ECONOMIC RESEARCH CENTERDISCUSSION PAPER
E-Series

No. E16-2

Gender Gap in Start-up Activities
byTakanori Adachi
Takanori Hisada

February 2016

ECONOMIC RESEARCH CENTERGRADUATE SCHOOL OF ECONOMICSNAGOYA UNIVERSITY

Gender Gap in Start-up Activities∗
Takanori Adachi†

Takanori Hisada‡

January 26, 2016

Abstract
This study examines the gender gap in start-up activities to determine
whether household status or employment status is responsible for creating
the observed gender gap. We consider entrepreneurship and intrapreneurship as two diﬀerent forms of start-up activities: while intrapreneurship is
conducted within an existing organization, entrepreneurship is solely an independent activity. This study focuses on this fundamental distinction to help
us identify parameters of our double selection model. Using representative
U.S. data, augmented by other sources, we find that the counterfactual rateof entrepreneurial activities by women who have the same (in the distributional sense) characteristics as men is lower than the actual rate for men. A
similar result is obtained for intrapreneurial activities. More importantly, ourBlinder–Oaxaca decomposition results suggest that for both entrepreneurshipand intrapreneurship, women’s employment status is more significant thantheir household status in explaining the gender gap in start-up activities.
This is more apparent in the group of whites, college graduates, and those
who work for a large firm.
JEL classification: J15; J16; L26; M13.

∗

We are grateful to Taehyun Ahn, Andrew Ching, Yuji Honjo, Hiroaki Ino, Masa Kato, Mizuki
Komura, Eiji Mangyo, Hikaru Ogawa, Atsushi Ohyama, Hiroyuki Okamuro, Lars Osberg, Koji
Shirai, Taiki Susa, Hidenori Takahashi, Masa Tsubuku, Shintaro Yamaguchi, Weina Zhou, andseminar and conference participants at Chuo, Dalhousie, Hitotsubashi, Kwansei Gakuin, Sogang,
the 42nd Annual Conference of the Eupean Association for Research in Industrial Economics,
and the Kansai Research Group for Econometrics for helpful comments on earlier versions of thepaper. Special thanks are also due to Rebecca McBee for helping us to construct our data. Adachi
acknowledges Grants-in-Aid for Scientific Research (A) (23243049) and (C) (15K03425) from theJapanese Ministry of Education, Science, Sports, and Culture. Any remaining errors are our own.
†
School of Economics, Nagoya University, 1 Furo-cho, Chikusa, Nagoya 464-8601, Japan. Email: adachi.t@soec.nagoya-u.ac.jp‡
Graduate School of Economics, Osaka University, 1-7 Machikaneyama, Toyonaka, Osaka 5600043, Japan. E-mail: pge023ht@student.econ.osaka-u.ac.jp

1

Introduction

Entrepreneurship is often promoted as an opportunity for women to improve theirworking life, which might not be easily achieved in the labor market. For instance,
the Small Business Agency (SBA) in the U.S. has an Oﬃce of Women’s Business
Ownership to promote female entrepreneurs.1 There are a number of reasons forthis. First, women have higher rates of unemployment than men (Azmat, Göell andManning (2006)). In addition, they may encounter the proverbial glass ceiling in aworkplace (Elliott and Smith (2004)). It is also known that women experience wage
gaps relative to white men (Blau and Kahn (1992)). However, intrapreneurship 2
also provides women with opportunities to engage in a start-up activity within an
established organization.3 Do gender diﬀerences also matter in intrapreneurship?
While gender diﬀerences in entrepreneurship have been extensively studied (see thenext section), little is known about how gender is related to intrapreneurship. In
this study, we examine how gender leads to diﬀerences in the determinants of intrapreneurship as well as those of entrepreneurship.
Our main findings are summarized as follows. First, we find that the counterfactual rate of entrepreneurial activities by women, who acquire the same (in the
distributional sense) observed characteristics as men, is lower than the actual rate ofmen’s entrepreneurial activities. Similarly, the counterfactual rate of intrapreneurial
activities by women, who have the same characteristics as men, is also lower than
the actual rate of men’s intrapreneurial activities. These two results suggest thatwomen may be in a disadvantageous position when becoming entrapreneurs and intrapreneurs. More importantly, our Blinder–Oaxaca decomposition results suggestthat for both entrepreneurship and intrapreneurship, women’s employment status1

See https://www.sba.gov/oﬃces/headquarters/wbo.
Intrapreneurship is sometimes called “corporate entrepreneurship.” In this study, we use “intrapreneurship” and “intrapreneurs” throughout.
3
For example, Parker (2009, p.31) states that “[d]ependent spinoﬀs are ventures formed incollaboration with an incumbent firm (sometimes termed ‘intrapreneurship’), whereas independent
spin-oﬀs are pursued entirely separately from an incumbent (‘entrepreneurship’).” See also 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), Antoncic and Hisrich(2003), Hellmann (2007), and Baruah and Ward (2015) for this distinction.
2

1

is more important than their household status to the gender gap in start-up activities. This is more apparent in the group of whites, college graduates, and those whowork for a large firm. An interesting diﬀerence between entrepreneurship and intrapreneurship is that while the female characteristics favor entrepreneurship, they
do not favor intarepreneurship, although these two results are not statistically significant.
In our conceptual framework, one first chooses whether to work independently.
If they do, they are called an entrepreneur. Entrepreneurship here is a broad concept: it includes both self-employment and business ownership. If one does notchoose to be an entrepreneur, they may become an intrapreneur.4 To formalize
this conceptual framework, we employ a double selection model, and estimate it byusing an individual-level survey that is representative of the United States (Panel
Study of Entrepreneurial Dynamics, II (PSED II)). Our double selection model issuperior to a nested multinomial logit model because the unobserved variable in theselection of entrepreneurship and that in the selection of intrapreneurship is foundto be negatively corellated (with statistical significance). We can deal with such an
asymmetry relationship in the triangularity of entrepreneurship, intrapreneurship,and else (i.e., not being involved in a start-up); this is not taken into account in anested multinomial logit model.
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 becomes either an entrepreneur or an intrapreneur (Parker (2011), Tietz
and Parker (2012)), or he faces the two alternatives equally (Martiarrena (2013)).
4

In this study, we do not describe the details of this organizational decision process. In ourconceptual framework proposed in Section 5, we assume that an individual chooses one of thethree alternatives that give them the best utility. If an individual who wants to be an intrapreneur
cannot become an intrapreneur because of limited capacities, he does not always choose his bestalternative. We do not model such frictions mainly because of data limitations. In some cases,
a worker may be “ordered” to be an intrapreneur within a company against his will. However,De Clercq, Castañer and Belausteguigoitia (2011) argue that being selected as an intrapreneur isusually financially rewarding. Thus, we would not lose much validity even if we assume that anindividual chooses the alternative that gives him the highest level of utility.

2Thus, in these frameworks, individuals do not distinguish between entrepreneurship and intrapreneurship; in the former concept, a distinction is made based onwhether an individual has a start-up plan in mind, and in the latter, no special
distinction is made among the three alternatives. However, a decision on whether
an individual works independently would be as important as whether he engages
in a start-up activity. In this study, we stress on this fundamental diﬀerence between entrepreneurship as an outside-organization activity and intrapreneurship asa within-organization activity.
In Parker’s (2011) framework of double selection, a distinction is made between
start-up activities (both entrepreneurship and intrapreneurship) and doing something else. Parker (2011) argues that this decision is aﬀected by family status. Thisis because start-up activities are presumed to be more intensive, and thus an individual would care about his family status when choosing whether to work for astart-up. However, once he decides to engage in start-up activity, family status
no longer matters to the choice between entrepreneurship or intrapreneurship. In
contrast, we view entrepreneurship and intrapreneurship as economically diﬀerent
modes. As Douglas and Fitzsimmon (2013) and Martiarena (2013) suggest, risk
aversion would probably matter to the distinction between entrepreneurship andintrapreneurship. Although we are not able to directly link between gender andrisk aversion owning to a lack of data, many experimental studies have found that,
controlling for other demographic characteristics such as age, educational attainment, occupation, and cultural background, women are on average more risk averse
than men (see the survey by Croson and Gneezy (2009)). In addition, many empirical studies find that credit would probably matter to one’s decision to be involvedin an entrepreneurial work (see footnote 6 below). Thus, an important economic
distinction between entrepreneurship and intrapreneurship is the diﬀerences in risk
sharing: in entrepreneurship, entrepreneurs are fully responsible for failures, whereas
in intrapreneurship, almost all the financial burden is on established organizations,although, as Martiarena (2013) and Kacperczyk (2015) point out, intrapreneurs are
incentivized because if they fail, it would become diﬃcult for them to be promoted
or rewarded financially.
3

Thus, to complement Parker’s (2011) conceptual framework, we employ a framework where an employed individual first makes a choice to remain in or move outof the organization; if he decides to remain, he then chooses whether to become an
intrapreneur. To estimate this framework, individuals in the second stage, who areeither intrapreneurs or workers, must have the same covariates. The follow-up partof PSED II has detailed information, such as Parker’s (2011) ‘employer size’, on
entrepreneurs and intrapreneurs only. The disadvantage of employing our conceptual framework is that we are not able to use the follow-up part.5 Thus, we focus
on the decisions by those who are currently employed, whereas in Parker’s (2011)
framework it is possible to include non-employed individuals in the initial stage.
This assumption excludes the non-employed from our sample. To control for thesize of the organization that the individual works for, we match the 2005 Marchversion of the Current Population Survey (CPS) with PSED II because it has information on firm size for each individual currently employed. One could view Parker’s(2011) framework as relatively long-term decision-making, whereas our framework
would be viewed as focusing on relatively short-term decision-making. In this sense,
our use of latent entrepreneurship and latent intrapreneurship could be justified.
This diﬀerence would explain our overall finding that work status is more important
than family status in the determination of the gender gap in entrepreneurship andintrapreneurship.
The rest of the paper is organized as follows. Section 2 briefly discusses studies
most closely related to our study. In Section 3, we introduce the theoretical background, and after describing the data used for this study in Section 4, we present
the empirical analysis in Section 5. We not only provide parameter estimates of
the alternative models but also compute the actual and counterfactual probabilities of becoming an entrepreneur or an intrapreneur, focusing on gender diﬀerences.
Section 6 concludes the paper.

5
However, our main results do not change significantly even if we use the follow-up part, although the sample size is smaller. The details are available upon request.

4

2

Related LiteratureThis study departs from the many of the existing studies on start-up activities
in that we consider both entrepreneurship and intrapreneurship as two diﬀerent
forms of start-up activities.6,7 In a closely related paper, Parker (2011) studies thefactors that are associated with intrapreneurship and those with entrepreneurship.
In the analysis presented below, we use Parker’s (2011) definition of (nascent) intrapreneurs: intrapreneurs are those considering starting a business for their employer. Parker (2011) then investigates what determines whether a new start-up iscommercialized via entrepreneurship or intrapreneurship and finds that conditionalon engaging in a start-up activity, younger and older individuals are more likely to
become intrapreneurs, and that the role of (general) human capital is more prominent in nascent entrepreneurship than in nascent intrapreneurship. Parker (2011)
also finds that African–Americans are equally likely to choose intrapreneurship as
they are to choose entrepreneurship. This is an interesting finding because one
would expect that they are less likely to engage in intrapreneurship because it involves more relations with others, there is a greater potential for discrimination to
occur. Our study produces a similar finding.
Similarly, Tietz and Parker (2012) consider a conceptual framework in which
intrapreneurship and entrepreneurship are tied as start-up activities. They study, inparticular, diﬀerences in motivation in entrepreneurship and intrapreneurship. Theyfind, for example, that if a business starter is financially motivated, he is more likely
to become an intrapreneur, whereas one’s motive for independence makes it more
6

One of the important issues is entry into entrepreneurship. Evans and Leighton (1989a, b) areamong the first to study entrepreneurial entry, but there are many other published and unpublishedpapers. See Evans and Jovanovic (1989), Holtz-Eakin, Joulfaian, and Rosen (1994a, b), Hamilton(2000), Parker (2000), Kawaguchi (2003), Hurst and Lusardi (2004), Kan and Tsai (2006), Buera(2009), Mondragón-Vélez (2009), Malchow-Møller, Markusen, and Skaksen (2010), Fairlie andKrashinsky (2012), and McCann and Folta (2012). Rybczynski (2009) examines an issue similar tothe one central to this study and finds that a gender gap in self-employment earnings can mostly
be ascribed to liquidity constraints.
7
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 entrepreneurs, Kim, Longest and Lippman (2015) on leisure-based and conventional entrepreneurs,
and Parker (2014) on serial and portfolio entrepreneurs.

