 2, the effect is statistically
significant. Generally, this suggests that part-time
employees are more likely to pursue entrepreneurship
than are full-time employees. 22
Now, turning our attention to interactions of gender
with family and employment variables, we find that
the presence of children has additional positive effects
for women (Specifications 2 and 4), although the effect
is not statistically significant. Thus, Hypothesis 2a is
weakly supported, and this result is in accordance with
Noseleit’s (2014) study, which finds that having a
child raises women’s probability of becoming self-
employed. 23
Marriage and family size also have
additional positive effects for women. This is consis-
tent with the findings of Patrick et al. (2016) that
married and unmarried women have heterogeneous
preferences for self-employment. It may be the case
that married women have access to greater wealth
because of their husbands’ income/wealth. Regarding
employment-related variables, being a part-time
employee has a negative effect on entrepreneurship
for women (Specifications 3 and 4), offsetting the
positive effect of part-time work alone. Thus, Hypoth-
esis 3a is not supported. This suggests that the meaning
of part-time work may differ across genders: Men may
work part-time to prepare for entrepreneurship,
whereas this may not be the case for women. It is
also observed that women who work for a large firm
have an additional positive effect on the choice of
entrepreneurship, although the effect is not statisti-
cally significant.
Interestingly, Specification 5 in Table 4 shows that
for women, education works positively for
entrepreneurship (and the effects are all statistically
significant), whereas the opposite is true for men. This
result is consistent with, among others, Macpherson
(1988), Evans and Leighton (1989a, b), Devine
(1994), Bates (1995), and Carr (1996). This finding
might indicate that women may be at a disadvantage in
their workplace and, therefore, that education may
help them try independent entrepreneurship.
Regarding other control variables, first, black
individuals are more likely to pursue entrepreneurship,
whereas individuals who were born outside the USA
are less likely to do so. Next, middle-aged individuals
are more likely to become entrepreneurs. The rela-
tionship between age and entrepreneurship is known
as an inverse U-shape (e.g., Lévesque and Minniti
2006; Kautonen et al. 2014). Here, too, we find an
inverse U-shaped relationship, as seen in Table 4:
Starting from ‘‘age 18–24’’ (the baseline is ‘‘age 55 or
higher’’ ), the highest absolute value of the coefficient
21 We also considered information on the presence of pre-
school children. However, it did not yield significant results. 22
This issue would be further pursued if a measure of voluntary
part-time work is available. We thank Kate Rybczynski for
pointing this out.
23 Rybczynski (2015), using Canadian data, arrives at a similar
conclusion, namely, that the number of children negatively
affects the continuation of women’s self-employment. See also
Okamuro and Ikeuchi (2012) for a study of the relationship
between women’s self-employment and work–life balance.
460 T. Adachi, T. Hisada
123
T a b le
5 E n tr e p re n e u rs h ip
(E q . 1 )—
su b sa m p le s:
m a rr ie d a n d u n m a rr ie d
M a rr ie d
U n m a rr ie d
S p e c ifi c a ti o n 1
S p e c ifi c a ti o n 3
S p e c ifi c a ti o n 5
S p e c ifi c a ti o n 1
S p e c ifi c a ti o n 3
S p e c ifi c a ti o n 5
D e p e n d e n t v a ri a b le :
1 =
S ta y in g in
0 =
G o in g o u t (E n tr e p re n e u rs h ip )
F e m a le
0 .0 1 2 * * *
(0 .0 0 4 )
0 .0 0 8
(0 .0 1 0 )
0 .0 4 9 * *
(0 .0 2 5 )
0 .0 2 3 * * *
(0 .0 0 6 )
0 .0 2 5 * *
(0 .0 1 3 )
0 .0 4 8 * *
(0 .0 2 1 )
F a m il y
C h il d re n u n d e r a g e 1 1
- 0 .0 0 7
(0 .0 0 5 )
- 0 .0 0 7
(0 .0 0 5 )
- 0 .0 0 6
(0 .0 0 5 )
- 0 .0 0 2
(0 .0 0 8 )
- 0 .0 0 3
(0 .0 0 8 )
- 0 .0 0 3
(0 .0 0 8 )
S iz e
- 0 .0 0 2
(0 .0 0 2 )
- 0 .0 0 2
(0 .0 0 2 )
- 0 .0 0 2
(0 .0 0 2 )
- 0 .0 0 2
(0 .0 0 2 )
- 0 .0 0 2
(0 .0 0 2 )
- 0 .0 0 2
(0 .0 0 2 )
E m p lo y m e n t
W o rk
st a tu s: R e f =
fu ll -t im
e
P a rt -t im
e - 0 .0 0 9
(0 .0 0 6 )
- 0 .0 2 1 * *
(0 .0 1 0 )
- 0 .0 0 9
(0 .0 0 6 )
- 0 .0 0 7
(0 .0 0 7 )
- 0 .0 1 1
(0 .0 0 9 )
- 0 .0 0 8
(0 .0 0 7 )
P a rt -t im
e 9
fe m a le
0 .0 1 8
(0 .0 1 2 )
0 .0 1 0
(0 .0 1 3 )
F ir m
si z e : R e f =
fi rm
si z e 1 0 0 – 9 9 9
F ir m
si z e 9 9 o r le ss
- 0 .0 0 4
(0 .0 0 6 )
- 0 .0 0 5
(0 .0 0 8 )
- 0 .0 0 4
(0 .0 0 6 )
- 0 .0 0 2
(0 .0 0 7 )
- 0 .0 0 2
(0 .0 0 9 )
- 0 .0 0 2
(0 .0 0 7 )
F ir m
si z e 1 0 0 0 o r m o re
- 0 .0 0 4
(0 .0 0 6 )
- 0 .0 0 4
(0 .0 0 8 )
- 0 .0 0 3
(0 .0 0 6 )
0 .0 0 2
(0 .0 0 7 )
0 .0 0 6
(0 .0 0 9 )
0 .0 0 3
(0 .0 0 7 )
F ir m
si z e 9 9 o r le ss
9 fe m a le
(� 1 0 � 1 )
0 .0 3 2
(0 .1 1 6 )
- 0 .0 0 4
(0 .1 4 3 )
F ir m
si z e 1 0 0 0 o r m o re
9 fe m a le
(� 1 0 � 1 )
0 .0 0 1
(0 .1 1 8 )
- 0 .0 9 4
(0 .1 5 0 )
R a c e : R e f =
w h it e
B la c k
- 0 .0 3 5 * * *
(0 .0 0 7 )
- 0 .0 3 5 * * *
(0 .0 0 7 )
- 0 .0 3 5 * * *
(0 .0 0 7 )
- 0 .0 1 5 *
(0 .0 0 8 )
- 0 .0 1 5 *
(0 .0 0 8 )
- 0 .0 1 5 *
(0 .0 0 8 )
H is p a n ic
- 0 .0 0 4
(0 .0 0 8 )
- 0 .0 0 4
(0 .0 0 8 )
- 0 .0 0 4
(0 .0 0 8 )
- 0 .0 0 4
(0 .0 0 9 )
- 0 .0 0 3
(0 .0 0 9 )
- 0 .0 0 3
(0 .0 0 9 )
Gender differences in entrepreneurship and intrapreneurship 461
123
T a b le
5 c o n ti n u e d
M a rr ie d
U n m a rr ie d
S p e c ifi c a ti o n 1
S p e c ifi c a ti o n 3
S p e c ifi c a ti o n 5
S p e c ifi c a ti o n 1
S p e c ifi c a ti o n 3
S p e c ifi c a ti o n 5
F o re ig n b o rn
0 .0 1 5 *
(0 .0 0 8 )
0 .0 1 5 *
(0 .0 0 8 )
0 .0 1 5 *
(0 .0 0 8 )
0 .0 1 3
(0 .0 0 9 )
0 .0 1 3
(0 .0 0 9 )
0 .0 1 3
(0 .0 0 9 )
A g e : R e f =
5 5 a n d m o re
1 8 – 2 4 (�
1 0 � 1 )
0 .0 5 0
(0 .1 2 4 )
0 .0 4 6
(0 .1 2 5 )
0 .0 4 5
(0 .1 2 4 )
- 0 .0 0 8
(0 .1 0 4 )
- 0 .0 0 6
(0 .1 0 4 )
- 0 .0 0 2
(0 .1 0 4 )
2 5 – 3 4
- 0 .0 1 5 *
(0 .0 0 8 )
- 0 .0 1 5 *
(0 .0 0 8 )
- 0 .0 1 5 *
(0 .0 0 8 )
- 0 .0 0 9
(0 .0 0 9 )
- 0 .0 0 9
(0 .0 0 9 )
- 0 .0 0 9
(0 .0 0 9 )
3 5 – 4 4
- 0 .0 1 4 *
(0 .0 0 7 )
- 0 .0 1 4 *
(0 .0 0 7 )
- 0 .0 1 4 *
(0 .0 0 7 )
- 0 .0 1 4
(0 .0 0 9 )
- 0 .0 1 4
(0 .0 0 9 )
- 0 .0 1 4
(0 .0 0 9 )
4 5 – 5 4
- 0 .0 1 4 * *
(0 .0 0 7 )
- 0 .0 1 5 * *
(0 .0 0 7 )
- 0 .0 1 4 * *
(0 .0 0 7 )
- 0 .0 0 5
(0 .0 0 9 )
- 0 .0 0 6
(0 .0 0 9 )
- 0 .0 0 5
(0 .0 0 9 )
E d u c a ti o n : R e f =
H S d ro p o u t
H S g ra d u a te
