Immigrant Entrepreneurship in the US: Intersectionality as a Blessing and a Curse

Abstract

Immigrant entrepreneurship is a crucial topic of interest for academics, policymakers, and the popular press. Discussions of related topics often use intersectionality to explain the compounding effects of multiple “oppressed” identities; the current study provides some novel insights into how intersectional effects can also confer unique advantages to immigrant populations in the United States. We examine intersectional effects across immigrants’ higher education, their home country’s entrepreneurial culture, and the host country’s state-level institutional environment on the probability that people become entrepreneurs. With a sample constructed from multiple sources and spanning 2005 to 2019, this research explores the channels that affect immigrants’ self-selection into entrepreneurship. Although higher education and entrepreneurial cultural background positively affect new venture creation, state-level institutional barriers, like E-Verify mandates, create heterogeneous effects across immigrant groups. Furthermore, the entrepreneurial culture of immigrants' home countries leaves a lasting impression on venture creation, particularly when combined with higher education and even in the face of institutional barriers. This study offers policy makers relevant insights for how to augment the contributions of immigrant entrepreneurs and enhance the positive spillovers of new venture creation.

Full text

Immigrant Entrepreneurship in the US: Intersectionality as a Blessing and a Curse
Punit Arora
City University of New York
160 Convent Ave, NAC 5/141
New York, NY 10031.
212-650-8502
parora@gc.cuny.edu
Priya Nagaraj
William Patterson University of New Jersey
Department of Economics, Finance & Global Business
3029 Valley Road
Wayne, NJ 07470.
973-720-3985.
nagarajp1@wpunj.edu
Marta Bengoa
City College of New York
Colin Powell School for Civic and Global Leadership
160 Convent Ave, NAC 4/120
New York, NY 10031.
212-650-6143.
mbengoa@ccny.cuny.edu
Debmalya Mukherjee
Associate Dean of Academics
Professor of Management
Department of Management
The University of Akron, College of Business
dmukher@uakron.edu
330-972-7039.
Acknowledgment: We thank Area Editor Stephanie Fernhaber and three anonymous reviewers for
their constructive and insightful feedback.
(Version April 2025: Accepted at the Journal of Business Venturing)

1
Immigrant Entrepreneurship in the United States: Intersectionality as a Blessing and a
Curse
ABSTRACT
Immigrant entrepreneurship is a crucial topic of interest for academics, policymakers, and the
popular press. Discussions of related topics often use intersectionality to explain the
compounding effects of multiple “oppressed” identities; the current study provides some novel
insights into how intersectional effects can also confer unique advantages to immigrant
populations in the United States. We examine intersectional effects across immigrants’ higher
education, their home country’s entrepreneurial culture, and the host country’s state-level
institutional environment on the probability that people become entrepreneurs. With a sample
constructed from multiple sources and spanning 2005 to 2019, this research explores the
channels that affect immigrants’ self-selection into entrepreneurship. Although higher education
and entrepreneurial cultural background positively affect new venture creation, state-level
institutional barriers, like E-Verify mandates, create heterogeneous effects across immigrant
groups. Furthermore, the entrepreneurial culture of immigrants' home countries leaves a lasting
impression on venture creation, particularly when combined with higher education and even in
the face of institutional barriers. This study offers policy makers relevant insights for how to
augment the contributions of immigrant entrepreneurs and enhance the positive spillovers of new
venture creation.
Keywords: Immigrant entrepreneurship; intersectional identities; adopted identities;
entrepreneurial mindmaps; new venture creations; host and home country institutions.