5likely for him to become an entrepreneur. Again, Tietz and Parker (2012) assume,
as in Parker (2011), that becoming an intrapreneur is contingent on the individual’s
willingness to engage in a start-up activity. However, it may not be so obvious that
all intrapreneurs recognize themselves as business starters because their activity ismore or less “protected” within an existing organization. Instead, individuals may
see more clearly the diﬀerence between working independently and working for an
established organization. Thus, in this study, we aim to complement Parker (2011)
and Tietz and Parker (2012) by considering this possibility.
On the other hand, like us, Douglas and Fitzsimmons (2013) focus on the inbetween nature of intrapreneurship; while it is an organizational activity, it is alsoa start-up activity. Douglas and Fitzsimmons (2013) study how an individual forms
his intention to behave entrepreneurially as an independent entrepreneur or a corporate intrapreneur, and find important cognitive diﬀerences between intrapreneurs
and entrepreneurs. Intrapreneurs have less self-eﬃcacy and greater risk aversion.
This paper aims to complement Douglas and Fitzsimmons (2013) by focusing on
demographic characteristics rather than intrinsic motivation.
Martiarena (2013) also studies the determinants of intrapreneurship, and contrasts intrapreneurship with entrepreneurship. In particular, Maritiarena (2013)
studies whether intrapreneurs resemble entrepreneurs or wage workers and argues
that among intrapreneurs, there exist what Maritiarena (2013) calls “engaged”
intrapreneurs, that is, those who eventually leave the company to create theirown business. Martiarena (2013) shows that intrapreneurs resemble wage workers rather than entrepreneurs and particularly finds personal characteristics of intrapreneurs as compared with entrepreneurs. Intrapreneurs are more risk-averse
than entrepreneurs, prefer lower but less uncertain rewards, and are less confident
in their entrepreneurial skills.8 In contrast, we focus on how gender matters diﬀer8

In a diﬀerent vein, Renko, Tarabishy, Carsrud and Bränback (2015) define an entrepreneurial
leadership style as constituting encouragement to break the status quo and to experiment and learn
for a higher goal. In contrast, we study a broader class of start-up participants, and focus on how
they play diﬀerently in a start-up. As explained above, Douglas and Fitzsimmons (2013) study
cognitive diﬀerences in entrepreneurial and intrapreneurial intentions (see Honig (2001), Monsen,
Patzelt, and Saxton (2010), and Zhang and Bartol (2010) for psychological studies of intrapreneurship.). Douglas and Fitzsimmons (2013) find that intrapreneurs, in comparison with entrepreneurs,

6ently to entrepreneurship and intrapreneurship.
Lastly, Kacperczyk (2015) studies gender diﬀerences in intrapreneurship, choosing the mutual fund industry to compare entrepreneurship and intrapreneurship.
Kacperczyk (2015) finds that women are more likely to pursue intrapreneurship
than entrepreneurship, presumably because female intrapreneurs can make use ofmaternity benefits, such as maternity leave, within the firm, and at the same time,they are financially rewarded: this balance may not be easily attained when women
pursue entrepreneurship. In contrast, this paper, by using nationally representative data and additional information to control for firm size, suggests that women
would find it more diﬃcult to become intrapreneurs than entrepreneurs, implyingthat women may be facing a “glass ceiling” even with respect to intrapreneurship.

3

Theoretical BackgroundHow do gender diﬀerences matter to the choice between entrepreneurship and intrapreneurship? Although entrepreneurship and intrapreneurship are both start-up
activities, they are fundamentally diﬀerent. Most importantly, entrepreneurship entails uncertainty: as Knight (1921, p.299) claims, “the entrepreneur ... takes over
all the uncertainty of the business along with control over it.” Existing empiricalstudies show that individuals with lower risk aversion are more likely to become
entrepreneurs (Ahn (2010), Caliendo, Fossen and Kritikos (2009), Ekelund, Johansson, Järvelin and Lichtermann (2005)). On the other hand, a part of the risk of
intrapreneurship is shared by the organization to which the intrapreneur belongs.
As for the relationships between risk/uncertainty aversion9 and gender, existhave less entrepreneurial self-eﬃcacy and are more risk-averse. Finally, Moriano, Molero, Topa,and Lévy Mangin (2014) examine how managerial leadership styles aﬀect intrapreneurial behavior
and find that transformative leadership, in which, for example, a mission is shared, mentoring isprovided, and innovative thinking is encouraged, is more eﬀective to intrapreneurship than transactional leadership, in which, for example, employees are extrinsically incentivized, and job scopes
are predetermined.
9
In this paper, we do not distinguish between risk aversion and uncertainty aversion. Skepticalviews about this distinction can be found in Schultz (1980), LeRoy and Singell (1987), and Demsetz(1988). Runde (1998) studies the wordings of Knight’s (1921) exposition.

7

ing studies in experimental economics have repeatedly found strong evidence thatwomen are 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).10
In particular, Johnson and Powell (1994) find that gender diﬀerences with respect
to risk attitudes are quite small in the managerial subsample, whereas in the nonmanagerial subsample, women show more risk aversion than men. At the same time,
however, entrepreneurship gives women great autonomy, and this especially benefits
them, depending on their family structure (Edwards and Field-Hendrey (2002) andLombard (2001)). Moreover, employers and co-workers may discriminate against
female workers (Becker (1957)). Employers may also have prejudices against female
workers that they are less capable or less reliable on average than male or white
workers (Phelps (1972)). Thus, they may face disadvantages in the labor market.
Hypothesis 1 (Gender and Entrepreneurship). Women are more likely to become entrepreneurs 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 entrepreneurship entails or they face more severe challenges that make it diﬃcult for them to become entrepreneurs, such as credit constraints or discrimination.
We expect that women are less likely to become intrapreneurs.
trapreneurship still entails greater risk than wage work does.

First, in-

In addition, in-trapreneurs may have to spend more time for the organization. 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. In addition, women may
be treated unequally in the workplace (Cotter, Hermsen, Ovadia and Vanneman(2001) and Elliott and Smith (2004)). It may also be that men are in a more advantageous position for intrapreneurship. For all these reasons, we have the following
10

Croson and Gneezy (2009) point out the following three reasons for these gender diﬀerences: (i)emotions (according to psychological studies, women react to uncertain situations more emotionallyand fear adverse outcomes more than men do), (ii) overconfidence (men are more overconfident
than women), and (iii) perception of risk as challenges or threats.

8

hypothesis:
Hypothesis 2 (Gender and Intrapreneurship). Women who work for an established organization are less likely to become intrapreneurs.
Lastly, one can think of the two problems as interrelated. In the model below,
we consider this possibility by allowing correlation between unobserved factors in
the choice of entrepreneurship and those in the choice of intrapreneurship. These
unobserved factors would conceivably be related to “entrepreneurial skills/talents”(Lucas (1978)) in general.

4

Data

4.1

Sample Construction

The data for this study is constructed from the Panel Study of Entrepreneurial
Dynamics II (PSED II), provided by the Survey Research Center at the Universityof Michigan.11 Intended to be nationally representative, PSED II is a longitudinal
dataset that comprises individuals in the process of business formation (i.e., nascententrepreneurs), 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. Their age is recorded as a categorical variable,
ranging from “18 to 20” and “75 and up”. Then, follow-up interviews were conducted
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 for PSED
II.” Originally, it had 31,845 individuals, including those who are currently businessowners (that is, those who answer “yes” to the question, “Are you, alone or withothers, currently the owner of a business you help manage, including self-employment
11

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).

9

or selling any goods or services to others?” (QFF1c)). They represent 14.4% of thetotal or 4,573 individuals. Because our conceptual framework (presented in the next
section) targets those who are currently working for an established organization,
we exclude current business owners, other races, retirees, and the non-employed.
That leaves us with 13,724 individuals. To help us define nascent entrepreneurs andnascent intrapreneurs, we use the following two questions:
1. “Are you, alone or with others, currently trying to start a new business, including any self-employment 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 deemeda nascent entrepreneur (see Table 1). Nascent intrapreneurs are those who answer“yes” to QFF1b. If a respondent answers “no” to both questions, he is neithera nascent entrepreneur nor an intrapreneur. Thus, we have three categories: (i) anascent entrepreneur (631 individuals), (ii) a nascent intrapreneur (622 individuals),and (iii) not involved in a start-up (12,471 individuals).
Table 1: Types of Start-up Participants
Answer to
QFF1a

Yes
No

QFF1b
Yes
Nascent
Intrapreneurs (197)Nascent
Intrapreneurs (425)No
Nascent
Entrepreneurs (631)Notinvolved(12,471)

Furthermore, among these nascent entrepreneurs, only those who answer positively to the following two questions are deemed real nascent entrepreneurs: (i)“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
10

launch a business?” (QFF2) and (ii) “Will you personally own all, part, or none ofthis 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 suchindividuals is 370 and the rest (252 individuals) are categorized as not being not involved in a start-up. The final sample size is 13,724. Unfortunately, in the screening
process to determine nascent business starters (entrepreneurs in PSEDII language),
information on work experience is not collected. Thus, age is interpreted as a roughmeasure of work experience. As for income, we transform categorical values into
continuous values, ranging from $10,000 to $125,000. We then take the logarithmof these values.12
Unfortunately, PSED II also misses the size of the firm for which an individual
works. This is important because the meaning of intrapreneurship will change,depending on the firm size. 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.13 We also add anotherimportant piece of information, which is whether the respondent is US-born. Thisis because, race, which would presumably be an important factor in the context ofentrepreneurship and intrapreneurship, would matter diﬀerently if we do or do notcontrol for whether English is the individual’s first language.

4.2

Summary Statistics

Table 2 shows the summary statistics of all 13,724 individuals in the entire sample
for each (exclusive) occupational mode. All variables are dummy variables (taking
12

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.
13This 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).

11

0 or 1) except “household size” and “income” (as well as “unemployment rate”,“homestead exemption”, “median home price,” and three tax rates; we will explainthese variables when we discuss identification of our model in the next section). As
explained above, age is used as a categorical variable in the original screening partof PSED II, and its categorization is arranged in the same manner as Parker (2011).
[Table 2]
Notably, the ratios of women are smaller for both the entrepreneurship andintrapreneurship groups.

The ratios of black individuals are higher in the en-

trepreneurship and intrapreneurship groups than in the no-involvement group. Thisis also true for Hispanic individuals. In the no-involvement group, 37% are aged
18 to 34, whereas 45% of the nascent entrepreneurs and 49% of the nascent intrapreneurs are 18 to 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 6% for theentrepreneurs and 8% for the intrapreneurs. These numbers imply that the groupsof business starters consist of younger individuals.
Regarding education, the ratio of individuals with some college education isparticularly high for entrepreneurship. Both in the no-involvement and the intrapreneurship groups, college graduates (including those with postgraduate degrees) account for about 40%. 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 1,000 workers or more. Note also that 40–50% work for organizationsof 100 workers or less in each group.
As for household variables, the number of household members is the highest for
the entrepreneurship group. The ratio of child presence (under age 11) is also higher
in the entrepreneurship and intrapreneurship groups. Recall that these two groups
consist of young individuals, although the ratio of married individuals is the highest
and the family size is the lowest in the no-involvement group. The mean income isthe highest among nascent intrapreneurs ($66,800), followed by non business starters
12

($65,700) and the nascent entrepreneurs ($64,700).
Table 3 presents the means of variables for each gender.14 The average householdincome of male interviewees ($67,800) is higher than that of female interviewees($63,700). While the ratios of entrepreneurship and intrapreneurship are similar
for males (4.1% and 4.3%, respectively), the ratio of female entrepreneurship (2%)
is higher than that of female intrapreneurship (1.8%). The ratio of women with acollege degree or more among all women (43%) is higher than the corresponding
ratio for men (37%). The ratio of women working part-time (24%) is much higher
than that of men (13%).
[Table 3]

5

Empirical Analysis

In this section, we first explain the double selection model. After reporting theestimates of the model, we allow interactions of gender with other control variables
and show the estimates of the alternative models. Finally, we show the results fromthe Blinder–Oaxaca decomposition.

5.1

Estimates of the Double Selection Model

We now propose and estimate a selection model based on the following conceptual framework. First, an individual chooses whether to work independently. If
he chooses this option, he is called an entrepreneur.15 If he does not become anentrepreneur, then he chooses whether to become an intrapreneur. The individual
chooses one of the three alternatives that gives him the best utility (see footnote 3
above).