0 .0 1 9 *
(0 .0 1 0 )
0 .0 1 8 *
(0 .0 1 0 )
0 .0 3 1 * * *
(0 .0 1 2 )
0 .0 2 8 * * *
(0 .0 1 0 )
0 .0 2 9 * * *
(0 .0 1 0 )
0 .0 3 5 * * *
(0 .0 1 2 )
S o m e c o ll e g e
0 .0 0 5
(0 .0 1 0 )
0 .0 0 4
(0 .0 1 0 )
0 .0 1 1
(0 .0 1 1 )
0 .0 1 3
(0 .0 1 0 )
0 .0 1 3
(0 .0 1 0 )
0 .0 1 7
(0 .0 1 2 )
B a c h e lo r
0 .0 2 3 * *
(0 .0 1 0 )
0 .0 2 2 * *
(0 .0 1 0 )
0 .0 3 1 * * *
(0 .0 1 2 )
0 .0 0 7
(0 .0 1 1 )
0 .0 0 7
(0 .0 1 1 )
0 .0 1 2
(0 .0 1 3 )
P o st g ra d u a te
0 .0 1 4
(0 .0 1 0 )
0 .0 1 4
(0 .0 1 0 )
0 .0 1 8
(0 .0 1 2 )
0 .0 2 2 *
(0 .0 1 3 )
0 .0 2 2 *
(0 .0 1 3 )
0 .0 4 4 * *
(0 .0 1 8 )
H S g ra d u a te
9 fe m a le
- 0 .0 4 9 *
(0 .0 2 6 )
- 0 .0 2 8
(0 .0 2 4 )
S o m e c o ll e g e 9
fe m a le
- 0 .0 3 5
(0 .0 2 5 )
- 0 .0 2 1
(0 .0 2 3 )
B a c h e lo r 9
fe m a le
- 0 .0 4 1
(0 .0 2 6 )
- 0 .0 2 2
(0 .0 2 3 )
P o st g ra d u a te
9 fe m a le
- 0 .0 2 8
(0 .0 2 7 )
- 0 .0 5 5 * *
(0 .0 2 7 )
In te rn e t
- 0 .0 2 1 * * *
(0 .0 0 7 )
- 0 .0 2 1 * * *
(0 .0 0 7 )
- 0 .0 2 1 * * *
(0 .0 0 7 )
- 0 .0 2 2 * * *
(0 .0 0 8 )
- 0 .0 2 2 * * *
(0 .0 0 8 )
- 0 .0 2 2 * * *
(0 .0 0 8 )
N o n -m
e tr o a re a
- 0 .0 0 3
(0 .0 0 5 )
- 0 .0 0 3
(0 .0 0 5 )
- 0 .0 0 3
(0 .0 0 5 )
0 .0 0 3
(0 .0 0 6 )
0 .0 0 3
(0 .0 0 6 )
0 .0 0 3
(0 .0 0 6 )
462 T. Adachi, T. Hisada
123
T a b le
5 c o n ti n u e d
M a rr ie d
U n m a rr ie d
S p e c ifi c a ti o n 1
S p e c ifi c a ti o n 3
S p e c ifi c a ti o n 5
S p e c ifi c a ti o n 1
S p e c ifi c a ti o n 3
S p e c ifi c a ti o n 5
U n e m p lo y m e n t ra te
0 .0 0 1
(0 .0 0 2 )
0 .0 0 1
(0 .0 0 2 )
0 .0 0 1
(0 .0 0 3 )
- 0 .0 0 1
(0 .0 0 4 )
- 0 .0 0 1
(0 .0 0 4 )
- 0 .0 0 1
(0 .0 0 4 )
H o m e st e a d e x e m p ti o n (�
1 0 � 3 )
- 0 .0 2 4 * *
(0 .0 1 2 )
- 0 .0 2 4 * *
(0 .0 1 2 )
- 0 .0 2 4 * *
(0 .0 1 2 )
- 0 .0 0 2
(0 .0 1 6 )
- 0 .0 0 3
(0 .0 1 6 )
- 0 .0 0 1
(0 .0 1 6 )
M e d ia n h o m e v a lu e (�
1 0 � 2 )
- 0 .0 0 4 *
(0 .0 0 2 )
- 0 .0 0 4 *
(0 .0 0 2 )
- 0 .0 0 4 *
(0 .0 0 2 )
- 0 .0 0 1
(0 .0 0 3 )
- 0 .0 0 1
(0 .0 0 3 )
- 0 .0 0 1
(0 .0 0 3 )
M a x im
u m
p e rs o n a l in c o m e ta x ra te
0 .0 0 1
(0 .0 0 1 )
0 .0 0 1
(0 .0 0 1 )
0 .0 0 1
(0 .0 0 1 )
0 .0 0 1
(0 .0 0 1 )
0 .0 0 1
(0 .0 0 1 )
0 .0 0 1
(0 .0 0 1 )
M a x im
u m
c o rp o ra te
in c o m e ta x ra te
0 .0 0 1
(0 .0 0 1 )
0 .0 0 1
(0 .0 0 1 )
0 .0 0 1
(0 .0 0 1 )
- 0 .0 0 1
(0 .0 0 1 )
- 0 .0 0 1
(0 .0 0 1 )
- 0 .0 0 1
(0 .0 0 1 )
S a le s ta x ra te
0 .0 0 2
(0 .0 0 2 )
0 .0 0 3
(0 .0 0 2 )
0 .0 0 3
(0 .0 0 2 )
0 .0 0 1
(0 .0 0 2 )
0 .0 0 1
(0 .0 0 2 )
0 .0 0 1
(0 .0 0 2 )
N 6 8 3 5
6 8 3 5
6 8 3 5
4 2 7 8
4 2 7 8
4 2 7 8
A v e ra g e (f o r d is c re te
v a ri a b le s)
a n d m a rg in a l (f o r c o n ti n u o u s v a ri a b le s)
e ff e c ts
R o b u st
st a n d a rd
e rr o rs
a re
in p a re n th e se s
* p \
0 :1 ; * * p \
0 :0 5 ; * * * p \
0 :0 1
Gender differences in entrepreneurship and intrapreneurship 463
123
T a b le
6 E n tr e p re n e u rs h ip
(E q . 1 )—
su b sa m p le s:
fu ll -t im
e a n d p a rt -t im
e
F u ll -t im
e P a rt -t im
e
S p e c ifi c a ti o n 1
S p e c ifi c a ti o n 2
S p e c ifi c a ti o n 5
S p e c ifi c a ti o n 1
S p e c ifi c a ti o n 2
S p e c ifi c a ti o n 5
D e p e n d e n t v a ri a b le
1 =
S ta y in g in
0 =
G o in g o u t (e n tr e p re n e u rs h ip )
F e m a le
0 .0 1 4 * * *
(0 .0 0 4 )
0 .0 2 1 * *
(0 .0 0 9 )
0 .0 4 6 * *
(0 .0 1 9 )
0 .0 3 2 * * *
(0 .0 0 9 )
0 .0 5 1 * *
(0 .0 2 0 )
0 .0 4 6
(0 .0 2 9 )
F a m il y
M a rr ie d
0 .0 0 5
(0 .0 0 4 )
0 .0 1 0 *
(0 .0 0 5 )
0 .0 0 6
(0 .0 0 4 )
0 .0 0 8
(0 .0 0 9 )
0 .0 1 3
(0 .0 1 3 )
0 .0 0 9
(0 .0 0 9 )
C h il d re n u n d e r a g e 1 1
- 0 .0 0 3
(0 .0 0 4 )
- 0 .0 0 1
(0 .0 0 6 )
- 0 .0 0 2
(0 .0 0 4 )
- 0 .0 0 7
(0 .0 1 3 )
- 0 .0 3 1 *
(0 .0 1 7 )
- 0 .0 0 6
(0 .0 1 2 )
S iz e
- 0 .0 0 3 *
(0 .0 0 2 )
- 0 .0 0 3
(0 .0 0 2 )
- 0 .0 0 3 *
(0 .0 0 2 )
- 0 .0 0 0
(0 .0 0 3 )
0 .0 0 4
(0 .0 0 4 )
- 0 .0 0 1
(0 .0 0 3 )
M a rr ie d 9
fe m a le
- 0 .0 1 1
(0 .0 0 8 )
- 0 .0 1 0
(0 .0 1 7 )
C h il d re n u n d e r a g e 1 1 9
fe m a le
- 0 .0 0 7
(0 .0 0 9 )
0 .0 4 1 *
(0 .0 2 3 )
S iz e 9
fe m a le
0 .0 0 1
(0 .0 0 3 )
- 0 .0 0 9
(0 .0 0 7 )
E m p lo y m e n t
F ir m
si z e : R e f =
fi rm
si z e 1 0 0 – 9 9 9
F ir m
si z e 9 9 o r le ss
- 0 .0 0 2
(0 .0 0 5 )
- 0 .0 0 3
(0 .0 0 5 )
- 0 .0 0 2
(0 .0 0 5 )
- 0 .0 1 1
(0 .0 1 3 )
- 0 .0 1 1
(0 .0 1 3 )
- 0 .0 1 0
(0 .0 1 3 )
F ir m
si z e 1 0 0 0 o r m o re
0 .0 0 2
(0 .0 0 5 )
0 .0 0 2
(0 .0 0 5 )
0 .0 0 3
(0 .0 0 5 )
- 0 .0 2 2 *
(0 .0 1 3 )
- 0 .0 2 1
(0 .0 1 3 )
- 0 .0 2 1 *
(0 .0 1 3 )
R a c e : R e f =
w h it e
B la c k
- 0 .0 2 9 * * *
(0 .0 0 5 )
- 0 .0 3 0 * * *
(0 .0 0 5 )
- 0 .0 2 9 * * *
(0 .0 0 5 )
0 .0 2 1
(0 .0 2 0 )
0 .0 2 3
(0 .0 2 0 )
0 .0 2 1
(0 .0 2 0 )
H is p a n ic
- 0 .0 0 6
(0 .0 0 6 )
- 0 .0 0 6
(0 .0 0 6 )
- 0 .0 0 6
(0 .0 0 6 )
0 .0 0 3
(0 .0 1 4 )
0 .0 0 4
(0 .0 1 4 )
0 .0 0 3
(0 .0 1 4 )
F o re ig n b o rn
0 .0 1 5 * *
(0 .0 0 6 )
0 .0 1 5 * *
(0 .0 0 6 )
0 .0 1 5 * *
(0 .0 0 6 )
0 .0 0 9
(0 .0 1 5 )
0 .0 1 0
(0 .0 1 5 )
0 .0 0 9
(0 .0 1 4 )
464 T. Adachi, T. Hisada
123
T a b le
6 c o n ti n u e d
F u ll -t im
e P a rt -t im
e
S p e c ifi c a ti o n 1
S p e c ifi c a ti o n 2
S p e c ifi c a ti o n 5
S p e c ifi c a ti o n 1
S p e c ifi c a ti o n 2
S p e c ifi c a ti o n 5
A g e : R e f =
5 5 a n d m o re
1 8 – 2 4
- 0 .0 1 3
(0 .0 0 8 )
- 0 .0 1 2
(0 .0 0 8 )
- 0 .0 1 3
(0 .0 0 8 )
0 .0 1 7
(0 .0 1 5 )
0 .0 1 5
(0 .0 1 5 )
0 .0 1 7
(0 .0 1 6 )
2 5 – 3 4
- 0 .0 1 4 * *
(0 .0 0 7 )
- 0 .0 1 3 *
(0 .0 0 7 )
- 0 .0 1 4 * *
(0 .0 0 7 )
- 0 .0 1 8
(0 .0 1 5 )
- 0 .0 1 9
(0 .0 1 5 )
- 0 .0 1 8
(0 .0 1 5 )
3 5 – 4 4
- 0 .0 1 7 * * *
(0 .0 0 6 )
- 0 .0 1 7 * * *
(0 .0 0 6 )
- 0 .0 1 8 * * *
(0 .0 0 6 )
- 0 .0 0 1
(0 .0 1 5 )
- 0 .0 0 2
(0 .0 1 6 )
- 0 .0 0 2
(0 .0 1 5 )
4 5 – 5 4
- 0 .0 1 3 * *
(0 .0 0 6 )
- 0 .0 1 3 * *
(0 .0 0 6 )
- 0 .0 1 3 * *
(0 .0 0 6 )
- 0 .0 1 5
(0 .0 1 2 )
- 0 .0 1 3
(0 .0 1 2 )
- 0 .0 1 5
(0 .0 1 2 )
E d u c a ti o n : R e f =
H S d ro p o u t
H S g ra d u a te
0 .0 2 5 * * *
(0 .0 0 8 )
0 .0 2 5 * * *
(0 .0 0 8 )
0 .0 3 4 * * *
(0 .0 0 9 )
0 .0 2 1
(0 .0 1 6 )
0 .0 2 1
(0 .0 1 6 )
0 .0 2 9
(0 .0 2 0 )
S o m e c o ll e g e
0 .0 1 0
(0 .0 0 8 )
0 .0 1 0
(0 .0 0 8 )
0 .0 1 6 *
(0 .0 0 9 )
0 .0 0 4
(0 .0 1 5 )
0 .0 0 2
(0 .0 1 5 )
0 .0 0 7
(0 .0 1 8 )
B a c h e lo r
0 .0 2 0 * *
(0 .0 0 8 )
0 .0 2 0 * *
(0 .0 0 8 )
0 .0 2 8 * * *
(0 .0 0 9 )
0 .0 1 2
(0 .0 1 7 )
0 .0 1 1
(0 .0 1 7 )
0 .0 0 8
(0 .0 2 1 )