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Executive Summary
Immigration and immigrant entrepreneurship have prompted a great deal of interest among a
wide range of audiences, from academics and policymakers to popular press and common people
on the street. In the past two decades, immigrants have started a vast number of small businesses;
they also have founded more than half of the “unicorn” startups valued at over $1 billion—and
more than half of such unicorns list only immigrants as founders. As research has shown,
immigrants are not just more likely to be entrepreneurs but also more likely to innovate.
Considering this outsized representation of immigrants among entrepreneurs, identifying factors
determining immigrants’ inclinations to start new ventures becomes theoretically and practically
vital. This study compiles data from multiple sources and leverages advanced econometrics to
provide robust evidence related to this question.
Specifically, we examine the intersectional effects of immigrants’ higher education (as a
resource and identity) and their entrepreneurial mindmaps (as created by their home country’s
entrepreneurial culture) in the context of the host country’s state-level institutional environment
(denoting prevalent power structures), on the probability of being an entrepreneur in the United
States. Prior research frequently utilizes intersectionality theory to predict the compounding
effects of multiple “oppressed” identities; the current study provides novel insights into how
intersectional effects can also confer unique advantages to some U.S. immigrant groups.
Intersectionality theory has been criticized for its overemphasis on innate traits, such as race
and gender. These critics suggest the need to grant more consideration to dynamic contextual
factors that also shape individual lived experiences, with the recognition that the "globalizing era
provides opportunities for people to develop and enact new identities that are no longer
necessarily tied to traditionally defined ethnolinguistic, national, or cultural identities" (Higgins
2011, p. 19). That is, individuals, including immigrants, are not confined to their innate identities
but instead can mold those identities to achieve desired outcomes (Leitch and Harrison 2016).
Such a perspective purposefully does not minimize the importance of oppressive power
structures. As our research shows, some intersectional effects continue to constrain
entrepreneurship among specific groups. Yet by clarifying the concurrent influence of contextual
factors, we demonstrate how the theorized effects persist even across identity (sub)categories. In
turn, our study highlights critical factors that can and do help people overcome institutional
challenges. Policymakers can leverage this information to identify and address both enablers and
barriers to new venture creation.
In relation to immigrant entrepreneurship literature, this study demonstrates the enduring
influence of cultural mindsets that immigrant entrepreneurs bring with them from their home
countries to their destinations. Thus, immigrants' risk-taking capacity and other entrepreneurial
activities depend on not only their formal education but also the informal mindmaps that they
have imbibed through cultures, customs, and traditions. By providing evidence of an embedded,
intersectional identity of an "educated immigrant go-getter," this study helps explain the
widespread occurrence of immigrant entrepreneurship in the United States while simultaneously
demonstrating the importance of formal institutional factors for determining entrepreneurship
levels. Policymakers can use these insights to design interventions aimed at attracting, retaining,
training, mentoring, and supporting immigrant entrepreneurs globally.

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1. Introduction
“A nation of immigrants is a nation of entrepreneurs.”
—Gidi Grinstein, as quoted in The Startup Nation (Senor and Singer, 2009, p. 121).
Due to the labor market challenges that immigrants tend to face, entrepreneurship
represents the primary means for immigrants to contribute to host economies (Amornsiripanitch
et al., 2023; Collins and Low, 2010; Edelman et al., 2010). For example, immigrants represent
only 13.7% of the total U.S. population (Budiman et al., 2020), but they founded more than 25%
of U.S. businesses (Bernstein et al., 2022). Not only is the new business growth rate higher for
immigrants than for natives (Kerr and Kerr, 2020), but their businesses are also more innovative,
as judged by the number of new patents filed by firms (Brown et al., 2019). Azoulay et al. (2020)
showed that U.S. immigrants are 80% more likely to start a new business than natives, although
there are significant spatial differences across the country,1 with potentially important and still
unexplored implications for immigrant entrepreneurship.
Prior research on immigrant entrepreneurship has addressed personal-level analyses
(Aliaga-Isla and Rialp, 2013), including the impact of human and social capital (Aldrich and
Kim, 2007; Toft-Kehler et al., 2014), family resources (Bird and Wennberg, 2016) , and financial
capital (Fairlie and Robb, 2009). Understanding of intersectional effects in entrepreneurship
remains insufficient though (Dabić et al., 2020; Murzacheva et al., 2020). Intersectionality theory
(Crenshaw, 1989) has primarily focused on intersecting “complex and oppressive” identities and
“structures, including gender, race, migration, age, sexual orientation, legal status, and class”
(Lassalle and Shaw, 2021, p. 1497). However, in applying it to immigrant entrepreneurship, we
offer the novel assertion that intersecting identities do not necessarily or solely create systems of
1 Immigrants formed 38% of new firms in top-performing states such as California and New York but around 22% in states such
as Illinois and Virginia.

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oppression and compounded inequalities. They also might confer unique advantages on some
social groups in certain conditions (Cho et al., 2013; Lewis and Crabbe, 2024; Stajkovic and
Stajkovic, 2025). For example, transnational immigrants possess unique contextual knowledge
gained from different “worlds,” which they might combine in novel ways to generate
entrepreneurial opportunities (Mindes and Lewin, 2024). In response to calls for research that
explores intersectional effects in immigrant entrepreneurship (Dabić et al., 2020; Martinez Dy,
2020), we take a novel perspective to theorize and examine the intersectional impact of
immigrants’ higher education and entrepreneurial mindmaps (home country’s entrepreneurial
culture), in the embedded context of their host country’s institutional environments, on new
venture creation across the United States.
Through this research effort, we make three key contributions. First, we embrace the
concept of dynamic centering” (Collins, 2008) to select intersectional categories that are
contextually salient to immigrant venturing as jointly considering all dimensions of an individual
identity instead would reduce groups to individuals, rendering the study moot (Young, 2002).
Guided by prior theoretical insights, we examine the intersectionality of immigrants’ most
valuable resources: their educational background and their entrepreneurial mindmaps. In general,
immigrants possess relatively less financial capital when they migrate (e.g., Cheng, 2015; Dheer,
2018), so their educational status and entrepreneurial mindmaps gain prominence for
determining their new venture creation. In selecting these categories, we also posit that higher
education can proxy for the entrepreneurs’ identity, resourcefulness, and social class, while the
home country’s entrepreneurial culture informs their prior exposure to risk-taking and self-
reliance. The resulting intersectional identity of an “educated immigrant go-getter” is then more
likely to prepare a person for an entrepreneurial career, in line with the recognition that an ability