14

The reason for statistical significances in the age groups can be ascribed to the fact that on
average women live longer than men do.
15
In line with our conceptual framework described above, our empirical analysis does not make aclear distinction between the self-employed and business owners, and treats them as entrepreneurs.
In addition, the qualification “nascent” is dropped for simpler expressions.

13More formally, let di ∈ {0, 1}, where di = 1 indicates individual i choosing to opt
out from working independently, and di = 0 indicates i becoming an entrepreneur,and let li ∈ {0, 1} denote whether individual i, conditional on di = 1, becomes anintrapreneur (li = 1) or not (li = 0). If individual i chooses di = 0, then his utility
is written asui = α0 + α1 f emalei + x′i α + ϵ1i ,(1)where f emalei is a dummy variable that indicates individual i’s gender, and xi
and ϵ1i include other control variables and all unobservable factors, respectively.
Similarly, individual i’s utility as an intrapreneur is written asvi = β0 + β1 f emalei + z′i β + ϵ2i ,(2)where zi indicates control variables, and ϵ2i collects all unobserved factors, while
he obtains (normalized) zero utility from li = 0. Thus, individual i, conditional on
di = 1, becomes an intrapreneur (i.e., li = 1) if and only if vi ≥ 0. Knowing thisorder structure, individual i first chooses entrepreneurship (i.e., di = 0) if and only
if ui ≥ vi .
For identification of the parameters, it must be that xi ̸= zi (i.e., the exclusion restriction). In this study, we assume that zi ⊂ xi and that (xi − zi ) 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(xi − zi ). These two variables vary across states, and are assumed to provide exogenous variations. The existing studies stress that capital constraints would prevent
potential entrepreneurs from starting up activities.16 However, capital constraints
would be much less relevant when an individual does not work independently. Thisis the economic justification 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 A1 formore details on these variables.
16

See the references in footnote 4, as well as Fan and White (2003), Berkowitz and White (2004),
Paik (2013), Rohlin and Ross (2016), and Cerqueiro and Penas (2014) for bankruptcy exemption
and entrepreneurship, and Blanchflower and Oswald (1998), Taylor (2001), Schmalz, Sraer, andThesmar (2013), and Adlino, Schoar, and Severino (2015) for housing and entrepreneurship.

14

We further assume that (ϵ1i , ϵ2i ) is distributed identically and independently
across individuals, and is independent of (f emalei , xi ) and (f emalei , zi ).17 The
distribution is bivariate normal with mean (0, 0), and we allow for correlation between ϵ1i and ϵ2i , with the correlation coeﬃcient denoted by ρ. Then, the parametersof equations (1) and (2) are jointly obtained by the maximum likelihood estimation.
In the estimation, observations that have a missing answer are dropped.
5.1.1

Selection Equation (1)The estimation results of the selection equation (1) are presented in Table 4 (Table A2.1 in Appendix A2 shows the parameter estimates). The diﬀerence between
Specifications 1 and 2 is that in Specification 2, gender and race are interacted.
First, Specification 1 shows that the coeﬃcients for women are positive with
1% statistical significance, meaning that women are, ceteris paribus, less likely to
become entrepreneurs. This result supports the idea in Hypothesis 1 that women are
eager to avoid entrepreneurial risk or face more severe credit constraints or discrimination. In other words, the benefits from autonomy and flexibility do not outweighthese costs and the ineﬃciency losses. In contrast with our prior expectation, marriage, household size, and the presence of children have no such significant eﬀects.
This also contradicts Noseleit’s (2014) study that uses data from European countries
and finds that having a child raises women’s probability of becoming self-employed,
whereas self-employment itself does not induce fertility.18[Tables 4 and 5]
Regarding other control variables, middle-aged individuals are more likely to become entrepreneurs. The relationship between age and entrepreneurship is known as
17

We do not use household income as an explanatory variable in fear of its possible correlation
with ϵ1i or ϵ2i . Furthermore, to consider possible correlations between the covariates and theunobservables, we also conduct a propensity score matching estimation of equations (1) and (2).
Overall, the results do not change substantially. Estimation results are available upon request.
18
See Okamuro and Ikeuchi (2012) for a study of the relationship between women’s selfemployment and work-life balance. Interestingly, Rybczynski (2015) finds that the number ofchildren is the most important factor that determines the continuation of women’s self-employment.

15

an inverse U-shape (Lévesque and Minniti (2006) and Kautonen, Down and Minniti(2014)). Here the inverse U-shape relationship is also found in Table 4: starting from“age 18 to 24” (the baseline is “age 55 or higher”), the highest absolute value of thecoeﬃcient is achieved at “age 55 or higher,” and a lower value is observed for “age45 to 54” in each specification (see also the corresponding estimates in Table A2.1).
Now, turning to education, we find that the coeﬃcients for the education variables(the baseline is “high- school dropout”) are all positive, and individuals with postgraduate education are less likely to choose entrepreneurship than the high-school
dropout, and this diﬀerence is 5% statistically significant.
Regarding work status, part-time workers are more likely to choose entrepreneurship, and the estimates are 10% statistically significant. The coeﬃcient for internet
usage works positively for entrepreneurship in all the models, with 1% statistical significance. This finding is consistent with Fairlie (2006), who argues that computeruse is positively related to entrepreneurship not only for those who work in the IT
industry but also for others in general. The coeﬃcients of homestead exemption
and median home value are negative with 10% statistical significance. However,
unemployment rate and tax rates have no significant eﬀects.
Recall that Specification 2 includes dummies for interactions of gender and racesuch as “white-female”, “black-male” and so on (with the baseline being “whitemale”). It is shown that the estimated coeﬃcient for “white-female” is positive (i.e,negative for entrepreneurship) and is 1% statistically significant, and “Hispanicfemale” also have positive signs. On the other hand, “black-male” has a negative
coeﬃcient with 1% statistical significance (the estimate for “Hispanic-male” is also
negative). Thus, statistical significance indicates that among white individuals,
women are less likely to choose entrepreneurship, whereas among black individuals,
males are more likely to do so. These results imply that women are more risk averse
and thus are likely to stay in the wage sector.

165.1.2

Selection Equation (2)In specification 1 in Table 5 (Table A2.2 in Appendix A2 shows the parameter estimates), the negative coeﬃcients for females are 1% statistically significant, implyingthat women who work for an existing organization are, ceteris paribus, less likely to
become intrapreneurs, remaining as employees. This finding supports Hypothesis
2. It suggests that women may not only be risk averse but also be in a disadvantageous position in the workplace. Next, marriage has no significant eﬀects as inentrepreneurship. Moreover, the estimated coeﬃcients for the household size andthe presence of children under age 11 are not statistically significant. This result,
together with the estimated coeﬃcients for household variables such as marriage onentrepreneurship, implies that household variables are not significantly related toentrepreneurship or intrapreneurship.
Regarding the age eﬀects, young workers are more likely to become intrapreneurs
than older workers, as opposed to the case of entrepreneurship. Thus, up to age 55,
the U-shape relationship holds for intrapreneurship. Next, individuals who use theinternet are more likely to become intrapreneurs. This may imply that computer
skills would be useful for both entrepreneurship and intrapreneurship. We also findthat part-time workers are less likely to choose intrapreneurship.
In Specification 2, which takes into account the interactions of gender and race,
we find that the estimated coeﬃcients for “white-female” are negative with 1% statistical significance, and that “black-female” and “Hispanic-female” also have negative
signs. In contrast, the estimated coeﬃcients for “black-male” and “Hispanic-male”
are positive, but not statistical significant. These results imply that gender is still
an issue that puts women in a disadvantageous position in the workplace, whereas
race matters less.
Notice that under all of the six specifications, the estimated correlation coeﬃcients between the unobservables (ϵ1i in equation (1) and ϵ2i in equation (2)) are close
to 0.9 and statistically significant. Recall that in our double selection model (i.e.,equations (1) and (2)). a low value of ϵ1i favors entreprenuership, and a high value of
ϵ2i favors intrapreneurship. That is, our positive estimates for ρ suggest that what
17

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 alternatives equally as in a multinominal logit model), the parameter estimates would be
biased, and predicted rates of entrepreneurship and intrapreneurship under counterfactual scenarios would be imprecise. As in Parker (2011), this justifies our double
selection model. Notice also that Specification 1 yields a smaller value of Akaike’s
Information Criterion (AIC; see Table A2.2 in Appendix A2). Thus, Specification 1
is preferred.19

5.2

Interactions of Gender with Control Variables

The coeﬃcients for controls such as human capital variables may diﬀer across genders. To consider this possibility, we interact gender with these variables.
5.2.1

Selection Equation (1)Table 6 presents the estimation results of selection equation (1) in consideration ofgender and family-related variables (Table A2.3 in Appendix A2 shows the parameter estimates). Table 8, on the other hand, considers the interactions of gender andeducation (Specification 5), and gender and financial environments (Specification
6). Table A2.5 in Appendix A2 shows the corresponding parameter estimates.
[Tables 6, 7, 8, and 9]
Table 6 shows, again, that the presence of children and the size of the household
do not matter to the choice of entrepreneurship across genders. On the other hand,Specification 5 in Table 8 shows that women are, as they acquire higher education,
19

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 less than 100
workers, and with a subsample of the others. We also conduct the same exercise by dividing thesample into those who work for a firm with less 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.

18

more likely to choose entrepreneurship. 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, so those with higher education may be more inclined toward
entrepreneurship than intrapreneurship. It could also imply that there are few
gender diﬀerences among individuals with high education in attitude toward risk,
as suggested by Johnson and Powell (1994).
5.2.2

Selection Equation (2)Table 7 shows the estimation results of the outcome equation (2) (Table A2.4 inAppendix A2 shows the parameter estimates). As in the case of entrepreneurship,
the presence of young children and the size of the household do not show statisticalsignificance in diﬀerences in the choice of intrapreneurship across genders. In Specification 5 in Table 9 (the corresponding parameter estimates are shown in Table
A2.6 in Appendix A2), the interaction terms of gender and education are no longer
statistically significant. That is, it is not necessarily the case that as women become more educated they are more likely to become intrapreneurs. Notice also thatSpecification 1 yields the lowest value of AIC. Thus, we use Specification 1 for thecounterfactual experiments and the Blinder–Oaxaca decomposition below.
In summary, we find that women are less likely to choose entrepreneurshipmainly because of their aversion to risk, credit constraints or discrimination, sacrificing autonomy and flexibility. However, women with higher education are more
likely to become entrepreneurs. We also find that women who work in established organizations are less likely to become intrapreneurs. Interestingly, education does notmatter much to intrapraneurship. It could be inferred that women may face morediﬃculty in the workplace than in the marketplace. Lastly, we find that household
status is not strongly related to entrepreneurship or intrapreneurship.

19

5.3

Counterfactual Experiments and the Blinder–Oaxaca
Decompositions

To consider further the interactions of gender and start-up activities, we computethe women’s actual and predicted probabilities of becoming entrepreneurs and intrapreneurs when they become the average men (i.e., in each simulation, eachwoman’s covariates are drawn from the estimated distribution of the covariates formen). We also show the results from the Blinder–Oaxaca decomposition (Blinder (1973), Oaxaca (1973), Oaxaca and Ramson (1994), Fairlie (2005), and Fortin,
Lemiuex, and Firpo (2011)). It decomposes the gender diﬀerences in the averagerate of becoming an entrepreneur or intrapreneur into the characteristics’ eﬀect andthe coeﬃcients’ eﬀect as given below:
Ym − Yf = Pr(βˆ∗ , Xm ) − Pr(βˆ∗ , Xf )|{z}characteristics’ eﬀect+Pr(βˆm , X ∗ ) − Pr(βˆf , X ∗ ),

|

{z

}

coeﬃcients’ eﬀect

where Ym − Yf expresses the gender gap in entrepreneurship or intrapreneurship, Pr
denotes the average predicted probability, βˆ∗ = Ωβˆm + (I − Ω)βˆf , with Ω being aweighting matrix, βˆm and βˆf being he parameter estimates in the male sample, andfemale sample, respectively, and finally, X ∗ = (I − Ω)Xm + ΩXf , with Xm and Xf
representing the observed characteristics of men and women, respectively.
5.3.1

EntrepreneurshipFirst, Panel A of Table 10 displays the actual probabilities of choosing entrepreneurship by gender in the diagonal cells and the counterfactual probabilities in the nondiagonal 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 entrepreneuris 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
are identical to that of men’s, they are less likely to choose entrepreneurship by 0.2%
points (this is not statistically significant, either). These two counterfactual scenar20

ios suggest that the female characteristics favor entrepreneurship. However, column“male” shows that even if the distribution of women’s characteristics xi is identicalto that of men’s (except f emalei ), women are less likely to become entrepreneurs
than men, and this diﬀerence (1.6%) is 1% statistically significant. Column “male”
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 bein a disadvantageous position when becoming entrepreneurs. In this sense, policies
for promoting entrepreneurship with an emphasis on women would be justified, as
the U.S. SBA currently emphasizes (see the first paragraph of Introduction). For
example, if the mismatching of nascent entrepreneurs and start-up assistance programs is, as found by Yusuf (2010), serious, policies would be better oﬀ focusing on
female entrepreneurship.
[Table 10]
Hundley (2000) finds that women’s earnings from self-employment are aﬀectedby family size and composition, and argues that women will choose to be selfemployed to devote more time to housework. However, in our study, we do notfind that family size and composition matter to female entrepreneurship or female
intrapreneurship.20 This result is also apparent in the results from the Blinder–
Oaxaca decomposition (see Tables 11, 12, and 13). According to Table 11, column“total,” the observed characteristics works to reduce the observed gender gap inentrepreneurship (1.4%) by 20.3%. The characteristics eﬀect has a negative contribution, suggesting that the observed characteristics of women are more likely to
become entrepreneurship than the observed characteristics of men. Thus, the observed gender gap in entrepreneurship increase from 1.4% to 1.68% (1.4 − (−0.28))
if men have the same characteristics as women. The eﬀect of the gender diﬀerencein part-time work itself contributes 9.2% of these countervailing eﬀects. These two
20
For other studies on gender diﬀerences in entrepreneurship, see Fairlie and Robb (2009), Leoni
and Falk (2010), and Bönte and Piegeler (2013).