P o st g ra d u a te
0 .0 2 2 * *
(0 .0 0 9 )
0 .0 2 2 * *
(0 .0 0 9 )
0 .0 2 8 * * *
(0 .0 1 0 )
- 0 .0 0 1
(0 .0 1 8 )
- 0 .0 0 3
(0 .0 1 8 )
0 .0 1 1
(0 .0 2 4 )
H S g ra d u a te
9 fe m a le
- 0 .0 4 1 * *
(0 .0 2 0 )
- 0 .0 2 3
(0 .0 3 4 )
S o m e c o ll e g e 9
fe m a le
- 0 .0 2 9
(0 .0 2 0 )
- 0 .0 1 4
(0 .0 3 1 )
B a c h e lo r 9
fe m a le (�
1 0 � 1 )
- 0 .0 3 6 *
(0 .0 2 0 )
0 .0 0 1
(0 .3 4 0 )
P o st g ra d u a te
9 fe m a le
- 0 .3 6 2
(0 .1 9 8 )
- 0 .0 2 8
(0 .0 3 5 )
In te rn e t
- 0 .0 2 7 * * *
(0 .0 0 6 )
- 0 .0 2 7 * * *
(0 .0 0 6 )
- 0 .0 2 7 * * *
(0 .0 0 6 )
- 0 .0 0 7
(0 .0 1 2 )
- 0 .0 0 6
(0 .0 1 2 )
- 0 .0 0 7
(0 .0 1 1 )
N o n -m
e tr o a re a
0 .0 0 1
(0 .0 0 4 )
0 .0 0 1
(0 .0 0 4 )
0 .0 0 1
(0 .0 0 4 )
- 0 .0 1 1
(0 .0 0 9 )
- 0 .0 1 1
(0 .0 0 9 )
- 0 .0 1 2
(0 .0 0 9 )
U n e m p lo y m e n t ra te (�
1 0 � 1 )
- 0 .0 0 5
(0 .0 2 2 )
- 0 .0 0 5
(0 .0 2 2 )
- 0 .0 0 6
(0 .0 2 2 )
0 .0 3 1
(0 .0 5 8 )
0 .0 3 3
(0 .0 5 6 )
0 .0 2 7
(0 .0 5 8 )
Gender differences in entrepreneurship and intrapreneurship 465
123
T a b le
6 c o n ti n u e d
F u ll -t im
e P a rt -t im
e
S p e c ifi c a ti o n 1
S p e c ifi c a ti o n 2
S p e c ifi c a ti o n 5
S p e c ifi c a ti o n 1
S p e c ifi c a ti o n 2
S p e c ifi c a ti o n 5
H o m e st e a d e x e m p ti o n (�
1 0 � 2 )
- 0 .0 0 2
(0 .0 0 1 )
- 0 .0 0 2
(0 .0 0 1 )
- 0 .0 0 2
(0 .0 0 1 )
- 0 .0 0 1
(0 .0 0 2 )
- 0 .0 0 1
(0 .0 0 2 )
- 0 .0 0 1
(0 .0 0 2 )
M e d ia n h o m e v a lu e (�
1 0 � 2 )
- 0 .0 0 2
(0 .0 0 2 )
- 0 .0 0 2
(0 .0 0 2 )
- 0 .0 0 2
(0 .0 0 2 )
- 0 .0 0 7
(0 .0 0 4 )
- 0 .0 0 6
(0 .0 0 4 )
- 0 .0 0 7
(0 .0 0 4 )
M a x im
u m
p e rs o n a l in c o m e ta x ra te (�
1 0 � 1 )
0 .0 0 9
(0 .0 0 9 )
0 .0 0 9
(0 .0 0 9 )
0 .0 0 9
(0 .0 0 9 )
0 .0 0 4
(0 .0 2 1 )
0 .0 0 1
(0 .0 2 1 )
0 .0 0 4
(0 .0 2 1 )
M a x im
u m
c o rp o ra te
in c o m e ta x ra te (�
1 0 � 1 )
0 .0 0 3
(0 .0 0 8 )
0 .0 0 3
(0 .0 0 8 )
0 .0 0 3
(0 .0 0 8 )
0 .0 0 4
(0 .0 1 8 )
0 .0 0 6
(0 .0 1 8 )
0 .0 0 4
(0 .0 1 8 )
S a le s ta x ra te
0 .0 0 2
(0 .0 0 1 )
0 .0 0 2
(0 .0 0 1 )
0 .0 0 2
(0 .0 0 1 )
0 .0 0 2
(0 .0 0 3 )
0 .0 0 2
(0 .0 0 3 )
0 .0 0 2
(0 .0 0 3 )
N 9 3 3 1
9 3 3 1
9 3 3 1
1 7 8 2
1 7 8 2
1 7 8 2
A v e ra g e (f o r d is c re te
v a ri a b le s)
a n d m a rg in a l (f o r c o n ti n u o u s v a ri a b le s)
e ff e c ts
R o b u st
st a n d a rd
e rr o rs
a re
in p a re n th e se s
* p \
0 :1 ; * * p \
0 :0 5 ; * * * p \
0 :0 1
466 T. Adachi, T. Hisada
123
T a b le
7 In tr a p re n e u rs h ip
(E q . 2 )
S p e c ifi c a ti o n 1
S p e c ifi c a ti o n 2
S p e c ifi c a ti o n 3
S p e c ifi c a ti o n 4
S p e c ifi c a ti o n 5
D e p e n d e n t v a ri a b le
1 =
In tr a p re n e u rs h ip
0 =
E ls e
F e m a le
- 0 .0 2 3 * * *
(0 .0 0 3 )
- 0 .0 2 1 * * *
(0 .0 0 8 )
- 0 .0 3 4
(0 .0 2 4 )
- 0 .0 3 3
(0 .0 2 8 )
- 0 .0 3 0 * *
(0 .0 1 4 )
F a m il y
M a rr ie d
- 0 .0 0 6
(0 .0 0 4 )
- 0 .0 0 6
(0 .0 0 5 )
- 0 .0 0 9
(0 .0 0 8 )
- 0 .0 1 0
(0 .0 1 1 )
- 0 .0 0 6
(0 .0 0 4 )
C h il d re n u n d e r a g e 1 1
0 .0 0 4
(0 .0 0 4 )
0 .0 0 5
(0 .0 0 5 )
0 .0 0 5
(0 .0 0 7 )
0 .0 0 7
(0 .0 0 8 )
0 .0 0 4
(0 .0 0 4 )
S iz e (�
1 0 � 1 )
0 .0 0 5
(0 .0 1 3 )
0 .0 0 4
(0 .0 1 6 )
0 .0 1 2
(0 .0 2 4 )
0 .0 1 2
(0 .0 1 2 )
0 .0 0 6
(0 .0 1 3 )
M a rr ie d 9
fe m a le
- 0 .0 0 1
(0 .0 0 7 )
0 .0 0 3
(0 .0 1 2 )
C h il d re n u n d e r a g e 1 1 9
fe m a le
- 0 .0 0 5
(0 .0 0 8 )
- 0 .0 0 5
(0 .0 1 0 )
S iz e 9
fe m a le
(� 1 0 � 2 )
0 .0 4 0
(0 .2 7 3 )
- 0 .0 0 2
(0 .3 3 5 )
E m p lo y m e n t
W o rk
st a tu s: R e f =
fu ll -t im
e
P a rt -t im
e - 0 .0 0 9 *
(0 .0 0 5 )
- 0 .0 0 9 *
(0 .0 0 5 )
- 0 .0 1 2
(0 .0 1 4 )
- 0 .0 1 3
(0 .0 1 2 )
- 0 .0 0 9 *
(0 .0 0 5 )
P a rt -t im
e 9
fe m a le
0 .0 0 9
(0 .0 1 5 )
0 .0 0 9
(0 .0 1 3 )
F ir m
si z e : R e f =
fi rm
si z e 1 0 0 – 9 9 9
F ir m
si z e 9 9 o r le ss
0 .0 0 3
(0 .0 0 4 )
0 .0 0 3
(0 .0 0 4 )
0 .0 0 4
(0 .0 0 8 )
0 .0 0 4
(0 .0 0 8 )
0 .0 0 3
(0 .0 0 4 )
F ir m
si z e 1 0 0 0 o r m o re
(� 1 0 � 2 )
0 .0 1 7
(0 .4 4 9 )
0 .0 1 0
(0 .4 4 9 )
- 0 .0 1 3
(0 .6 8 9 )
- 0 .0 0 1
(0 .6 8 1 )
0 .0 2 8
(0 .4 4 8 )
F ir m
si z e 9 9 o r le ss
9 fe m a le
0 .0 0 1
(0 .0 1 1 )
0 .0 0 1
(0 .0 1 1 )
F ir m
si z e 1 0 0 0 o r m o re
9 fe m a le
0 .0 0 1
(0 .0 1 2 )
0 .0 0 1
(0 .0 1 2 )
Gender differences in entrepreneurship and intrapreneurship 467
123
T a b le
7 c o n ti n u e d
S p e c ifi c a ti o n 1
S p e c ifi c a ti o n 2
S p e c ifi c a ti o n 3
S p e c ifi c a ti o n 4
S p e c ifi c a ti o n 5
R a c e : R e f =
w h it e
B la c k
0 .0 1 4 * * *
(0 .0 0 5 )
0 .0 1 4 * * *
(0 .0 0 5 )
0 .0 2 5
(0 .0 2 9 )
0 .0 2 5
(0 .0 2 5 )
0 .0 1 4 * * *
(0 .0 0 5 )
H is p a n ic
0 .0 0 9 *
(0 .0 0 5 )
0 .0 0 9 *
(0 .0 0 5 )
0 .0 1 3
(0 .0 1 1 )
0 .0 1 3
(0 .0 1 0 )
0 .0 0 9 *
(0 .0 0 5 )
F o re ig n b o rn
0 .0 0 5
(0 .0 0 5 )
0 .0 0 5
(0 .0 0 5 )
0 .0 0 2
(0 .0 1 2 )
0 .0 0 3
(0 .0 1 1 )
0 .0 0 5
(0 .0 0 5 )
A g e : R e f =
5 5 a n d m o re
1 8 – 2 4
0 .0 2 4 * * *
(0 .0 0 7 )
0 .0 2 4 * * *
(0 .0 0 7 )
0 .0 3 0 * *
(0 .0 1 2 )
0 .0 2 9 * * *
(0 .0 1 1 )
0 .0 2 4 * * *
(0 .0 0 7 )
2 5 – 3 4
0 .0 1 6 * * *
(0 .0 0 6 )
0 .0 1 6 * * *
(0 .0 0 6 )
0 .0 2 3
(0 .0 1 9 )
0 .0 2 2
(0 .0 1 6 )
0 .0 1 6 * * *
(0 .0 0 6 )
3 5 – 4 4
0 .0 0 9
(0 .0 0 6 )
0 .0 0 9
(0 .0 0 6 )
0 .0 1 4
(0 .0 1 7 )
0 .0 1 4
(0 .0 1 5 )
0 .0 0 8
(0 .0 0 6 )
4 5 – 5 4
0 .0 0 9 *
(0 .0 0 5 )
0 .0 0 9 *
(0 .0 0 5 )
0 .0 1 4
(0 .0 1 5 )
0 .0 1 3
(0 .0 1 3 )
0 .0 0 9 *
(0 .0 0 5 )
E d u c a ti o n : R e f =
H S d ro p o u t
H S g ra d u a te
- 0 .0 0 8
(0 .0 0 7 )
- 0 .0 0 8
(0 .0 0 7 )
- 0 .0 1 7
(0 .0 2 5 )
- 0 .0 1 6
(0 .0 2 2 )
- 0 .0 0 9
(0 .0 0 8 )
S o m e c o ll e g e
- 0 .0 0 7
(0 .0 0 7 )
- 0 .0 0 7
(0 .0 0 7 )
- 0 .0 1 1
(0 .0 1 3 )
- 0 .0 1 0
(0 .0 1 2 )
- 0 .0 0 8
(0 .0 0 8 )
B a c h e lo r
- 0 .0 0 8
(0 .0 0 7 )
- 0 .0 0 8
(0 .0 0 7 )
- 0 .0 1 4
(0 .0 2 1 )
- 0 .0 1 4
(0 .0 1 8 )
- 0 .0 1 1
(0 .0 0 8 )
P o st g ra d u a te
0 .0 0 1
(0 .0 0 8 )
0 .0 0 1
(0 .0 0 7 )
- 0 .0 0 4
(0 .0 1 9 )
- 0 .0 0 4
(0 .0 1 6 )
- 0 .0 0 2
(0 .0 0 9 )
H S g ra d u a te
9 fe m a le
0 .0 0 3
(0 .0 1 6 )
S o m e c o ll e g e 9
fe m a le
0 .0 0 5
(0 .0 1 6 )
B a c h e lo r 9
fe m a le
0 .0 1 3
(0 .0 1 6 )
P o st g ra d u a te
9 fe m a le
0 .0 1 0
(0 .0 1 6 )
468 T. Adachi, T. Hisada
123
is achieved at ‘‘age 35–44,’’ and a lower value is
observed for ‘‘age 45–54’’ in each specification. The
effect of Internet use works positively for
entrepreneurship in all the specifications, with 1 %
statistical significance. This finding is consistent with
Fairlie (2006), who argues that computer use is
positively related to entrepreneurship not only for
those who work in the IT industry, but also for others
in general. The effects of homestead exemption and
median home value are negative, with 10 % statistical
significance. However, unemployment rate and tax
rates have no significant effects.
Table 5 shows the estimated average/marginal
effects in Eq. (1) from subsamples of married and
unmarried individuals. In each specification, the
estimated negative effect of being a woman in
entrepreneurship is weaker for married individuals
than for unmarried individuals. Thus, marriage
encourages more women to choose entrepreneurship.
Interestingly, for unmarried women, working for a
small or large firm has a positive effect on independent
entrepreneurship, whereas the opposite is true for
married women (see the results from Specification 3).
Moreover, in all of the specifications, the effect of
being an unmarried part-time employee is no longer
statistically significant. Unmarried part-time employ-
ees are as likely to become an entrepreneur as full-time
employees are. Now, Table 6 divides the sample into
full-time and part-time workers. Except for Specifi-
cation 5, the estimated negative effect of being a
woman on entrepreneurship is weaker for full-time
employees than for part-time employees. Somewhat
unexpectedly, for part-time workers, the interaction of
being as a woman and the presence of children has a
negative effect on entrepreneurship, while it is positive
for full-time workers. This is presumably because
part-time female workers with children are not so
attracted to entrepreneurship because they already
have time flexibility, while full-time female workers
are inclined toward entrepreneurship if they have a
child.
4.1.2 Selection of intrapreneurship (Eq. 2)
Next, the estimates for average (for discrete variables)
and marginal (for continuous variables) effects of
Eq. (2) are presented in Table 7. Here, positive signs
of coefficients mean positive effects on intrapreneur-
ship and vice versa.T a b le
7 c o n ti n u e d
S p e c ifi c a ti o n 1
S p e c ifi c a ti o n 2
S p e c ifi c a ti o n 3
S p e c ifi c a ti o n 4
S p e c ifi c a ti o n 5
In te rn e t
0 .0 1 0 * *
(0 .0 0 5 )
0 .0 1 0 * *
(0 .0 0 5 )
0 .0 1 8
(0 .0 2 4 )
0 .0 1 8
(0 .0 2 1 )
0 .0 1 0 * *
(0 .0 0 5 )
N 1 1 ,1 1 3
1 1 ,1 1 3
1 1 ,1 1 3
1 1 ,1 1 3
1 1 ,1 1 3
A v e ra g e (f o r d is c re te
v a ri a b le s)
a n d m a rg in a l (f o r c o n ti n u o u s v a ri a b le s)
e ff e c ts
R o b u st
st a n d a rd
e rr o rs
a re
in p a re n th e se s
* p \
0 :1 ; * * p \
0 :0 5 ; * * * p \
0 :0 1
Gender differences in entrepreneurship and intrapreneurship 469
123
T a b le
8 In tr a p re n e u rs h ip
(E q . 2 )—
su b sa m p le s:
m a rr ie d a n d u n m a rr ie d
M a rr ie d
U n m a rr ie d
S p e c ifi c a ti o n 1
S p e c ifi c a ti o n 3
S p e c ifi c a ti o n 5
S p e c ifi c a ti o n 1
S p e c ifi c a ti o n 3
S p e c ifi c a ti o n 5
D e p e n d e n t v a ri a b le
1 =
In tr a p re n e u rs h ip
0 =
E ls e
F e m a le
- 0 .0 3 2 * * *
(0 .0 0 8 )
- 0 .0 3 9 * * *
(0 .0 1 3 )
- 0 .0 7 2 * *
(0 .0 3 6 )
- 0 .0 2 5 * * *
(0 .0 0 6 )
- 0 .0 1 6
(0 .0 1 4 )
- 0 .0 2 5
(0 .0 2 0 )
F a m il y
C h il d re n u n d e r a g e 1 1
0 .0 0 2
(0 .0 0 7 )
0 .0 0 2
(0 .0 0 7 )
0 .0 0 1
(0 .0 0 7 )
0 .0 1 1
(0 .0 0 8 )
0 .0 1 1
(0 .0 0 8 )
0 .0 1 2
(0 .0 0 8 )
S iz e
0 .0 0 1
(0 .0 0 3 )
0 .0 0 1
(0 .0 0 3 )
0 .0 0 1
(0 .0 0 3 )
0 .0 0 1
(0 .0 0 2 )
0 .0 0 1
(0 .0 0 2 )
0 .0 0 2
(0 .0 0 2 )
E m p lo y m e n t
W o rk
st a tu s: R e f =
fu ll -t im
e
P a rt -t im
e - 0 .0 1 1
(0 .0 1 0 )
- 0 .0 1 5
(0 .0 1 9 )
- 0 .0 1 1
(0 .0 1 0 )
- 0 .0 0 7
(0 .0 0 8 )
- 0 .0 1 4
(0 .0 1 0 )
- 0 .0 0 7
(0 .0 0 8 )
P a rt -t im
e 9
fe m a le
0 .0 0 5
(0 .0 2 1 )
0 .0 1 7
(0 .0 1 4 )
F ir m
si z e : R e f =
fi rm
si z e 1 0 0 – 9 9 9
F ir m
si z e 9 9 o r le ss
- 0 .0 0 2
(0 .0 0 7 )
- 0 .0 0 5
(0 .0 0 8 )
- 0 .0 0 3
(0 .0 0 7 )
0 .0 1 5 *
(0 .0 0 8 )
0 .0 2 0 *
(0 .0 1 0 )
0 .0 1 5 *
(0 .0 0 8 )
F ir m
si z e 1 0 0 0 o r m o re
- 0 .0 0 6
(0 .0 0 7 )
- 0 .0 1 0
(0 .0 0 8 )
- 0 .0 0 7
(0 .0 0 7 )
0 .0 1 2
(0 .0 0 8 )
0 .0 1 9 *
(0 .0 1 1 )
0 .0 1 2
(0 .0 0 8 )
F ir m
si z e 9 9 o r le ss
9 fe m a le
0 .0 0 8
(0 .0 1 4 )
- 0 .0 1 3
(0 .0 1 6 )
F ir m
si z e 1 0 0 0 o r m o re
9 fe m a le
(0 .0 1 5 )
0 .0 1 1
(0 .0 1 7 )
- 0 .0 1 7
R a c e : R e f =
w h it e
B la c k
0 .0 2 4
(0 .0 1 9 )
0 .0 2 4
(0 .0 1 9 )
0 .0 2 2
(0 .0 1 9 )
0 .0 2 0 * * *
(0 .0 0 8 )
0 .0 2 1 * * *
(0 .0 0 8 )
0 .0 2 0 * * *
(0 .0 0 8 )
H is p a n ic
0 .0 0 7
(0 .0 1 0 )
0 .0 0 6
(0 .0 1 0 )
0 .0 0 6
(0 .0 1 0 )
0 .0 1 5 *
(0 .0 0 8 )
0 .0 1 6 *
(0 .0 0 8 )
0 .0 1 5 *
(0 .0 0 8 )
470 T. Adachi, T. Hisada
123
T a b le
8 c o n ti n u e d
M a rr ie d
U n m a rr ie d
S p e c ifi c a ti o n 1
S p e c ifi c a ti o n 3
S p e c ifi c a ti o n 5
S p e c ifi c a ti o n 1
S p e c ifi c a ti o n 3
S p e c ifi c a ti o n 5
F o re ig n b o rn
0 .0 0 4
(0 .0 1 0 )
0 .0 0 4
(0 .0 1 0 )
0 .0 0 4
(0 .0 0 9 )
0 .0 0 2
(0 .0 0 8 )
0 .0 0 2
(0 .0 0 8 )
0 .0 0 2
(0 .0 0 8 )
A g e : R e f =
5 5 a n d m o re
1 8 – 2 4
0 .0 3 1 * *
(0 .0 1 4 )
0 .0 3 1 * *
(0 .0 1 4 )
0 .0 3 0 * *
(0 .0 1 4 )
0 .0 1 8 *
(0 .0 1 0 )
0 .0 1 8 *
(0 .0 1 0 )
0 .0 1 6
(0 .0 1 0 )
2 5 – 3 4
0 .0 3 5 * * *
(0 .0 1 2 )
0 .0 3 5 * * *
(0 .0 1 2 )
0 .0 3 4 * * *
(0 .0 1 2 )
0 .0 0 2
(0 .0 1 0 )
0 .0 0 2
(0 .0 1 0 )
0 .0 0 1
(0 .0 1 0 )
3 5 – 4 4
0 .0 1 5
(0 .0 1 1 )
0 .0 1 5
(0 .0 1 1 )
0 .0 1 5
(0 .0 1 1 )
0 .0 1 2
(0 .0 1 0 )
0 .0 1 2
(0 .0 1 0 )
0 .0 1 2
(0 .0 1 0 )
4 5 – 5 4
0 .0 2 0 * *
(0 .0 1 0 )
0 .0 2 0 *
(0 .0 1 0 )
0 .0 1 9 *
(0 .0 1 0 )
0 .0 0 3
(0 .0 0 9 )
0 .0 0 3
(0 .0 0 9 )
0 .0 0 2
(0 .0 0 9 )
E d u c a ti o n : R e f =
H S d ro p o u t
H S g ra d u a te
- 0 .0 2 1
(0 .0 1 4 )
- 0 .0 2 1
(0 .0 1 4 )
- 0 .0 3 0
(0 .0 1 9 )
- 0 .0 0 6
(0 .0 1 0 )
- 0 .0 0 6
(0 .0 1 0 )
- 0 .0 0 3
(0 .0 1 2 )
S o m e c o ll e g e
- 0 .0 1 3
(0 .0 1 2 )
- 0 .0 1 3
(0 .0 1 2 )
- 0 .0 1 7
(0 .0 1 4 )
- 0 .0 0 2
(0 .0 1 1 )
- 0 .0 0 2
(0 .0 1 1 )
- 0 .0 0 5
(0 .0 1 3 )
B a c h e lo r
- 0 .0 1 9
(0 .0 1 5 )
- 0 .0 1 9
(0 .0 1 5 )
- 0 .0 3 3 *
(0 .0 2 0 )
- 0 .0 0 8
(0 .0 1 2 )
- 0 .0 0 8
(0 .0 1 2 )
- 0 .0 0 3
(0 .0 1 4 )
P o st g ra d u a te
- 0 .0 0 5
(0 .0 1 3 )
- 0 .0 0 5
(0 .0 1 3 )
- 0 .0 0 9
(0 .0 1 5 )
- 0 .0 0 2
(0 .0 1 3 )
- 0 .0 0 1
(0 .0 1 3 )
- 0 .0 2 2
(0 .0 1 8 )
H S g ra d u a te