21

findings are more apparent in the higher education group. However, a similar effect is not found in the lower education group. Compared with the eﬀects frompart-time work, family-related variables have more modest eﬀects. This would suggest that women’s employment status is much more important to the gender gap inentrepreneurial activities.
Table 12 shows that a similar result is obtained for both the higher educationgroup and the white group. Interestingly, the opposite result is found for the blackgroup; the part-time diﬀerence contributes positively to the gender gap. In the
Hispanic group, the observed characteristics explain 67% of the gender gap, andthe family related variables are more important than the part-time variables in
explaining the gender gap. Table 13 shows that in the smaller firm-size group, thediﬀerence in the variable for high-school graduate works to reduce the gender gap.
On the other hand, in the larger firm-size group, the part-time variable reduces thegender gap by 61.7% of all the included variables (= (−14.9)/(−24.0)).
[Tables 11, 12, and 13]
5.3.2

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 intrapreneur.
Importantly, if a woman has the same characteristics as men’s, her likelihood of becoming an intrapreneur would be 1.7%, higher than the actual rate of 1.6%, although
this diﬀerence is not statistically significant. On the other hand, the opposite is true
for men (see row “male”). These two counterfactual scenarios suggest that, in contrast to entrepreneurship, the female characteristics do not favor intrapreneurship.
More importantly, both columns “male” and “female” suggest that women may also
be in a disadvantageous position when becoming intrapreneurs. The male–female
diﬀerence is 2.2% points if he distribution of women’s characteristics xi is identicalto that of men’s (except f emalei ), and this diﬀerence is 1% statistically significant.

22Comparison within column “female” gives a similar result.21
Lastly, Tables 14, 15, and 16 show the results from the Blinder–Oaxaca decomposition. Table 14 shows that the part-time variable contributes to the gender gap in
intrapreneurship more in the higher education group. Family-related variables have,again, small eﬀects, suggesting that women’s employment status is much more important to the gender gap in intrapreneurial activities as well. Table 15 indicatesthat in the black and the Hispanic groups, the age variables have large eﬀects. In
the Hispanic group, the presence of children explain a large part of the gender gap,
possibly implying a characteristic feature of Hispanic intrapreneurship because thisis not observed either in the white or in the Hispanic group. Finally, it is interesting
to see the contrast between the large firm-size group and the small firm-size groupin Table 16. In the larger firm-size group, most of the explained diﬀerences arebecause of the part-time diﬀerence. This is in contrast with the smaller firm-sizegroup. In both groups, family-related variables are not dominant in these explained
diﬀerences.
[Tables 14, 15, and 16]

6

Concluding Remarks

This study examines how gender matters to entrepreneurship and intrapreneurship.
Our counterfactual experiments suggest that the rate of entrepreneurial activities
21

Notice that it is possible to compute the actual and counterfactual (when all women acquire thesame chanaracteristics as men) rates of the non-involved for men and for women in the following
way:

Entrepreneurs
Intrapreneurs
Non-involved

Male
FemaleMale
FemaleMale
FemaleActual (%)
3.56
2.16
3.85
1.62
42.59
46.21

Counterfactual (%)
1.99
1.67
46.34

However, it is not possible to predict how the three rates for men would change because we donot model interactions among individuals.

23

by women, who acquire the same (in the disributional sense) characteristics as men,
is lower than the rate of men’s entrepreneurial activities. Similarly, the rate of
intrapreneurial activities by women with the same characteristics as men will belower than the rate of men’s intrapreneurial activities. These two findings suggestthat women may be in a disadvantageous position when becoming entrapreneurs
and intrapreneurs. In addition, our Blinder–Oaxaca decomposition results suggestthat for both entrepreneurship and intrapreneurship, women’s employment status
is much more important than their household status to the gender gap in start-up
activities. This is more apparent in the group of whites, college graduates, andthose who work for a large firm. An interesting diﬀerence between entrepreneurshipand intrapreneurship is that while the female characteristics favor entrepreneurship,
they do not favor intarepreneurship, although these two results are not statisticallysignificant.
If the government aims to reduce the gender gap in start-up activities, ourresults would imply that the government should focus on improving workplace conditions for women, rather than implementing family-related policies. However, one
should be very careful about deriving policy implications from our results because
we are silent on the performances of start-up activities. In particular, it would be
diﬃcult to measure the performances of intrapreneurial activities: the process andthe performance of an intrapreneurial activity should be recorded, and a suﬃcientnumber of such observations should be available to researchers.
To measure the performance of start-up activities, it would be interesting tostudy how gender matters to the duration of intrapreneurship. In the context ofracial diﬀerences in entrepreneurship, Fairlie (1999) finds that blacks are more likely
to exit self-employment than whites. Ahn (2011) also finds that the reason the
duration of minority self-employment is shorter than that for white self-employment
is mainly the lack of work experience prior to entry into self-employment. Is there agender gap in terms of the duration of intrapreneurship? This and other important
issues await future research to deepen our understanding of start-up activities in a
broader sense.

24

Appendix
A1. 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 information is based on Table 1 of Corradin, Gropp, Huizinga, and Laeven (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-occupied housing units). The state-specific unemployment rate is the annual
average in 2005 (available at the Webpage of the U.S. Bureau of Labor Statistics22 ). 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). Following Rohlin and Ross
(2016), we use the highest marginal rate for individual income and corporate income
taxes.
Table A1 presents the state-level data for the financial environment. All thesevariables have suﬃcient variations. Table A2 shows that the correlations among
these variables are weak, except for the one between individual income tax andcorporate income tax. There are seven states that do not set an exemption level. In
Table A1, such a state is deemed “unlimited,” and in the empirical analysis below
we impute $500,000, the maximum amount from the rest of the states, for thesestates’ exemption level. The federal level of exemption in 2005 was $36,900, and forstates 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.23[Tables A1 and A2]
22
23

The URL is http://www.bls.gov/news.release/archives/srgune 03012006.pdf.
However, in 2006, Delaware set $50,000 for its homestead exemption.

25

A2. Parameter Estimates
In the main text, we present the estimates of the avergage (marginal for continuous
variables) eﬀects for each equation under each of the six specifications. Below, weshow the original parameter estimates.
[Tables A2.1-A2.6]

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35

Table 2: Summary Statistics: Across Modes
Not involved
MeanN
Female
0.512
Race
White
0.750
Black
0.117
Hispanic
0.133
Foreign born
0.120
Age
18 to 240.137
25 to 34
0.229
35 to 440.256
45 to 540.237
55 to 640.110
65 and more
0.031
Education
HS dropout
0.057
HS graduate
0.270
Some college
0.269
Bachelor
0.258
Postgraduate
0.146Internet
0.834
Employment Status
Full time
0.816
Part time
0.184
Firm size
Under firm size 24
0.312
Firm size 25 to 99
0.124
Firm size 100 to 499
0.125
Firm size 500 to 999
0.062
Firm size 1000 and more
0.378
Household
Married
0.598
Children under age 11
0.332
Size
3.006
Income65744.689
Non-metro area
0.226
Unemployment rate
5.074
Homestead exemption
129.770
Median home value
203.731
Maximum personal income tax rate
5.558
Maximum corporate income tax rate
6.686
Sales tax rate
5.321

Entrepreneurs
MeanN

Intrapreneurs
MeanN12, 9740.331∗∗∗ 380

0.297∗∗∗ 370

12, 63112, 63112, 631
10, 5100.643∗∗∗
0.204∗∗∗
0.154
0.098

368
368
368
324

0.633∗∗∗
0.174∗∗∗
0.194∗∗∗
0.153

353
353
353
311

12, 813
12, 813
12, 813
12, 813
12, 813
12, 8130.172∗
0.277∗∗
0.278
0.210
0.054∗∗∗
0.009∗∗∗

374
374
374
374
374
374

0.212∗∗∗
0.281∗∗
0.256
0.176∗∗∗
0.070∗∗∗
0.007∗∗∗

366
366
366
366
366
366

12, 837
12, 837
12, 837
12, 837
12, 837
12, 891

0.083∗
0.205∗∗∗
0.366∗∗∗
0.241
0.105∗∗∗
0.913∗∗∗

377
377
377
377
377
378

0.102∗∗∗
0.258
0.248
0.233
0.159
0.867∗

363
363
363
363
363
366

12, 974
12, 974

0.819
0.181

380
380

0.849∗
0.151∗

370
370

10, 510
10, 510
10, 510
10, 510
10, 510

0.348
0.140
0.095∗
0.049
0.368

324
324
324
324
324

0.335
0.096∗
0.111
0.054
0.404

311
311
311
311
311

12, 827
12, 806
12, 829
10, 74212, 974
12, 974
12, 974
12, 974
12, 974
12, 974
12, 974

0.545∗∗
0.418∗∗∗
3.296∗∗∗
64705.431
0.207
4.983∗∗
159.730∗∗∗205.834
5.202∗∗
6.444
5.292

378
377
377
336
380
380
380
380
380
380
380

0.530∗∗∗
0.386∗∗
3.167∗∗
66787.562
0.207
5.098
146.421∗206.447
5.395
6.761
5.417

364
362
364
323
370
370
370
370
370
370
370

Notes: Sample weights are used to calculate the means. The unit is $1,000 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 Not Involved.

36

Table 3: Summary Statistics: Across Genders
Mean
Not involved
Intrapreneurs
Entrepreneurs
Race
White
Black
Hispanic
Foreign bornAge
18 to 2425 to 34
35 to 44
45 to 54
55 to 64
65 and more
Education
HS dropout
HS graduate
Some college
Bachelor
Postgraduate
Internet
Employment Status
Full time
Part time
Firm size
Under firm size 24
Firm size 25 to 99
Firm size 100 to 499
Firm size 500 to 999
Firm size 1000 and moreHousehold
MarriedChildren under age 11
Size
IncomeNon-metro area
Unemployment rate
Homestead exemption
Median home value
Maximum personal income tax rate
Maximum corporate income tax rate
Sales tax rate

Male
N

Mean

Female
N

0.916
0.043
0.041

7, 213
7, 213
7, 2130.962∗∗∗
0.018∗∗∗
0.020∗∗∗

6, 511
6, 511
6, 511

0.744
0.102
0.154
0.118

7, 002
7, 002
7, 0025, 8910.743
0.141∗∗∗
0.117∗∗∗
0.122

6, 3506, 3506, 3505, 254

0.153
0.225
0.269
0.223
0.106
0.024

7, 137
7, 137
7, 137
7, 137
7, 137
7, 1370.128∗∗∗
0.240∗
0.244∗∗∗
0.246∗∗∗
0.109
0.034∗∗∗

6, 416
6, 416
6, 416
6, 416
6, 416
6, 416

0.072
0.286
0.268
0.242
0.132
0.818

7, 1307, 1307, 1307, 1307, 1307, 1630.046∗∗∗
0.249∗∗∗
0.275
0.271∗∗∗
0.159∗∗∗
0.856∗∗∗

6, 447
6, 447
6, 447
6, 447
6, 447
6, 472

0.873
0.127

7, 213
7, 2130.760∗∗∗
0.240∗∗∗

6, 511
6, 511

0.315
0.129
0.114
0.059
0.383

5, 891
5, 891
5, 891
5, 891
5, 8910.312
0.118∗
0.133∗∗∗
0.063
0.373

5, 254
5, 254
5, 254
5, 254
5, 254

0.604
0.341
3.070
67841.541
0.232
5.079
130.743201.510
5.489
6.672
5.362

7, 1407, 1207, 136
6, 0297, 213
7, 213
7, 213
7, 213
7, 213
7, 213
7, 2130.585∗∗
0.331
2.970∗∗∗
63653.488∗∗∗0.217∗∗
5.066
131.632
206.245∗∗5.595∗∗
6.691
5.283∗∗∗

6, 4296, 425
6, 434
5, 372
6, 511
6, 511
6, 511
6, 511
6, 511
6, 511
6, 511

Notes: Sample weights are used to calculate the means. The unit is $1,000 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.