9 fe m a le
0 .0 4 6
(0 .0 3 7 )
- 0 .0 1 1
(0 .0 2 3 )
S o m e c o ll e g e 9
fe m a le
0 .0 2 7
(0 .0 3 4 )
0 .0 0 6
(0 .0 2 2 )
B a c h e lo r 9
fe m a le
0 .0 6 0 *
(0 .0 3 5 )
- 0 .0 1 4
(0 .0 2 4 )
P o st g ra d u a te
9 fe m a le
0 .0 2 9
(0 .0 3 3 )
0 .0 3 5
(0 .0 2 6 )
Gender differences in entrepreneurship and intrapreneurship 471
123
In Specification 1, the negative effect of being a
woman is 1 % statistically significant, implying that
women are, ceteris paribus , less likely to become
intrapreneurs and to remain as employees than men are.
This finding supports Hypothesis 1b. It suggests that
women may be not only risk averse, but also in a
disadvantageous position in the workplace. In contrast
to the case of entrepreneurship above, however,
statistical significance is not seen in Specifications 3
and 4. As expected, the interaction of children and
gender (being a woman) has a negative effect
(Specifications 2 and 4), although no statistical signif-
icance is found. Thus, Hypothesis 2b is weakly
supported. Marriage, as in the choice of entrepreneur-
ship, also has a negative effect. However, the interac-
tion of gender and marriage shows mixed results: In
Specification 2, it has a negative effect, whereas
Specification 4 indicates a positive sign. Neither of
these is statistically significant, though. The interaction
of gender and family size also produces mixed results.
Regarding employment-related variables, part-time
work has a negative effect on intrapreneurship, and this
effectisstatisticallysignificantinSpecifications1,2 and
5,where itisnot interacted with gender. Specifications 3
and 4 show that being a woman has a reverse effect,
which supports Hypothesis 3b: The negative effect of
part-time work on intrapreneurship is stronger for men,
while for women, part-time work does not have as much
of an adverse effect as it does for men. Again, this may
suggest that the meaning of part-time work in organi-
zations, and thus its effect on one’s propensity to be an
intrapreneur, differs across genders. The effects of firm
size may also be different across genders: Both in
Specifications 3 and 4, among those who work for a
small firm, women are more likely to be an intrapreneur,
whereas among thosewhoworkfor a large firm, menare
more likely to be an intrapreneur.
Next, if we look at other control variables, black
individuals are also more likely to be an intrapreneur.
The average effects of education level have negative
effects on intrapreneurship, although being a woman
has reverse effects. However, they are relatively
smaller than the effects on entrepreneurship, and none
has statistical significance. This result is consistent
with Parker’s (2011) finding that the role of (general)
human capital is more prominent in nascent
entrepreneurship than in nascent intrapreneurship.
Regarding age effects, young employees are more
likely to become intrapreneurs than older employees,T a b le
8 c o n ti n u e d
M a rr ie d
U n m a rr ie d
S p e c ifi c a ti o n 1
S p e c ifi c a ti o n 3
S p e c ifi c a ti o n 5
S p e c ifi c a ti o n 1
S p e c ifi c a ti o n 3
S p e c ifi c a ti o n 5
In te rn e t
0 .0 2 2 *
(0 .0 1 3 )
0 .0 2 3 *
(0 .0 1 3 )
0 .0 2 2 *
(0 .0 1 3 )
0 .0 1 1
(0 .0 0 7 )
0 .0 1 2
(0 .0 0 7 )
0 .0 1 2
(0 .0 0 7 )
N 6 8 3 5
6 8 3 5
6 8 3 5
4 2 7 8
4 2 7 8
4 2 7 8
A v e ra g e (f o r d is c re te
v a ri a b le s)
a n d m a rg in a l (f o r c o n ti n u o u s v a ri a b le s)
e ff e c ts
R o b u st
st a n d a rd
e rr o rs
a re
in p a re n th e se s
* p \
0 :1 ; * * p \
0 :0 5 ; * * * p \ 0 :0 1
472 T. Adachi, T. Hisada
123
T a b le
9 In tr a p re n e u rs h ip
(E q . 2 )—
su b sa m p le s:
fu ll -t im
e a n d p a rt -t im
e
F u ll -t im
e P a rt -t im
e
S p e c ifi c a ti o n 1
S p e c ifi c a ti o n 2
S p e c ifi c a ti o n 5
S p e c ifi c a ti o n 1
S p e c ifi c a ti o n 2
S p e c ifi c a ti o n 5
D e p e n d e n t v a ri a b le
1 =
In tr a p re n e u rs h ip
0 =
E ls e
F e m a le
- 0 .0 3 8 * * *
(0 .0 0 5 )
- 0 .0 4 2 * * *
(0 .0 1 2 )
- 0 .0 7 8 *
(0 .0 4 1 )
- 0 .0 1 1
(0 .0 1 1 )
- 0 .0 2 0
(0 .0 2 3 )
- 0 .0 1 0
(0 .0 2 4 )
F a m il y
M a rr ie d
- 0 .0 1 0 *
(0 .0 0 6 )
- 0 .0 1 6 * *
(0 .0 0 7 )
- 0 .0 0 9
(0 .0 0 7 )
- 0 .0 1 7 *
(0 .0 0 9 )
- 0 .0 1 4
(0 .0 1 4 )
- 0 .0 1 8 *
(0 .0 1 0 )
C h il d re n u n d e r a g e 1 1
0 .0 0 6
(0 .0 0 6 )
0 .0 0 7
(0 .0 0 8 )
0 .0 0 6
(0 .0 0 7 )
0 .0 0 3
(0 .0 0 9 )
0 .0 0 7
(0 .0 1 6 )
0 .0 0 3
(0 .0 0 9 )
S iz e
0 .0 0 3
(0 .0 0 2 )
0 .0 0 4
(0 .0 0 3 )
0 .0 0 2
(0 .0 0 3 )
0 .0 0 1
(0 .0 0 2 )
- 0 .0 0 1
(0 .0 0 4 )
0 .0 0 1
(0 .0 0 2 )
M a rr ie d 9
fe m a le
0 .0 1 6
(0 .0 1 1 )
- 0 .0 0 7
(0 .0 1 7 )
C h il d re n u n d e r a g e 1 1 9
fe m a le
0 .0 0 1
(0 .0 1 3 )
- 0 .0 0 5
(0 .0 2 0 )
S iz e 9
fe m a le
- 0 .0 0 2
(0 .0 0 4 )
0 .0 0 3
(0 .0 0 5 )
E m p lo y m e n t
F ir m
si z e : R e f =
fi rm
si z e 1 0 0 – 9 9 9
Gender differences in entrepreneurship and intrapreneurship 473
123
T a b le
9 c o n ti n u e d
F u ll -t im
e P a rt -t im
e
S p e c ifi c a ti o n 1
S p e c ifi c a ti o n 2
S p e c ifi c a ti o n 5
S p e c ifi c a ti o n 1
S p e c ifi c a ti o n 2
S p e c ifi c a ti o n 5
F ir m
si z e 9 9 o r le ss
0 .0 0 4
(0 .0 0 7 )
0 .0 0 4
(0 .0 0 7 )
0 .0 0 3
(0 .0 0 7 )
0 .0 1 4
(0 .0 1 2 )
0 .0 1 5
(0 .0 1 3 )
0 .0 1 5
(0 .0 1 3 )
F ir m
si z e 1 0 0 0 o r m o re
- 0 .0 0 2
(0 .0 0 7 )
- 0 .0 0 2
(0 .0 0 7 )
- 0 .0 0 1
(0 .0 0 7 )
0 .0 0 1
(0 .0 1 2 )
0 .0 0 2
(0 .0 1 2 )
0 .0 0 1
(0 .0 1 2 )
R a c e : R e f =
w h it e
B la c k
0 .0 4 4 * * *
(0 .0 0 8 )
0 .0 4 5 * * *
(0 .0 0 8 )
0 .0 3 5
(0 .0 2 3 )
0 .0 1 6
(0 .0 1 1 )
0 .0 1 6
(0 .0 1 1 )
0 .0 1 5
(0 .0 1 1 )
H is p a n ic
0 .0 1 6 *
(0 .0 0 9 )
0 .0 1 6 *
(0 .0 0 9 )
0 .0 1 4
(0 .0 1 0 )
0 .0 1 5
(0 .0 1 1 )
0 .0 1 5
(0 .0 1 1 )
0 .0 1 6
(0 .0 1 2 )
F o re ig n b o rn
- 0 .0 0 2
(0 .0 0 8 )
- 0 .0 0 2
(0 .0 0 8 )
0 .0 0 5
(0 .0 1 4 )
- 0 .3 5 9 * * *
(0 .0 7 9 )
- 0 .3 6 0 * * *
(0 .0 7 6 )
- 0 .3 2 6 * * *
(0 .0 9 2 )
A g e : R e f =
5 5 a n d m o re
1 8 – 2 4
0 .0 4 0 * * *
(0 .0 1 1 )
0 .0 3 9 * * *
(0 .0 1 1 )
0 .0 3 8 * * *
(0 .0 1 3 )
0 .0 2 5
(0 .0 1 6 )
0 .0 2 7
(0 .0 1 7 )
0 .0 3 0 *
(0 .0 1 6 )
2 5 – 3 4
0 .0 2 7 * * *
(0 .0 0 9 )
0 .0 2 6 * * *
(0 .0 0 9 )
0 .0 2 4 *
(0 .0 1 2 )
0 .0 2 9
(0 .0 1 8 )
0 .0 3 0
(0 .0 2 0 )
0 .0 3 3
(0 .0 2 2 )
3 5 – 4 4
0 .0 2 1 * *
(0 .0 0 9 )
0 .0 2 1 * *
(0 .0 0 9 )
0 .0 1 4
(0 .0 1 6 )
0 .0 3 6 * *
(0 .0 1 6 )
0 .0 3 7 * *
(0 .0 1 7 )
0 .0 4 0 * *
(0 .0 1 8 )
4 5 – 5 4
0 .0 1 8 * *
(0 .0 0 8 )
0 .0 1 7 * *
(0 .0 0 8 )
0 .0 1 4
(0 .0 1 2 )
0 .0 2 8 *
(0 .0 1 5 )
0 .0 2 9 *
(0 .0 1 6 )
0 .0 3 2 *
(0 .0 1 8 )
474 T. Adachi, T. Hisada
123
T a b le
9 c o n ti n u e d
F u ll -t im
e P a rt -t im
e
S p e c ifi c a ti o n 1
S p e c ifi c a ti o n 2
S p e c ifi c a ti o n 5
S p e c ifi c a ti o n 1
S p e c ifi c a ti o n 2
S p e c ifi c a ti o n 5
E d u c a ti o n : R e f =
H S d ro p o u t
H S g ra d u a te
- 0 .0 3 3 * * *
(0 .0 1 1 )
- 0 .0 3 3 * * *
(0 .0 1 1 )
- 0 .0 3 3
(0 .0 2 8 )
- 0 .0 0 6
(0 .0 1 2 )
- 0 .0 0 6
(0 .0 1 3 )
- 0 .0 0 8
(0 .0 1 8 )
S o m e c o ll e g e
- 0 .0 1 4
(0 .0 1 1 )
- 0 .0 1 4
(0 .0 1 1 )
- 0 .0 1 7
(0 .0 1 6 )
- 0 .0 1 5
(0 .0 1 3 )
- 0 .0 1 5
(0 .0 1 3 )
- 0 .0 2 5
(0 .0 1 9 )
B a c h e lo r
- 0 .0 2 5 * *
(0 .0 1 1 )
- 0 .0 2 5 * *
(0 .0 1 1 )
- 0 .0 3 4
(0 .0 2 4 )
- 0 .0 2 0
(0 .0 1 6 )
- 0 .0 2 1
(0 .0 1 7 )
0 .0 0 4
(0 .0 1 9 )
P o st g ra d u a te
- 0 .0 1 9
(0 .0 1 2 )
- 0 .0 1 9
(0 .0 1 2 )
- 0 .0 2 2
(0 .0 2 5 )
0 .0 0 0
(0 .0 1 5 )
0 .0 0 1
(0 .0 1 5 )
0 .0 0 8
(0 .0 2 1 )
H S g ra d u a te
9 fe m a le
0 .0 3 3
(0 .0 4 4 )
0 .0 0 4
(0 .0 2 4 )
S o m e c o ll e g e 9
fe m a le
0 .0 3 1
(0 .0 3 6 )
0 .0 1 4
(0 .0 2 5 )
B a c h e lo r 9
fe m a le
0 .0 5 6
(0 .0 3 8 )
- 0 .3 1 8 * * *
(0 .0 9 4 )
P o st g ra d u a te
9 fe m a le
0 .0 4 5
(0 .0 3 5 )
- 0 .0 1 1
(0 .0 2 9 )
In te rn e t
0 .0 3 8 * * *
(0 .0 0 8 )
0 .0 3 8 * * *
(0 .0 0 8 )
0 .0 3 1
(0 .0 2 2 )
0 .0 0 3
(0 .0 1 0 )
0 .0 0 4
(0 .0 1 0 )
0 .0 0 2
(0 .0 1 0 )
N 9 3 3 1
9 3 3 1
9 3 3 1
1 7 8 2
1 7 8 2
1 7 8 2
A v e ra g e (f o r d is c re te
v a ri a b le s)
a n d m a rg in a l (f o r c o n ti n u o u s v a ri a b le s)
e ff e c ts
R o b u st
st a n d a rd
e rr o rs
a re
in p a re n th e se s
* p \
0 :1 ; * * p \
0 :0 5 ; * * * p \
0 :0 1
Gender differences in entrepreneurship and intrapreneurship 475
123
as opposed to the case of entrepreneurship. Individuals
who use the Internet are also more likely to become
intrapreneurs. This may imply that computer skills
would be useful for both entrepreneurship and
intrapreneurship.
Table 8 shows the estimated average/marginal
effects in Eq. (2) from subsamples of married and
unmarried individuals. In each specification, the
estimated negative effect of being a woman on
intrapreneurship is stronger for married individuals
than for unmarried individuals. Thus, in contrast to
entrepreneurship, more women are discouraged from
trying intrapreneurship if they are married. In addition,
for unmarried women, working for a small or large
firm has a negative effect on intrapreneurship,
whereas the opposite is true for married women
(compare the results from Specification 3). This is also
in contrast to entrepreneurship. Interestingly, in all of
the specifications, the effects of being a married or
unmarried part-time employee lose statistical signif-
icance. This suggests that part-time employees are,
regardless of marital status, as likely to pursue
intrapreneurship as full-time employees.
Table 9 shows the estimated effects from subsam-
ples of full-time and part-time workers. In each
specification, the estimated negative effect of being a
woman on intrapreneurship is stronger for full-time
employees than for part-time employees. Again, this is
opposite to the results from the estimates of Eq. (1).
Note also that the signs for the interactions of gender
with marriage, children, and family size have opposite
signs between the full-time and part-time subsamples.
For example, as in the case of entrepreneurship, for
part-time workers, the interaction of being as a woman
and the presence of children has a negative effect on
intrapreneurship. That is, if a part-time worker is a
woman with children, she is not inclined toward either
entrepreneurship or intrapreneurship. Such a woman
may be satisfied with time flexibility so that
entrepreneurship is less attractive, and she may not
want to lose the flexibility by becoming an intrapre-
neur, either.
Finally, the parameter estimates (available upon
request) indicate that Specification 1 yields a smaller
value of Akaike’s information criterion (AIC). Thus,
Specification 1 is preferred. 24 Thus, the counterfactual
probabilities computed in Table 10 in the next
subsection are based on Specification 1. In this
specification, the estimated correlation coefficients
between the unobservables, �1i in Eq. (1) and �2i in
Eq. (2), are greater than 0.9 and are statistically
significant. Recall that in our bivariate probit model
with sample selection, a low value of �1i favors
entrepreneurship, and a high value of �2i favors
intrapreneurship. That is, our positive estimates for q suggest that what Lucas (1978) calls (unobserved)
‘‘entrepreneurial skills/talents,’’ are negatively related
to (unobserved) ‘‘intrapreneurial skills/talents.’’ If one
ignores this correlation (i.e., estimating each of the
equations independently, or treating the three alterna-
tives equally as in a multinomial logit model), the
parameter estimates would be biased, and the pre-
dicted rates of entrepreneurship and intrapreneurship
Table 10 Actual and counterfactual rates of start-up activities
Gender Characteristics
Male Female Difference
Panel A: Pr (Entrepreneur)
Male 3.56 %
(0.0024)
3.83 %
(0.0003)
0.27 %
Female 1.99 %
(0.0002)
2.16 %
(0.0020)
-0.17 %
Difference -1.57 %*** 1.67 %***
Panel B: Pr (Intraperneur)
Male 3.85 %
(0.0025)
3.74 %
(0.0002)
-0.11 %
Female 1.68 %
(0.0001)
1.62 %
(0.0017)
-0.06 %
Difference -2.17 %*** 2.11 %***
Standard errors in parentheses. Diagonal cells are actual rates,
and non-diagonal cells are counterfactual rates
For example, the (male, female) cell in Panel A means that if
all men’s characteristics are drawn from the distribution of
covariates for women, 3.83 % of men would be engaged in
entrepreneurial activities, a higher number than the actual rate,
3.56 %
* p\0:1; ** p\0:05; *** p\0:01
24 To consider the possibility that intrapreneurship may mean
different things across firm sizes, we estimate the two equations
with a subsample of those who work for a firm with fewer than
100 workers and with a subsample of the others. We also
conduct the same exercise by dividing the sample into those who
work for a firm with fewer than 25 workers (this is the minimum
number for the firm size categorization) and others. We find that
overall, the parameter estimates (available upon request) are
similar across the subsamples.