37

Table 4: Average/Marginal Eﬀects in Equation (1)Specification 1
Dependent variable:
1 = Staying in
0 = Going out (Entrepreneurship)
FemaleRace: Ref = whiteBlack
Hispanic
Race: Ref = white maleWhite female
Black maleBlack female
Hispanic male
Hispanic female
Foreign born
Age: Ref = 55 and more
18 to 2425 to 34
35 to 44
45 to 54
Education: Ref = HS dropout
HS graduate
Some college
Bachelor
Postgraduate
Internet
Employment Status: Ref = full time
Part time
Firm size: Ref = under firm size 24
Firm size 25 to 99 (×102 )Firm size 100 to 499
Firm size 500 to 999
Firm size 1000 and moreHousehold
MarriedChildren under age 11
Size
Non-metro area
Unemployment rate (×102 )Homestead exemption (×102 )Median home value (×102 )Maximum personal income tax rate
Maximum corporate income tax rate (×102 )Sales tax rate
N

0.016∗∗∗(0.003)−0.025∗∗∗−0.004(0.005)(0.006)

Specification 2

0.014∗∗(0.006)0.015∗∗∗−0.029∗∗∗−0.005−0.0030.010
0.014∗∗−0.002−0.014∗∗−0.014∗∗−0.012∗∗(0.007)(0.006)(0.006)(0.005)−0.002−0.014∗∗−0.014∗∗−0.012∗∗(0.007)(0.006)(0.006)(0.005)0.023∗∗∗
0.009
0.018∗∗
0.018∗∗−0.023∗∗∗(0.007)(0.007)(0.007)(0.008)(0.005)0.023∗∗∗
0.009
0.018∗∗
0.018∗∗−0.023∗∗∗(0.007)(0.007)(0.007)(0.008)(0.005)−0.008∗(0.005)−0.008∗(0.005)

0.043
0.006
−0.0020.003(0.511)(0.006)(0.007)(0.004)

0.037
0.006
−0.0020.003(0.511)(0.006)(0.007)(0.004)0.006−0.004−0.002−0.001−0.009−0.002∗−0.003∗
0.001
0.033
0.002

(0.004)(0.004)(0.001)(0.004)(0.210)(−0.002)(0.002)(0.001)(0.074)(0.001)0.006−0.004−0.002−0.001−0.013−0.002∗−0.003∗
0.001
0.033
0.002

(0.004)(0.004)(0.001)(0.004)(0.209)(0.001)(0.002)(0.001)(0.074)(0.001)

11, 113

Notes: Average (for discrete variables) and marginal (for continuous variables) eﬀects;
Robust standard errors are in parentheses.
∗
p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01

38

11, 113(0.004)(0.007)(0.007)(0.007)(0.010)(0.006)

Table 5: Average/Marginal Eﬀects in Equation (2)Specification 1
Dependent variable:
1 = Intrapreneurship
0 = Else
FemaleRace: Ref = whiteBlack
Hispanic
Race: Ref = white maleWhite female
Black maleBlack female
Hispanic male
Hispanic female
Foreign born
Age: Ref = 55 and more
18 to 2425 to 34
35 to 44
45 to 54
Education: Ref = HS dropout
HS graduate
Some college
Bachelor
Postgraduate
Internet
Employment Status: Ref = full time
Part time
Firm size: Ref = under firm size 24
Firm size 25 to 99
Firm size 100 to 499
Firm size 500 to 999
Firm size 1000 and moreHousehold
MarriedChildren under age 11
Size
N−0.023∗∗∗(0.003)0.014∗∗∗
0.009∗(0.005)(0.005)Specification 2

0.006(0.005)−0.022∗∗∗0.017∗∗−0.0130.010−0.0140.005

0.025∗∗∗
0.016∗∗∗
0.009
0.009∗(0.007)(0.006)(0.006)(0.005)0.025∗∗∗
0.016∗∗∗
0.009
0.009∗(0.007)(0.006)(0.006)(0.005)−0.008−0.007−0.0080.025
0.010∗∗(0.007)(0.007)(0.007)(0.750)(0.005)−0.008−0.007−0.0080.036
0.010∗∗(0.007)(0.007)(0.007)(0.750)(0.005)−0.009∗(0.005)−0.010∗(0.005)−0.018∗∗∗−0.007−0.008−0.007∗(0.006)(0.005)(0.007)(0.004)−0.018∗∗∗−0.007−0.007−0.007∗(0.006)(0.005)(0.007)(0.004)−0.0060.004
0.001(0.004)(0.004)(0.001)−0.006∗
0.004
0.001

(0.004)(0.004)(0.001)

11, 113

11, 113

Notes: Average (for discrete variables) and marginal (for continuous variables) eﬀects;
Robust standard errors are in parentheses.
∗
p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01

39

(0.004)(0.007)(0.008)(0.006)(0.010)(0.005)

Table 6: Average/Marginal Eﬀects in Equation (1)Dependent variable:
1 = Staying in
0 = Going out (Entrepreneurship)
FemaleRace: Ref = whiteBlack
Hispanic
Foreign born
Age: Ref = 55 and more
Age 18 to 24
Age 25 to 34
Age 35 to 44
Age 45 to 54
Education: Ref = HS dropout
HS graduate
Some college
Bachelor
Postgraduate
Internet
Employment Status: Ref = full time
Part time
Firm size: Ref = under firm size 24
Firm size 25 to 99 (×102 )Firm size 100 to 499
Firm size 500 to 999
Firm size 1000 and moreHousehold
MarriedChildren under age 11Children under age 11 × Female
Size
Size × Female
Non-metro area
Unemployment rate (×102 )Homestead exemption (×102 )Median home value (×102 )Maximum personal income tax rate
Maximum corporate income tax rate (×102 )Sales tax rate
N

Specification 3

Specification 40.018∗∗∗(0.004)0.020∗∗∗(0.007)−0.025∗∗∗−0.0040.014∗∗(0.005)(0.006)(0.006)−0.025∗∗∗−0.0040.014∗∗(0.005)(0.006)(0.006)−0.002−0.013∗∗−0.014∗∗−0.012∗∗(0.007)(0.006)(0.006)(0.005)−0.002−0.013∗∗−0.014∗∗−0.012∗∗(0.007)(0.006)(0.006)(0.005)0.023∗∗∗
0.009
0.018∗∗
0.018∗∗−0.023∗∗∗(0.007)(0.007)(0.007)(0.008)(0.005)0.023∗∗∗
0.009
0.018∗∗
0.018∗∗−0.023∗∗∗(0.007)(0.007)(0.007)(0.008)(0.005)−0.008∗(0.005)−0.008∗(0.005)

0.042
0.006
−0.0020.003(0.511)(0.006)(0.007)(0.004)

0.037
0.006
−0.0020.003(0.511)(0.006)(0.007)(0.004)0.005−0.002−0.003−0.002(0.004)(0.005)(0.007)(0.001)

0.005−0.004(0.004)(0.004)−0.001−0.008−0.002∗−0.003∗
0.001
0.033
0.002

(0.004)(0.210)(0.001)(0.002)(0.001)(0.074)(0.001)−0.001−0.001−0.001−0.009−0.002∗−0.003∗
0.001
0.033
0.002

(0.002)(0.002)(0.004)(0.210)(0.001)(0.002)(0.001)(0.074)(0.001)

11, 113

Notes: Average (for discrete variables) and marginal (for continuous variables) eﬀects;
Robust standard errors are in parentheses.
∗
p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01

40

11, 113

Table 7: Average/Marginal Eﬀects in Equation (2)Specification 3
Dependent variable:
1 = Intrapreneurship
0 = Else
FemaleRace: Ref = whiteBlack
Hispanic
Foreign born
Age: Ref = 55 and more
Age 18 to 24
Age 25 to 34
Age 35 to 44
Age 45 to 54
Education: Ref = HS dropout
HS graduate
Some college
Bachelor
Postgraduate
Internet
Employment Status: Ref = full time
Part time
Firm size: Ref = under firm size 24
Firm size 25 to 99
Firm size 100 to 499
Firm size 500 to 999
Firm size 1000 and moreHousehold
MarriedChildren under age 11Children under age 11 × Female
Size
Size × Female
N

Specification 4−0.021∗∗∗(0.004)−0.021∗∗∗(0.007)0.014∗∗∗
0.009∗
0.006(0.005)(0.005)(0.005)0.014∗∗∗
0.009∗
0.006(0.005)(0.005)(0.005)0.025∗∗∗
0.017∗∗∗
0.009
0.009∗(0.007)(0.006)(0.006)(0.005)0.025∗∗∗
0.017∗∗∗
0.009
0.009∗(0.007)(0.006)(0.006)(0.005)−0.008−0.007−0.0070.000
0.010∗∗(0.007)(0.007)(0.007)(0.007)(0.005)−0.008−0.007−0.0080.000
0.010∗∗(0.007)(0.007)(0.007)(0.008)(0.005)−0.009∗(0.005)−0.009∗(0.005)−0.018∗∗∗−0.007−0.007−0.007∗(0.006)(0.005)(0.007)(0.004)−0.018∗∗∗−0.007−0.008−0.007∗(0.006)(0.005)(0.007)(0.004)−0.006∗0.005−0.0050.001(0.004)(0.005)(0.007)(0.001)−0.0060.004(0.004)(0.004)0.001−0.001(0.001)(0.002)

11, 113

11, 113

Notes: Average (for discrete variables) and marginal (for continuous variables) eﬀects;
Robust standard errors are in parentheses.
∗
p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01

41

Table 8: Average/Marginal Eﬀects in Equation (1)Dependent variable:
1 = Staying in
0 = Going out (Entrepreneurship)
FemaleRace: Ref = whiteBlack
Hispanic
Foreign born
Age: Ref = 55 and more
Age 18 to 24
Age 25 to 34
Age 35 to 44
Age 45 to 54
Education: Ref = HS dropout
HS graduate
Some college
Bachelor
Postgraduate
HS graduate × Female
Some colleage × Female
Bachelor × Female
Postgraduate × Female
Internet
Employment Status: Ref = full time
Part time
Firm size: Ref = under firm size 24
Firm size 25 to 99 (×102 )Firm size 100 to 499
Firm size 500 to 999
Firm size 1000 and moreHousehold
MarriedChildren under age 11
Size
Non-metro area
Unemployment rate (×102 )Homestead exemption (×103 )Homestead exemption × Female (×102 )Median home value (×102 )Median home value × Female (×102 )Maximum personal income tax rate
Maximum corporate income tax rate (×102 )Sales tax rate
N

Specification 5

Specification 6

0.048∗∗∗(0.016)0.023∗∗∗(0.008)−0.025∗∗∗−0.0040.014∗∗(0.005)(0.006)(0.006)−0.026∗∗∗−0.0040.014∗∗(0.005)(0.006)(0.006)−0.002−0.013∗∗−0.014∗∗−0.012∗∗(0.007)(0.006)(0.006)(0.005)−0.002−0.014∗∗−0.014∗∗−0.012∗∗(0.007)(0.006)(0.006)(0.005)0.034∗∗∗
0.014∗
0.026∗∗∗
0.026∗∗∗−0.040∗∗−0.029∗−0.034∗∗−0.034∗−0.023∗∗∗(0.008)(0.008)(0.009)(0.009)(0.017)(0.017)(0.017)(0.018)(0.006)0.023∗∗∗
0.009
0.018∗∗
0.018∗∗(0.007)(0.007)(0.007)(0.008)−0.023∗∗∗(0.005)−0.008∗(0.005)−0.008∗(0.005)