476 T. Adachi, T. Hisada
123
Table 11 Decomposition of gender differences
Specification 1
Weighted coefficients Male coefficients Female coefficients
Panel A: entrepreneurship
Male mean (%) 3.56 3.56 3.56
Female mean (%) 2.16 2.16 2.16
Male–female difference (%) 1.40 1.40 1.40
Explained difference (%) -0.15 -0.41 -0.09
Unexplained difference (%) 1.55 1.81 1.49
Contributions from gender differences in
Family 0.0001403
1.00 %
-0.0001800
-1.29 %
0.0005394
3.85 %
Married (-0.0002549)
(-1.82 %)
(-0.0005433)
(-3.88 %)
(0.0000244)
(0.17 %)
Children under age 11 (0.0001163)
(0.83 %)
(0.0001082)
(0.77 %)
(0.0002083)
(1.49 %)
Size (0.0002789)
(1.99 %)
(0.0002551)
(1.82 %)
(0.0003067)
(2.19 %)
Employment -0.0004895
-3.49 %
-0.0020922
-14.94 %
-0.0000299
-0.21 %
Part-time (-0.0005644)
(-4.03 %)
(-0.0021928)
(-15.66 %)
(-0.0000678)
(-0.48 %)
Firm size 99 or less (0.0000713)
(0.51 %)
(0.0001003)
(0.72 %)
(0.0000332)
(0.24 %)
Firm size 1000 or more (0.0000036)
(0.03 %)
(0.0000003)
(0.00 %)
(0.0000047)
(0.03 %)
Age 0.0003143
2.25 %
0.0003384
2.41 %
0.0000272
0.20 %
Education 0.0001248
0.89 %
0.0001224
0.88 %
-0.0002649
-1.89 %
Other variables -0.0016054
-11.48 %
-0.0022889
-16.35 %
-0.0011610
-8.28 %
All included variables -0.0015155
-10.83 %
-0.0041003
-29.29 %
-0.0008891
-6.33%
N 11,113 11,113 11,113
Panel B: intrapreneurship
Male mean (%) 3.41 3.41 3.41
Female mean (%) 0.94 0.94 0.94
Male–female difference (%) 2.46 2.46 2.46
Explained difference (%) 0.14 0.18 0.01
Unexplained difference (%) 2.33 2.28 2.45
Contributions from gender differences in
Family -0.0000274
-0.11 %
-0.0000466
-0.18 %
-0.0003161
-1.28 %
Married (-0.0000991)
(-0.40 %)
(-0.0002128)
(-0.86 %)
(-0.0002382)
(-0.97 %)
Gender differences in entrepreneurship and intrapreneurship 477
123
under counterfactual scenarios would be imprecise. As
in Parker (2011), this justifies our empirical model of
double selection.
In summary, we find that women are less likely to
choose entrepreneurship presumably because of their
aversion to risk, the existence of credit constraints or
discrimination. Furthermore, marriage, children, and
family size have additional effects that work positively
for women. Thus, Hypotheses 1a and 2a are supported.
We find, however, that part-time work has additional
negative effects on entrepreneurship for women,
rejecting Hypothesis 3a. As for intrapreneurship, we
find that women are also less likely to become
intrapreneurs (Hypothesis 1b). In addition, the pres-
ence of children has additional negative effects on
intrapreneurship for women, supporting Hypothesis
2b. This may suggest that intrapreneurship does not
provide women with more time flexibility. Interest-
ingly, for women, the negative effect of being a part-
time worker on intrapreneurship is weaker, suggesting
that part-time work is not so disadvantageous for
women to become an intrapreneur and that part-time
work would be a greater stigma for men.
4.2 Decomposition of the gender gap
To explore further the relationships between gender
and start-up activities, we compute women’s actual
and predicted probabilities of becoming independent
entrepreneurs and intrapreneurs when they become the
average man (i.e., in each simulation, each woman’s
covariates are drawn from the estimated distribution of
the covariates for men). We also show the results from
the nonlinear version of the Blinder–Oaxaca decom-
position (see, e.g., Blinder 1973; Oaxaca 1973;
Oaxaca and Ransom 1994; Fairlie 1999, 2003, 2005;
Yun 2004; Fortin et al. 2011). It decomposes the
gender differences in the average rate of becoming an
Table 11 continued
Specification 1
Weighted coefficients Male coefficients Female coefficients
Children under age 11 (0.0000643)
(0.26 %)
(0.0001222)
(0.50 %)
(-0.0000402)
(-0.16 %)
Size (0.0000074)
(0.03 %)
(0.0000440)
(0.18 %)
(-0.0000377)
(-0.15 %)
Employment 0.0011145
4.52 %
0.0017755
7.21 %
0.0002038
0.83 %
Part-time (0.0010971)
(4.45 %)
(0.0017600)
(7.14 %)
(0.0001800)
(0.73 %)
Firm size 99 or less (0.0000170)
(0.07 %)
(0.0000160)
(0.07 %)
(0.0000245)
(0.10 %)
Firm size 1000 or more (0.0000004)
(0.00 %)
(-0.0000005)
(0.00 %)
(-0.0000007)
(0.00 %)
Age 0.0003798
1.55 %
0.0005048
2.05 %
0.0003291
1.34 %
Education 0.0000038
0.02 %
0.0001252
0.51 %
-0.0000587
-0.24 %
Other variables -0.0000840
-0.33 %
-0.0005557
-2.26 %
-0.0000125
-0.05 %
All included variables 0.0013867
5.65 %
0.0018031
7.33 %
0.0001456
0.60 %
N 10,791 10,791 10,791
The decomposition is considered for nonlinear
478 T. Adachi, T. Hisada
123
independent entrepreneur or intrapreneur into the
characteristics’ effect and the coefficients’ effect as
given below:
Prm � Prf ¼ Prðb̂�; XmÞ � Prðb̂�; Xf Þ |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}
characteristics0 effect ð‘‘explained00Þ
þ Prðb̂m; X �Þ � Prðb̂f ; X
�Þ |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}
coefficients0 effect ð‘‘unexplained00Þ
;
where Prm and Prf denote the average predicted
probabilities of becoming an independent entrepre-
neur or intrapreneur for men and women, respectively
(thus, Prm � Prf expresses the observed gender gap in independent entrepreneurship or intrapreneurship),
b̂� ¼ Xb̂m þ ðI � XÞb̂f , with X being a weighting matrix, b̂m and b̂f being the parameter estimates in the male sample, and female sample, respectively, and
finally, X� ¼ ðI � XÞXm þ XXf , with Xm and Xf representing the observed characteristics of men and
women, respectively. 25
4.2.1 Entrepreneurship
First, Panel A of Table 10 displays the actual prob-
abilities of choosing entrepreneurship by gender in the
diagonal cells and the counterfactual probabilities in
the non-diagonal cells. As shown in the table, if the
distribution of men’s characteristics is identical to that
of women’s, then the predicted chance of becoming an
entrepreneur is 3.8 %, whereas the actual chance is
3.6 %, although this difference is not statistically
significant. On the other hand, if the distribution of
women’s characteristics is identical to that of men’s,
they are less likely to choose entrepreneurship by
0.2 % points (this is also not statistically significant).
These two counterfactual scenarios suggest that
female characteristics do favor entrepreneurship.
However, column ‘‘Male’’ shows that even if the
distribution of women’s characteristics xi is identical
to that of men’s (except femalei), women are less
likely to become entrepreneurs than men, and this
difference (1.6 %) is 1 % statistically significant.
Column ‘‘Female’’ also shows a similar result if the
distribution of men’s characteristics becomes identical
to that of women’s. These two results show that
women are less likely to choose entrepreneurship
precisely because they are women, suggesting that
women may be in a disadvantageous position when
becoming entrepreneurs. In this sense, policies for
promoting entrepreneurship with an emphasis on
women would be justified, as the US SBA currently
emphasizes (recall the first paragraph of the Introduc-
tion). For example, if the mismatch of nascent
entrepreneurs and start-up assistance programs is
serious, as found by Yusuf (2010), policies would be
better improved focusing on women’s
entrepreneurship.
Now, Panel A of Table 11 shows the results from
decomposition of the gender differences in
entrepreneurship. Following Oaxaca and Ransom
(1994), we show the decomposition result from the
weighted coefficients (b̂� above) in column ‘‘Weighted coefficients.’’ It is well known that the
decomposition result is sensitive to whether male or
female coefficients are used. Although we prefer to
argue the result based on the weighted coefficients, we
also show the results from the use of the two
coefficients, respectively (in columns ‘‘Male coeffi-
cients’’ and ‘‘Female coefficients’’). It is observed that
the gender differences in the observed characteristics
reduce the observed gender gap by 10.8 %. Among
these contributions, the effects of the gender differ-
ences in the employment-related variables on the
gender gap are larger (in absolute terms) than those in
Table 12 Actual and counterfactual rates of the three modes
Actual (%) Counterfactual (%)
Entrepreneurs
Male 3.56
Female 2.16 1.99
Intrapreneurs
Male 3.85
Female 1.62 1.67
Uninvolved
Male 42.59
Female 46.21 46.34
25 Our implementation is based on Sinning et al. (2008).
Following Oaxaca and Ransom (1994), we do not include the
gender dummy when we obtain the estimates. This issue has not
been settled in the literature. For example, Elder et al. (2010)
recommend the inclusion of the group variable, whereas Lee
(2015) opposes it.
Gender differences in entrepreneurship and intrapreneurship 479
123
Table 13 State-level data
State Homestead
exemption ($)
Median home
value ($)
Unemployment
rate (%)
Individual income
tax rate (%)
Corporate income
tax rate (%)
Sales tax
rate (%)
Alabama 10,000 97,500 4 5 6.5 4
Arizona 150,000 185,400 4.7 5.04 6.97 5.6
Arkansas Unlimited 87,400 4.9 7 6.5 6
California 75,000 477,700 5.4 9.3 8.84 6.25
Colorado 90,000 223,300 5 4.63 4.63 2.9
Connecticut 150,000 271,500 4.9 5 7.5 6
Delaware 0 203,800 4.2 5.95 8.7 0
D.C. 36,900 384,400 6.5 9 9.98 5.75
Florida Unlimited 189,500 3.8 0 5.5 6
Georgia 20,000 147,500 5.3 6 6 4
Idaho 50,000 134,900 3.8 7.8 7.6 6
Illinois 15,000 183,900 5.7 3 7.3 6.25
Indiana 15,000 114,400 5.4 3.4 8.5 6
Iowa Unlimited 106,600 4.6 8.98 12 5
Kansas Unlimited 107,800 5.1 6.45 4 5.3
Kentucky 10,000 103,900 6.1 6 8.25 6
Louisiana 25,000 101,700 7.1 6 8 4
Maine 70,000 155,300 4.8 8.5 8.93 5
Maryland 0 280,200 4.1 4.75 7 5
Massachusetts 500,000 361,500 4.8 5.3 9.5 5
Michigan 36,900 149,300 6.7 3.9 1.9 6
Minnesota 200,000 198,800 4 7.85 9.8 6.5
Mississippi 150,000 82,700 7.9 5 5 7
Missouri 15,000 123,100 5.4 6 6.25 4.225
Montana 200,000 131,600 4 11 6.75 0
Nebraska 12,500 113,200 3.8 6.84 7.81 5.5
Nevada 200,000 283,400 4.1 0 0 6.5
New Hampshire 200,000 240,100 3.6 5 9.25 0
New Jersey 36,900 333,900 4.4 8.97 9 6
New Mexico 60,000 125,500 5.3 6.8 7.6 5
New York 20,000 258,900 5 7.7 7.5 4
North Carolina 10,000 127,600 5.2 8.25 6.9 4.5
North Dakota 80,000 88,600 3.4 5.54 7 5
Ohio 10,000 129,600 5.9 7.5 8.5 6
Oklahoma Unlimited 89,100 4.4 6.65 6 4.5
Oregon 33,000 201,200 6.1 9 6.6 0
Pennsylvania 36,900 131,900 5 3.07 9.99 6
Rhode Island 200,000 281,300 5 9.9 9 7
South Carolina 36,900 113,100 6.8 7 5 5
South Dakota Unlimited 101,700 3.9 0 0 4
Tennessee 7,500 114,000 5.6 6 6.5 7
Texas Unlimited 106,000 5.3 0 0 6.25
Utah 40,000 167,200 4.3 7 5 4.75
Vermont 150,000 173,400 3.5 9.5 9.75 6
480 T. Adachi, T. Hisada
123
the family-related variables (�3:5 vs. 1.0 %). Thus, while we find that family size also matters to women’s
entrepreneurship as in Hundley (2000), employment
status would be more important in explaining the
gender gap in entrepreneurship. 26
4.2.2 Intrapreneurship
Now, we look at intrapreneurship. Panel B of Table 10
depicts the gender differences in the actual and the
counterfactual probabilities of becoming an intrapre-
neur. Importantly, if a woman has the same charac-
teristics as a man, her likelihood of becoming an
intrapreneur would be 1.7 %, slightly higher than the
actual rate of 1.6 %, although this difference is not
statistically significant. On the other hand, the oppo-
site is true for men (see row ‘‘Male’’). These two
counterfactual scenarios suggest that, in contrast to
entrepreneurship, female characteristics do not favor
intrapreneurship. More importantly, columns ‘‘Male’’
and ‘‘Female’’ both suggest that women may also be in
a disadvantageous position when becoming intrapre-
neurs. The male–female difference is 2.2 % points if
the distribution of female characteristics xi is identical
to that of male characteristics (except femalei), and
this difference is 1 % statistically significant.
Comparison within column ‘‘Female’’ gives a similar
result. 27
Lastly, Panel B of Table 11 shows the results from
decomposition of the gender differences in
intrapreneurship. Now, in contrast to the case of
entrepreneurship, the gender differences in the
employment-related variables (positively) contribute
to the gender gap in intrapreneurship, and these effects
are larger than those in the family-related variables
(4.5 vs. 0.1 %). Although the gender differences in
employment status is more significant than those in
family status in explaining the observed gender gap in
both entrepreneurship and intrapreneurship, their
signs vary across the two start-up modes.
5 Concluding remarks
By broadening the concept of start-up activity, this
study examines how gender matters in entrepreneur-
ship and intrapreneurship. We find that marriage,
children, and family size have additional positive
effects on women’s entrepreneurship, whereas part-
time work has additional negative effects. For
women’s intrapreneurship, children have additional
Table 13 continued
State Homestead
exemption ($)
Median home
value ($)
Unemployment
rate (%)
Individual income
tax rate (%)
Corporate income
tax rate (%)
Sales tax
rate (%)
Virginia 10,000 212,300 3.5 5.75 6 4
Washington 40,000 227,700 5.5 0 0 6.5
West Virginia 50,000 84,400 5 6.5 9 6
Wisconsin 40,000 152,600 4.7 6.75 7.9 5
Wyoming 20,000 135,000 3.6 0 0 4
Mean 74,107 a
175,416 4.9 5.8 6.7 4.9
Median 40,000 147,500 4.9 6 7 5.3
SD 93,558 87,561 1.01 2.81 2.89 1.76
Source Corradin et al. (2016) (Homestead exemptions), 2005 American Community Survey (Median home values)
Bureau of Labor Statistics (Unemployment rates), Tax foundation (Taxes)
Alaska and Hawaii are not included because the PSED II does not included individuals living in these states. a States with ‘‘Unlimited’’ are excluded
26 For other aspects of gender differences in entrepreneurship,
Leoni and Falk (2010) focus on areas of university graduates’
majors, and Bönte and Piegeler (2013) consider gender differ-
ences in preferences toward competitive situations.
27 Notice that it is possible to compute the actual and
counterfactual (when all women acquire the same characteris-
tics as men) rates of the non-involved for men and for women as
in Table 12. Unfortunately, however, it is not possible to predict
how the three rates for men would change because we do not
model interactions among individuals.
Gender differences in entrepreneurship and intrapreneurship 481
123
negative effects, whereas part-time work is not
disadvantageous for women in becoming intrapre-
neurs. Our counterfactual experiments suggest that the
rate of entrepreneurial activities by women, who
acquire the same characteristics as men (in the
distributional sense), is lower than that of men’s
entrepreneurial activities. Similarly, the rate of
intrapreneurial activities of women with the same
characteristics as men will be lower than the rate of
men’s intrapreneurial activities. These two findings
suggest that women may be disadvantaged for
becoming entrepreneurs and intrapreneurs. In addi-
tion, our decomposition results suggest that for both
entrepreneurship and intrapreneurship, the gender
differences in the employment-related variables are
more significant than those in the family-related
variables in affecting the observed gender gap nega-
tively (for entrepreneurship) or positively (for
intrapreneurship).
Our empirical results would imply that if the
government aims to reduce the gender gap in start-up
activities, it should recognize that workplace condi-
tions, rather than family-related policies, would be
important. However, caution must be taken when
deriving policy implications from our results because
we do not discuss the performances of these start-up
activities. 28
In particular, it is difficult to measure the
performance of intrapreneurial activities: The process
and performance of an intrapreneurial activity has to
be recorded, and a sufficient number of such obser-
vations have to be made available to researchers.
Nonetheless, it would be interesting to study how
gender matters to the duration of intrapreneurship
when measuring the performance of start-up activities.
However, our empirical model applied in this paper is
inherently static and has obvious limitations. Impor-
tant issues, including this, await future research to
deepen our understanding of start-up activities in a
broader sense.
Acknowledgments We thank Rui Baptista (editor-in-charge) and two anonymous referees for invaluable suggestions. We are
also grateful to Taehyun Ahn, Andrew Ching, Yuji Honjo,
Hiroaki Ino, Masa Kato, Mizuki Komura, Eiji Mangyo, Hitoshi
Mitsuhashi, Akira Nagae, Ryo Nakajima, Hikaru Ogawa, Fumio
Ohtake, Atsushi Ohyama, Hiroyuki Okamuro, Lars Osberg,
Hideo Owan, Kate Rybczynski, Koji Shirai, Taiki Susa,
Hidenori Takahashi, Ryuichi Tanaka, Masa Tsubuku, Shintaro
Yamaguchi, Weina Zhou, and seminar and conference
participants at Chuo, Dalhousie, Hitotsubashi, Keio, Kwansei
Gakuin, Sogang, the 42nd Annual Conference of the European
Association for Research in Industrial Economics, the Kansai
Research Group for Econometrics, the Tokyo Labor Economics
Workshop, the Kansai Labor Research Group, the 50th Annual
Conference of the Canadian Economics Association, and the
2016 Spring Meeting of the Japanese Economic Association for
helpful comments and discussions on earlier versions of the
paper. Special thanks are also due to Rebecca McBee for helping
us construct data used in this paper. Adachi acknowledges
Grants-in-Aid for Scientific Research (A) (23243049) and
(C) (15K03425) from the Japan Society for the Promotion of
Science. Any remaining errors are our own.
Appendix: Variables of the financial environment
Since the PSED II was conducted from September
2005 to February 2006, we set 2005 as the base year.
To measure state-varying bankruptcy exemptions, we
use homestead exemptions in 2005, and this informa-
tion is based on Table 1 of Corradin et al. (2016). To
capture the local housing market, we use the median
value of owner-occupied housing units in 2005, and
this variable comes directly from the 2005 American
Community Survey (Variable B25077; owner-occu-
pied housing units). The state-specific unemployment
Table 14 Correlations between institutional
variables
Exemp Home Unemp Ind inc Corp inc Sales
Exemp – -0.08 -0.20 -0.19 -0.17 0.07
Home – – -0.06 0.14 0.19 0.03
Unemp – – – 0.08 -0.02 0.23
Ind inc – – – – 0.71 -0.17
Corp inc – – – – – -0.04
Sales – – – – – –
28 For example, Fairlie and Robb (2009) find that the lower
performances of women-owned businesses are explained by
both less human and financial capital that are specific to starting
a business. See also Robb and Watson (2012) on gender
differences in the performance of new ventures, Fairlie (1999)
and Ahn (2011) on racial differences in the duration of
entrepreneurship, and Oe and Mitsuhashi (2013) on the effects
of founders’ experiences on the profitability of start-ups.
482 T. Adachi, T. Hisada
123
rate is the annual average in 2005 (available at the
Web page of the US Bureau of Labor Statistics 29 ).
Finally, we consider three tax rates: individual
income, corporate income, and sales taxes in 2005.
The information is taken from the Tax Foundation’s
Webpage (http://taxfoundation.org/tax-topics/state-
taxes; accessed July 2016). Following Rohlin and
Ross (2016), we use the highest marginal rate for
individual income and corporate income taxes.
Table 13 presents the state-level data for the
financial environment. All these variables have suffi-
cient variations. Table 14 shows that the correlations
among these variables are weak, except for the one
between individual income tax and corporate income
tax. There are seven states that do not set an exemption
level. In Table 13, such a state is deemed ‘‘unlimited,’’
and in our empirical analysis, we impute $500,000, the
maximum amount from the rest of the states, for these
states’ exemption level. The federal level of exemp-
tion in 2005 was $36,900, and for states that had a
lower amount but allowed their residents to opt out for
the federal level, the amount is set at $36,900.
However, 17 states continued to have a lower amount
than $36,900. In particular, there are two states
(Delaware and Maryland) that did not permit any
homestead exemption. 30
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- Gender differences in entrepreneurship and intrapreneurship: an empirical analysis
- Abstract
- Introduction
- Related literature and hypothesis building
- Data
- Sample construction
- Summary statistics
- Empirical analysis
- Estimates of the bivariate probit model with sample selection
- Selection of entrepreneurship (Eq. 1)
- Selection of intrapreneurship (Eq. 2)
- Decomposition of the gender gap
- Entrepreneurship
- Intrapreneurship
- Concluding remarks
- Acknowledgments
- Appendix: Variables of the financial environment
- References