0.041
0.007
−0.0020.003(0.512)(0.006)(0.007)(0.004)

0.023
0.006
−0.0020.003(0.510)(0.006)(0.007)(0.004)0.006−0.004−0.002−0.001−0.015−0.017∗(0.004)(0.004)(0.001)(0.004)(0.211)(0.010)−0.003∗(0.002)

0.001
0.031
0.002

(0.001)(0.074)(0.001)

0.005−0.004−0.002−0.001−0.006−0.004−0.003∗−0.002−0.001
0.001
0.037
0.002

(0.004)(0.004)(0.001)(0.004)(0.210)(0.012)(0.002)(0.002)(0.003)(0.001)(0.074)(0.001)

11, 113

Notes: Average (for discrete variables) and marginal (for continuous variables) eﬀects;
Robust standard errors are in parentheses.
∗
p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01

42

11, 113

Table 9: Average/Marginal Eﬀects in Equation (2)Specification 5
Dependent variable:
1 = Intrapreneurship
0 = Else
FemaleRace: Ref = whiteBlack
Hispanic
Foreign born
Age: Ref = 55 and more
Age 18 to 24
Age 25 to 34
Age 35 to 44
Age 45 to 54
Education: Ref = HS dropout
HS graduate
Some college
Bachelor
Postgraduate (×101 )HS graduate × Female
Some colleage × Female
Bachelor × Female
Postgraduate × Female
Internet
Employment Status: Ref = full time
Part time
Firm size: Ref = under firm size 24
Firm size 25 to 99
Firm size 100 to 499
Firm size 500 to 999
Firm size 1000 and moreHousehold
MarriedChildren under age 11
Size
N

Specification 6−0.030∗∗(0.014)−0.023∗∗∗(0.003)0.014∗∗∗
0.009∗
0.006(0.005)(0.005)(0.005)0.014∗∗∗
0.009∗
0.006(0.005)(0.005)(0.005)0.024∗∗∗
0.016∗∗∗
0.008
0.009∗(0.007)(0.006)(0.006)(0.005)0.025∗∗∗
0.016∗∗∗
0.009
0.009∗(0.007)(0.006)(0.006)(0.005)−0.009−0.008−0.011−0.028
0.004
0.006
0.012
0.011
0.010∗∗(0.008)(0.008)(0.008)(0.087)(0.016)(0.016)(0.016)(0.016)(0.005)−0.008−0.007−0.0070.003

0.010∗∗(0.005)−0.009∗(0.005)−0.009∗(0.005)−0.018∗∗∗−0.007−0.007−0.007∗(0.006)(0.005)(0.007)(0.004)−0.018∗∗∗−0.007−0.008−0.007∗(0.006)(0.005)(0.007)(0.004)−0.0060.004
0.001(0.004)(0.004)(0.001)−0.0060.004
0.001(0.004)(0.004)(0.001)

11, 113

11, 113

Notes: Average (for discrete variables) and marginal (for continuous variables) eﬀects;
Robust standard errors are in parentheses.
∗
p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01

43

(0.007)(0.007)(0.007)(0.075)

Table 10: Actual and Counterfactual Rates of Start-Up Activities
Char

Male

FemaleMale

3.56%(0.0024)3.83%(0.0003)0.27%

Female1.99%(0.0002)

2.16%(0.0020)−0.16%

Diﬀerence−1.57%∗∗∗

1.67%∗∗∗

Male

3.85%
(0.0025)3.75%(0.0002)−0.09%

Female1.67%(0.0001)

1.62%(0.0017)

0.05%

Diﬀerence−2.18%∗∗∗

2.13%∗∗∗

Gender

Diﬀerence

Panel A: Pr(Entrepreneur)

Panel B : Pr(Intraperneur)

Notes: Standard errors in parentheses. Diagonal cells are actual rates, and nondiagonal cells are counterfactual rates.
For example, the (male, female) cell in Panel A means that if all men’s characteristics are drawn from the distributionof 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

44

Table 11: Decompositon of Gender Gap in Entreperneurship: Across Educational Attainments
Total

Bachelor or More

Some College or Less

Male mean (%)

3.56

3.29

3.72

Female mean (%)

2.16

2.26

2.17

Male − Female gap (%)

1.40

1.03

1.55

Contributions from gender diﬀerences in:

N

Black

−0.0009766−6.98%−0.0011176−10.84%−0.0009787−6.30%

Hispanic

0.0000848

0.61%

0.0001494

1.45%−0.0002022−1.30%

Foreign born

0.0000771

0.55%

0.0000405

0.39%

0.0000717

0.46%

Age 18 to 24

0.0000660

0.47%−0.0001039−1.01%

0.0004668

3.01%

Age 25 to 34

0.0000569

0.41%

0.0001696

1.65%

0.0009404

6.06%

Age 35 to 44

0.0006996

5.00%

0.0003778

3.66%

0.0009349

6.02%

Age 45 to 54−0.0005886−4.20%−0.0000975−0.95%−0.0012276−7.91%

HS graduate−0.0008346−5.96%−0.0006838−4.40%

Some college

0.0001464

1.05%

0.0005426

3.49%

Bachelor

0.0005923

4.23%

Postgraduate

0.0001902

1.36%

0.0000006

0.01%Internet−0.0010313−7.37%−0.0027611−26.79%−0.0011653−7.51%Part time−0.0012970−9.27%−0.0016377−15.89%−0.0011066−7.13%

Firm size 25 to 99−0.0000121−0.09%

0.0001666

1.62%−0.0001332−0.86%

Firm size 100 to 499

0.0001656

1.18%

0.0007333

7.11%

0.0000010

0.01%

Firm size 500 to 999

0.0000004

0.00%−0.0000210−0.20%−0.0000264−0.17%

Firm size 1000 and more

−0.0000080−0.06%−0.0002781−2.70%

0.0000554

0.36%

Married−0.0003621−2.59%−0.0010912−10.59%−0.0000435−0.28%

Children under age 11 0.0001582

1.13%

0.0002557

2.48%

0.0000849

0.55%Household size

0.0003445

2.46%

0.0000457

0.44%

0.0003671

2.36%

Non-metro area

0.0000126

0.09%

0.0000523

0.51%

0.0000490

0.32%

Unemployment rate

−0.0000003

0.00%−0.0000451−0.44%−0.0000222−0.14%

Homestead exemption−0.0000827−0.59%−0.0001683−1.63%−0.0000072−0.05%Median home value−0.0001228−0.88%−0.0001552−1.51%−0.0000580−0.37%

Maximum personal income tax rate

0.0000683

0.49%−0.0000380−0.37%

0.0000370

0.24%

Maximum corporate income tax rate

−0.0000088−0.06%−0.0000017−0.02%−0.0000103−0.07%

Sales tax rate

−0.0001582−1.13%−0.0001569−1.52%−0.0000867−0.56%All included variables−0.0028201−20.15%−0.0056817−55.12%−0.0022006−14.17%

11, 1134, 258

Note: Blinder-Oaxaca decomposition is based on analyses of a pooled sample of male and female.

45

6, 748

Table 12: Decompositon of Gender Gap in Entreprenuership: Across Races
White Group

Black Group

Hispanic Group

Male mean (%)

3.20

9.14

5.24

Female mean (%)

1.93

5.20

8.26

Male − Female gap (%)

1.28

3.94

−3.02

Contributions from gender diﬀerences in:N

Foreign born

0.0001325

1.04%−0.0062241−15.80%0.0029146−9.65%

Age 18 to 24−0.0000215−0.17%−0.0006488−1.65%−0.0000659

0.22%

Age 25 to 34

0.0000583

0.46%

0.0000887

0.23%−0.0011036

3.65%

Age 35 to 44

0.0007372

5.78%

0.0007155

1.82%

0.0194980−64.52%

Age 45 to 54−0.0004320−3.39%−0.0001083−0.27%−0.0125884

41.66%

HS graduate−0.0010587−8.30%

0.0017886

4.54%

0.0019754−6.54%

Some college

0.0003237

2.54%

0.0103573

26.29%

0.0017104−5.66%

Bachelor

0.0007531

5.90%−0.0038762−9.84%−0.0094734

31.35%

Postgraduate

0.0001083

0.85%−0.0118908−30.18%−0.0157497

52.12%

Internet−0.0008377−6.56%−0.0025292−6.42%−0.0018801

6.22%

Part time−0.0020245−15.86%0.0006942

1.76%−0.0001171

0.39%

Firm size 25 to 99−0.0000536−0.42%

0.0000677

0.17%

0.0029293−9.69%

Firm size 100 to 499

0.0002329

1.82%−0.0003412−0.87%−0.0081340

26.92%

Firm size 500 to 999

0.0000475

0.37%

0.0005867

1.49%−0.0043290

14.33%

Firm size 1000 and more

−0.0000202−0.16%−0.0011942−3.03%

0.0001571−0.52%

Married−0.0003153−2.47%

0.0020109

5.10%−0.0030299

10.03%

Children under age 110.0000465

0.36%

0.0003981

1.01%

0.0022911−7.58%

Household size

0.0003541

2.78%−0.0002007−0.51%

0.0026918−8.91%

Non-metro area

0.0000465

0.36%−0.0005253−1.33%

0.0001712−0.57%

Unemployment rate

0.0000043

0.03%

0.0003089

0.78%−0.0004883

1.62%

Homestead exemption−0.0001120−0.88%

0.0004814

1.22%−0.0004835

1.60%Median home value−0.0001726−1.35%

0.0000653

0.17%

0.0007982−2.64%

Maximum personal income tax rate

0.0000364

0.29%−0.0006869−1.74%

0.0042837−14.18%Maximum corporate income tax rate

−0.0000175−0.14%

0.0001114

0.28%−0.0028776

9.52%

Sales tax rate

−0.0000797−0.62%−0.0008001−2.03%

0.0010885−3.60%All included variables−0.0022643−17.74%−0.0113511−28.81%−0.0198113

65.56%

9, 446683

Note: Blinder-Oaxaca decomposition is based on analyses of a pooled sample of male and female.

46

473

Table 13: Decompositon of Gender Gap in Entrepreneurship: Across Firm Sizes
Firm size 99 or Less

Firm Size 100 or More

Male mean (%)

3.81

3.36

Female mean (%)

2.21

2.11

Male − Female gap (%)

1.60

1.24

Contributions from gender diﬀerences in:

N

Black

−0.0004563−2.86%−0.0014572−11.73%

Hispanic

0.0001757

1.10%

0.0000104

0.08%

Foreign born

0.0000527

0.33%

0.0000848

0.68%

Age 18 to 24

0.0007618

4.77%−0.0003522−2.83%

Age 25 to 34−0.0004493−2.82%−0.0000305−0.25%

Age 35 to 44

0.0013760

8.62%

0.0002578

2.07%

Age 45 to 54−0.0014240−8.93%−0.0000536−0.43%

HS graduate−0.0024155−15.14%0.0001441

1.16%

Some college

0.0003369

2.11%−0.0000524−0.42%

Bachelor

0.0006624

4.15%

0.0004244

3.41%

Postgraduate

0.0001997

1.25%

0.0001296

1.04%

Internet−0.0013857−8.68%−0.0006581−5.30%Part time−0.0005168−3.24%−0.0018560−14.94%

Firm size 25 to 99−0.0000429−0.27%

Firm size 500 to 999

0.0000404

0.33%

Firm size 1000 and more

0.0001103

0.89%

Married−0.0000714−0.45%−0.0005887−4.74%

Children under age 11−0.0000698−0.44%

0.0003908

3.14%Household size

0.0001064

0.67%

0.0005939

4.78%

Non-metro area

0.0000017

0.01%

0.0000632

0.51%

Unemployment rate

−0.0000009−0.01%−0.0000025−0.02%

Homestead exemption−0.0000807−0.51%−0.0000264−0.21%Median home value−0.0000805−0.50%−0.0001447−1.16%

Maximum personal income tax rate

−0.0000102−0.06%

0.0001028

0.83%

Maximum corporate income tax rate

0.0000188

0.12%−0.0000186−0.15%

Sales tax rate

−0.0002729−1.71%−0.0000616−0.50%All included variables−0.0035848−22.47%−0.0029503−23.74%4, 885

6, 228

Note: Blinder-Oaxaca decomposition is based on analyses of a pooled sample of male and female.

47

Table 14: Decompositon of Gender Gap in Intrapreneurship: Across Educational Attainments
Total

Bachelor or More

Some College or Less

Male mean (%)

2.95

3.20

2.79

Female mean (%)

0.91

1.18

0.98

Male − Female gap (%)

2.03

2.02

1.81

Contributions from gender diﬀerences in:
Black

N

−0.0002371−1.17%−0.0001972−0.98%−0.0002453−1.35%

Hispanic

0.0001359

0.67%

0.0000187

0.09%

0.0001910

1.05%

Foreign born

−0.0000519−0.26%−0.0000349−0.17%−0.0000484−0.27%

Age 18 to 24

0.0006564

3.23%

0.0006470

3.20%

0.0010566

5.83%

Age 25 to 34

0.0000097

0.05%−0.0005715−2.83%

0.0002719

1.50%

Age 35 to 44

0.0002524

1.24%

0.0001626

0.81%

0.0003210

1.77%

Age 45 to 54−0.0003022−1.49%−0.0003453−1.71%−0.0002504−1.38%

HS graduate−0.0001689−0.83%−0.0000954−0.53%

Some college

0.0000997

0.49%

0.0001856

1.02%

Bachelor

0.0001317

0.65%

Postgraduate−0.0000211−0.10%−0.0000042−0.02%

Internet−0.0002623−1.29%−0.0001581−0.78%−0.0002504−1.38%Part time

0.0014489

7.13%

0.0018420

9.12%

0.0011952

6.60%

Firm size 25 to 99−0.0003675−1.81%−0.0004124−2.04%−0.0002713−1.50%

Firm size 100 to 499

0.0001083

0.53%−0.0000745−0.37%

0.0000079

0.04%

Firm size 500 to 999

0.0000004

0.00%

0.0000755

0.37%−0.0000163−0.09%

Firm size 1000 and more

−0.0000214−0.11%

0.0000930

0.46%

0.0003646

2.01%

Married−0.0002715−1.34%

0.0005477

2.71%−0.0002992−1.65%

Children under age 110.0000841

0.41%−0.0001659−0.82%

0.0001511

0.83%

Household size

0.0000122

0.06%−0.0002440−1.21%

0.0000350

0.19%

All included variables

0.0012359

6.08%

0.0011786

5.84%

0.0023031

12.71%

10, 791

4, 170Note: Blinder-Oaxaca decomposition is based on analyses of a pooled sample of male and female.

48

6, 545

Table 15: Decompositon of Gender Gap in Intrapreneurship: Across Races
White Group

Black Group

Hispanic Group

Male mean (%)

2.79

8.58

6.88

Female mean (%)

0.91

1.69

4.84

Male − Female gap (%)

1.88

6.89

2.05

Contributions from gender diﬀerences in:N

Foreign born

−0.0000616−0.33%

Age 18 to 24

0.0005812

3.10%

Age 25 to 34

0.0000104

0.06%

Age 35 to 44

0.0002619

1.40%

Age 45 to 54−0.0002755

HS graduate

0.0005280

0.77%

0.0011069

5.41%

0.0255306

37.05%

0.0023292

11.38%−0.0185742−26.96%0.0085679

41.87%

0.0101990

14.80%

0.0009041

4.42%−1.47%−0.0158373−22.98%−0.0042963−20.99%0.0000256

0.14%−0.0065773−9.55%−0.0000804−0.39%

Some college−0.0000676−0.36%−0.0033060−4.80%

0.0004977

2.43%

Bachelor−0.0000537−0.29%

0.0015259

2.21%

0.0014626

7.15%

Postgraduate−0.0000354−0.19%

0.0037822

5.49%

0.0002006

0.98%

Internet−0.0003245−1.73%−0.0035293−5.12%−0.0000035−0.02%Part time

0.0017484

9.32%−0.0004029−0.58%

0.0000467

0.23%

Firm size 25 to 99−0.0004582−2.44%

0.0000583

0.08%

0.0019070

9.32%

Firm size 100 to 499

0.0001150

0.61%

0.0042730

6.20%−0.0001350−0.66%

Firm size 500 to 999−0.0000708−0.38%

0.0007591

1.10%

0.0008734

4.27%

Firm size 1000 and more

−0.0000679−0.36%

0.0000518

0.08%−0.0006294−3.08%

Married−0.0001020−0.54%−0.0026100−3.79%

0.0003493

1.71%

Children under age 110.0001074

0.57%

0.0000921

0.13%

0.0039902

19.50%

Household size

−0.0000457−0.24%

0.0004343

0.63%

0.0014428

7.05%

All included variables

0.0012872

6.86%−0.0036027−5.23%

0.0185340

90.56%

9, 200692

Note: Blinder-Oaxaca decomposition is based on analyses of a pooled sample of male and female.

49

621

Table 16: Decompositon of Gender Gap in Intrapreneurship: Across Firm Sizes
Firm size 99 or Less

Firm Size 100 or More

Male mean (%)

3.00

3.24

Female mean (%)

1.25

1.20

Male − Female gap mean (%)

1.74

2.04

Contributions from gender diﬀerences in:

N

Black

−0.0000371−0.21%

Hispanic−0.0000103−0.06%

0.0001553

0.76%

Foreign born

−0.0000117−0.07%−0.0000196−0.10%

Age 18 to 24

0.0001216

0.70%

0.0004864

2.38%

Age 25 to 34−0.0000064−0.04%

0.0002826

1.38%

Age 35 to 44−0.0000338−0.19%

0.0003838

1.88%

Age 45 to 54−0.0000342−0.20%−0.0003432−1.68%

HS graduate−0.0001737−1.00%

0.0000279

0.14%

Some college

0.0000833

0.48%−0.0000177−0.09%

Bachelor

0.0001135

0.65%

0.0000340

0.17%

Postgraduate

0.0000188

0.11%−0.0000170−0.08%

Internet−0.0001536−0.88%−0.0001292−0.63%

0.0012374

6.05%

Firm size 500 to 999−0.0000039−0.02%

Firm size 1000 and more

0.0000005

0.00%Part time

0.0000940

0.54%

Firm size 25 to 99−0.0001364−0.78%−0.0004540−2.22%

Married−0.0000335−0.19%−0.0001417−0.69%

Children under age 110.0000090

0.05%

0.0001383

0.68%

Household size

0.0000337

0.19%−0.0002320−1.13%All included variables−0.0001571−0.90%

0.0013880

6.79%

4, 735

6, 056

Note: Blinder-Oaxaca decomposition is based on analyses of a pooled sample of male and female.

50

Table A1: State-Level Data
State

HomesteadMedianUnemployment Individual Income Corporate Income Sales Tax
Exemption ($) Home Value ($) Rate (%)
Tax Rate (%)
Tax Rate (%)
Rate (%)

Alabama
Arizona
Arkansas
California
Colorado
Conneticut
Delaware
D.C.
FloridaGeorgia
Idaho
Illinois
Indiana
Iowa
KansasKentuckyLouisiana
Maine
Maryland
Massachusetts
MichiganMinnesota
Mississippi
MissouriMontana
Nebraska
NevadaNew Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
OregonPennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming

10,000150,000
Unlimited
75,00090,000150,000
0
36,900
Unlimited
20,00050,00015,00015,000
Unlimited
Unlimited
10,00025,00070,0000500,000
36,900200,000
150,000
15,000200,000
12,500200,000
200,000
36,90060,00020,00010,00080,00010,000
Unlimited
33,00036,900200,000
36,900
Unlimited
7,500
Unlimited
40,000150,000
10,00040,00050,00040,00020,00097,500185,400
87,400477,700
223,300
271,500
203,800
384,400
189,500
147,500
134,900
183,900
114,400
106,600
107,800
103,900
101,700
155,300
280,200
361,500
149,300
198,800
82,700123,100
131,600
113,200
283,400
240,100
333,900
125,500
258,900
127,600
88,600129,600
89,100201,200
131,900
281,300
113,100
101,700
114,000
106,000
167,200
173,400
212,300
227,700
84,400152,600
135,000

4
4.7
4.9
5.4
5
4.9
4.2
6.5
3.8
5.3
3.8
5.7
5.4
4.6
5.1
6.1
7.1
4.8
4.1
4.8
6.7
4
7.9
5.4
4
3.8
4.1
3.6
4.4
5.3
5
5.2
3.4
5.9
4.4
6.1
5
5
6.8
3.9
5.6
5.3
4.3
3.5
3.5
5.5
5
4.7
3.6

5
5.04
7
9.3
4.63
5
5.95
9
0
6
7.8
3
3.4
8.98
6.45
6
6
8.5
4.75
5.3
3.9
7.85
5
6
11
6.84
0
5
8.97
6.8
7.7
8.25
5.54
7.5
6.65
9
3.07
9.9
7
0
6
0
7
9.5
5.75
0
6.5
6.75
0

6.5
6.97
6.5
8.84
4.63
7.5
8.7
9.98
5.5
6
7.6
7.3
8.5
12
4
8.25
8
8.93
7
9.5
1.9
9.8
5
6.25
6.75
7.81
0
9.25
9
7.6
7.5
6.9
7
8.5
6
6.6
9.99
9
5
0
6.5
0
5
9.75
6
0
9
7.9
0

4
5.6
6
6.25
2.9
6
0
5.75
6
4
6
6.25
6
5
5.3
6
4
5
5
5
6
6.5
7
4.225
0
5.5
6.5
0
6
5
4
4.5
5
6
4.5
0
6
7
5
4
7
6.25
4.75
6
4
6.5
6
5
4

Mean
MedianStd. Dev.

74,107a
40,00093,558

175,416
147,500
87,561

4.9
4.9
1.01

5.8
6
2.81

6.7
7
2.89

4.9
5.3
1.76

Source: Corradin, Gropp, Huizinga and Laeven (2016) (Homestead exemptions); 2005 Amercan Community Survey (Median home values);
Bureau of Labor Statistics (Unemployment rates); Tax Foundation (Taxes)
Note: Araska and Hawaii are not included because the PSED II does not included individulas living in these states.
a: States with “Unlimited” are excluded.

51

Table A2: Correlations between Institutional Variables

Exemp
Home
Unemp
Ind Inc
Corp Inc
Sales

Exemp Home Unemp−0.08 −0.20
−0.06
-

52

Ind In Corp Inc
−0.19
−0.17
0.14
0.19
0.08
−0.02
0.71
-

Sales
0.07
0.03
0.23
−0.17
−0.04
-

Table A2.1: Parameter Estimates in Equation (1)Specification 1
Dependent variable:
1 = Staying in
0 = Going out (Entrepreneurship)
FemaleRace: Ref = whiteBlack
Hispanic
Race: Ref = white maleWhite female
Black maleBlack female
Hispanic male
Hispanic female
Foreign born
Age: Ref = 55 and more
18 to 2425 to 34
35 to 44
45 to 54
Education: Ref = HS dropout
HS graduate
Some college
Bachelor
Postgraduate
Internet
Employment Status:Ref = full time
Part time
Firm size: Ref = under firm size 24
Firm size 25 to 99
Firm size 100 to 499
Firm size 500 to 999
Firm size 1000 and moreHousehold
MarriedChildren under age 11
Size
Non-metro area
Unemployment rate
Homestead exemption (×102 )Median home value (×102 )Maximum personal income tax rate
Maximum corporate income tax rate
Sales tax rate
Constant
N

0.256∗∗∗(0.053)−0.402∗∗∗−0.062(0.080)(0.092)

Specification 20.226∗∗(0.090)0.241∗∗∗−0.461∗∗∗−0.089−0.0530.159
0.228∗∗−0.030−0.213∗∗−0.225∗∗−0.184∗∗(0.116)(0.095)(0.090)(0.083)−0.030−0.214∗∗−0.225∗∗−0.184∗∗(0.116)(0.094)(0.089)(0.083)0.369∗∗∗
0.138
0.288∗∗
0.289∗∗−0.361∗∗∗(0.110)(0.109)(0.114)(0.123)(0.086)

0.371∗∗∗
0.139
0.290∗∗
0.289∗∗−0.361∗∗∗(0.110)(0.109)(0.114)(0.123)(0.086)−0.128∗(0.073)−0.124∗(0.073)

0.006
0.101
−0.0300.040(0.080)(0.088)(0.107)(0.059)

0.005
0.101
−0.0320.039(0.080)(0.088)(0.107)(0.059)0.087−0.061−0.030−0.018−0.003−0.027∗−0.045∗
0.013
0.004
0.029
1.989∗∗∗(0.058)(0.067)(0.022)(0.059)(0.033)(0.015)(0.026)(0.013)(0.012)(0.019)(0.239)0.090−0.062−0.030−0.017−0.004−0.027∗−0.045∗
0.013
0.004
0.029
1.996∗∗∗(0.058)(0.067)(0.022)(0.059)(0.033)(0.015)(0.026)(0.013)(0.012)(0.019)(0.239)11, 113

Note: Robust standard errors are in parentheses.
∗
p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01

53

11, 113(0.059)(0.105)(0.115)(0.112)(0.151)(0.090)Table A2.2: Parameter Estimates in Equation (2)Specification 1
Dependent variable:
1 = Intrapreneurship
0 = Else
FemaleRace: Ref = whiteBlack
Hispanic
Race: Ref = white maleWhite female
Black maleBlack female
Hispanic male
Hispanic female
Foreign born
Age: Ref = 55 and more
18 to 2425 to 34
35 to 44
45 to 54
Education: Ref = HS dropout
HS graduate
Some college
Bachelor
Postgraduate
Internet
Employment Status:Ref = full time
Part time
Firm size: Ref = under firm size 24
Firm size 25 to 99
Firm size 100 to 499
Firm size 500 to 999
Firm size 1000 and moreHousehold
MarriedChildren under age 11
Sizeρ
Constant
LL
AIC
N−0.367∗∗∗(0.054)0.222∗∗∗
0.151∗(0.084)(0.088)Specification 2

0.090(0.079)−0.348∗∗∗0.274∗∗−0.2070.163−0.2230.089

0.396∗∗∗
0.265∗∗∗
0.141
0.148∗(0.108)(0.095)(0.091)(0.083)0.397∗∗∗
0.264∗∗∗
0.142
0.148∗(0.108)(0.095)(0.090)(0.083)−0.137−0.112−0.1210.004
0.167∗∗(0.110)(0.111)(0.114)(0.121)(0.078)−0.137−0.113−0.1220.006
0.167∗∗(0.110)(0.111)(0.114)(0.121)(0.078)−0.151∗(0.081)−0.154∗(0.081)−0.296∗∗∗−0.108−0.121−0.110∗(0.092)(0.083)(0.113)(0.058)−0.295∗∗∗−0.107−0.119−0.109∗(0.092)(0.083)(0.113)(0.058)−0.0950.060
0.008
0.895∗∗∗−1.908∗∗∗(0.059)(0.070)(0.022)(0.019)(0.136)−0.097∗0.062
0.008
0.867∗∗∗−1.913∗∗∗(0.059)(0.070)(0.022)(0.025)(0.136)−2739.717
5583.435
11, 113(0.060)(0.107)(0.129)(0.102)(0.156)(0.079)−2738.989
5589.97811, 113

Notes: Robust standard errors are in parentheses. LL stands for Log Likelihood, and AIC for Akaike’s Information Criterion.
∗p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01

54

Table A2.3: Parameter Estimates in Equation (1)Dependent variable:
1 = Staying in
0 = Going out (Entrepreneurship)
FemaleRace: Ref = whiteBlack
Hispanic
Foreign born
Age: Ref = 55 and more
Age 18 to 24
Age 25 to 34
Age 35 to 44
Age 45 to 54
Education: Ref = HS dropout
HS graduate
Some college
Bachelor
Postgraduate
Internet
Employment Status: Ref = full time
Part time
Firm size: Ref = under firm size 24
Firm size 25 to 99
Firm size 100 to 499
Firm size 500 to 999
Firm size 1000 and moreHousehold
MarriedChildren under age 11Children under age 11 × Female
Household size
Size × Female
Non-metro area
Unemployment rate (×102 )Homestead exemption (×102 )Median home value
Maximum personal income tax rate
Maximum corporate income tax rate
Sales tax rate
Constant
N

Specification 3

Specification 40.274∗∗∗(0.064)0.319∗∗∗(0.116)−0.402∗∗∗−0.0630.226∗∗(0.080)(0.092)(0.090)−0.402∗∗∗−0.0610.226∗∗(0.080)(0.092)(0.090)−0.030−0.212∗∗−0.225∗∗−0.184∗∗(0.116)(0.095)(0.090)(0.083)−0.032−0.211∗∗−0.224∗∗−0.183∗∗(0.116)(0.095)(0.090)(0.083)0.370∗∗∗
0.140
0.289∗∗
0.289∗∗−0.362∗∗∗(0.110)(0.109)(0.114)(0.123)(0.086)

0.369∗∗∗
0.138
0.288∗∗
0.287∗∗−0.361∗∗∗(0.110)(0.109)(0.114)(0.123)(0.086)−0.126∗(0.073)−0.126∗(0.073)

0.006
0.101
−0.0300.039(0.080)(0.088)(0.107)(0.059)

0.005
0.100
−0.0300.039(0.080)(0.088)(0.107)(0.059)0.086−0.041−0.051−0.030(0.058)(0.078)(0.107)(0.022)0.087−0.062(0.058)(0.067)−0.018−0.003−0.027∗−0.045∗
0.013
0.004
0.029
1.984∗∗∗(0.059)(0.033)(0.015)(0.026)(0.013)(0.012)(0.019)(0.240)−0.022−0.021−0.018−0.003−0.027∗−0.045∗
0.012
0.004
0.029
1.968∗∗∗(0.026)(0.034)(0.059)(0.033)(0.015)(0.026)(0.013)(0.012)(0.019)(0.243)

11, 113

Note: Robust standard errors are in parentheses.
∗
p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01

55

11, 113

Table A2.4: Parameter Estimates in Equation (2)Specification 3
Dependent variable:
1 = Intrapreneurship
0 = Else
Female−0.342∗∗∗Black
0.224∗∗∗
Hispanic
0.150∗
Foreign born
0.090
Age 18 to 24
0.397∗∗∗
Age 25 to 34
0.267∗∗∗
Age 35 to 44
0.141
Age 45 to 54
0.148∗
HS graduate−0.135
Some college−0.110Bachelor−0.119Postgraduate
0.006
Internet
0.165∗∗Part time−0.147∗
Firm size 25 to 99−0.297∗∗∗
Firm size 100 to 499−0.107
Firm size 500 to 999−0.121
Firm size 1000 and more
−0.110∗Married−0.097∗
Children under age 11
0.085
Children under age 11 × Female−0.076
Household size
0.008
Household size × Female
ρ
0.859∗∗∗
Constant−1.916∗∗∗
LL−2739.393
AIC
5586.785
N11, 113(0.065)(0.084)(0.088)(0.079)(0.108)(0.095)(0.091)(0.083)(0.110)(0.111)(0.114)(0.121)(0.078)(0.081)(0.092)(0.083)(0.113)(0.058)(0.059)(0.079)(0.114)(0.022)(0.023)(0.137)

Specification 4−0.333∗∗∗0.222∗∗∗
0.151∗
0.090
0.396∗∗∗
0.267∗∗∗
0.143
0.149∗−0.137−0.112−0.121
0.004
0.166∗∗−0.149∗−0.297∗∗∗−0.108−0.122−0.110∗−0.0950.059(0.119)(0.084)(0.088)(0.079)(0.108)(0.095)(0.091)(0.083)(0.110)(0.111)(0.114)(0.121)(0.078)(0.081)(0.092)(0.083)(0.112)(0.058)(0.059)(0.070)0.012−0.0120.847∗∗∗−1.919∗∗∗−2739.492
5586.985
11, 113(0.024)(0.037)(0.024)(0.141)

Notes: Robust standard errors are in parentheses. LL stands for Log Likelihood, and AIC for Akaike’s Information Criterion.
∗p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01

56

Table A2.5: Parameter Estimates in Equation (1)Dependent variable:
1 = Staying in
0 = Going out (Entrepreneurship)
FemaleRace: Ref = whiteBlack
Hispanic
Foreign born
Age: Ref = 55 and more
Age 18 to 24
Age 25 to 34
Age 35 to 44
Age 45 to 54
Education: Ref = HS dropout
HS graduate
Some college
Bachelor
Postgraduate
HS graduate × Female
Some colleage × Female
Bachelor × Female
Postgraduate × Female
Internet
Employment Status: Ref = full time
Part time
Firm size: Ref = under firm size 24
Firm size 25 to 99
Firm size 100 to 499
Firm size 500 to 999
Firm size 1000 and moreHousehold
MarriedChildren under age 11
Size
Non-metro area
Unemployment rate
Homestead exemption (×102 )Homestead exemption × Female (×102 )Median home value (×102 )Median home value × Female (×102 )Maximum personal income tax rate
Maximum corporate income tax rate
Sales tax rate
Constant
N

Specification 5

Specification 60.756∗∗∗(0.245)0.368∗∗∗(0.118)−0.404∗∗∗−0.0620.229∗∗(0.080)(0.092)(0.091)−0.408∗∗∗−0.0640.226∗∗(0.080)(0.092)(0.090)−0.028−0.212∗∗−0.228∗∗−0.185∗∗(0.117)(0.095)(0.090)(0.083)−0.035−0.215∗∗−0.228∗∗−0.186∗∗(0.117)(0.095)(0.090)(0.083)0.532∗∗∗
0.228∗
0.412∗∗∗
0.411∗∗∗−0.635∗∗−0.447∗−0.533∗∗−0.529∗−0.359∗∗∗(0.130)(0.125)(0.133)(0.144)(0.269)(0.260)(0.266)(0.280)(0.086)

0.371∗∗∗
0.139
0.290∗∗
0.291∗∗(0.110)(0.109)(0.115)(0.123)−0.359∗∗∗(0.086)−0.128∗(0.072)−0.130∗(0.073)

0.005
0.106
−0.0330.045(0.081)(0.088)(0.107)(0.059)

0.002
0.098
−0.0360.039(0.080)(0.088)(0.107)(0.059)0.091−0.060−0.032−0.018−0.004−0.027∗(0.058)(0.068)(0.022)(0.060)(0.033)(0.015)−0.045∗(0.027)0.084−0.062−0.029−0.015−0.002−0.005−0.052∗−0.038−0.017
0.012
0.005
0.029
1.943∗∗∗(0.058)(0.067)(0.022)(0.059)(0.033)(0.019)(0.027)(0.033)(0.046)(0.013)(0.012)(0.019)(0.245)

0.013
0.004
0.030
1.875∗∗∗
11, 113

Note: Robust standard errors are in parentheses.
∗
p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01

57

(0.013)(0.012)(0.019)(0.244)

11, 113

Table A2.6: Parameter Estimates in Equation (2)Specification 5
Dependent variable:
1 = Intrapreneurship
0 = Else
FemaleRace: Ref = whiteBlack
Hispanic
Foreign born
Age: Ref = 55 and more
Age 18 to 24
Age 25 to 34
Age 35 to 44
Age 45 to 54
Education: Ref = HS dropout
HS graduate
Some college
Bachelor
Postgraduate
HS graduate × Female
Some colleage × Female
Bachelor × Female
Postgraduate × Female
Internet
Employment Status: Ref = full time
Part time
Firm size: Ref = under firm size 24
Firm size 25 to 99
Firm size 100 to 499
Firm size 500 to 999
Firm size 1000 and moreHousehold
MarriedChildren under age 11
Sizeρ
Constant
LL
AIC
N

Specification 6−0.487∗∗(0.228)−0.367∗∗∗(0.054)0.223∗∗∗
0.150∗
0.091(0.084)(0.088)(0.079)0.222∗∗∗
0.151∗
0.090(0.084)(0.088)(0.079)0.388∗∗∗
0.258∗∗∗
0.136
0.146∗(0.109)(0.094)(0.091)(0.083)0.396∗∗∗
0.265∗∗∗
0.141
0.148∗(0.108)(0.095)(0.091)(0.083)−0.144−0.128−0.176−0.046
0.057
0.089
0.198
0.186
0.169∗∗(0.127)(0.128)(0.135)(0.141)(0.253)(0.251)(0.252)(0.262)(0.078)−0.137−0.112−0.1210.004

0.167∗∗(0.078)−0.147∗(0.081)−0.151∗(0.081)−0.295∗∗∗−0.109−0.121−0.108∗(0.092)(0.083)(0.113)(0.058)−0.296∗∗∗−0.108−0.121−0.110∗(0.092)(0.083)(0.113)(0.058)−0.0950.061
0.009
0.907∗∗∗−1.883∗∗∗(0.059)(0.070)(0.022)(0.053)(0.147)−0.0950.060
0.008
0.899∗∗∗−1.908∗∗∗(0.059)(0.070)(0.022)(0.020)(0.136)−2735.725
5591.451
11, 113(0.110)(0.111)(0.114)(0.121)−2737.844
5583.689
11, 113

Notes: Robust standard errors are in parentheses. LL stands for Log Likelihood, and AIC for Akaike’s Information Criterion.
∗p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01

58