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to assume risk and perform entrepreneurial tasks requires not just on formal education but also
an informal sense of comfort, obtained through deep-rooted mindmaps, culture, customs, and
traditions (Minniti and Naudé, 2010).
Second, we enrich intersectionality theory by contextualizing its predictions. Because
people are embedded within institutional environments, we explore the focal intersectional
effects by accounting for the state-level regulatory regimes. Immigrants relocate to different U.S.
states, with varying regulatory regimes that in turn can facilitate or hamper entrepreneurial
activities. Building on evidence of the importance of subnational institutions (Gill and Larson,
2014; Thams et al., 2018), we examine the effect of mandatory E-Verify reporting as a proxy for
(un)welcoming environments with the premise that unfriendly labor market conditions trigger
entrepreneurial responses from predisposed immigrants.2 With this assessment, we respond to
calls to clarify the impact of education and other attributes according to the context of regional
institutional systems (Dheer, 2018; Dheer et al., 2019).
Third, intersectionality research tends to focus on the compounded disadvantages
generated by multiple social inequalities or unequal power advantages enjoyed by dominant
social groups (Qureshi et al., 2023). We, in contrast, propose that intersectionality can also create
unique advantages equally worthy of further scholarly examination. In a recent review, Thatcher
et al. (2023) argue that while intersectionality is often used to examine disadvantages, some
recent studies have detected its contextual benefits for specific groups. For example, Stokes
(2015) identifies a “glass runway” effect that privileges gay male workers in the fashion
industry, and Cheng (2013) shows that a white racial identity can help women negate the
inequalities generated by the intersection of their gender and immigrant status. According to
2 E-Verify is the U.S. federal system for verifying the employment eligibility of newly hired employees.

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Tlaiss and McAdam (2021), a privileged religious identity bestows offsetting benefits on some
women in Lebanon too. We build on this nascent evidence to propose that higher education and
entrepreneurial mindmaps can establish identities and resources that offer offsetting benefits. The
results offer a more holistic picture of the privileges and challenges that immigrant entrepreneurs
in the United States encounter.
Relatedly, we acknowledge that intersectionality theory has been criticized for being “too
deterministic by placing too much emphasis on imposed attributes” such as race and gender, as
well as “failing to recognize—or refusing—hierarchizations between attributes and oppressive
structures” (Lassalle and Shaw, 2021, p. 1499). As scholars argue, “the experience of
intersectionality is highly contextual and dynamic” (Thatcher et al., 2023, p. 729), so it needs to
be understood according to processes of structuration that stem from the interplay of agents and
contexts (Anthias, 2013; Lassalle and Shaw, 2021; Martinez Dy et al., 2014). It also appears
critical to situate actors within their institutional environments (Dabić et al., 2020; Sinkovics and
Reuber, 2021; Van Burg et al., 2022). We propose and show that such contextualization is
important, not only to ground intersectionality research but also to account for the fluid, dynamic
elements of human identities. For the purposes of this study, identity refers to “the set of
meanings that define individuals as occupants of roles in society, members of groups or social
categories, or unique persons with characteristics that identify them” (Burke and Stets, 2022, p.
1). Therefore, each person can occupy a range of roles and identities—from a diligent tennis
player and studious research assistant to a supportive parent and moral churchgoer. We argue
that by accounting for non-innate characteristics that define individual identities, intersectionality
scholars can better anticipate how people will confront and respond to societal pressures to

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define them, as well as identify the potential facilitators of and obstacles to entrepreneurship
more accurately (Barnard et al., 2019; Brzozowski et al., 2014; Kalnins and Chung, 2006).
In support of these theoretical contributions, we estimate a probabilistic model with fixed
effects and examine the embedded intersectional effects on immigrant entrepreneurship across
the United States. With a novel dataset assembled from multiple sources, we conduct a
difference-in-difference analysis to establish causality. This advanced identification strategy
provides robust evidence of the heterogeneous effects of host country institutions (i.e., state-level
E-Verify regulations) on immigrants.3 We also consider institutional effects separately for two
groups of entrepreneurs: those who started their businesses without incorporating and those who
have incorporated businesses. The results provide actionable insights for designing immigration
policies that can augment the contributions of immigrant entrepreneurs. As the United States,
Europe, and the rest of the world grapple with immigration policies and other grand challenges
(Fernhaber and Zou, 2022; Nayak et al., 2025), such research is imperative and exigent.
2. Literature Review and Theory Development
2.1 Intersectionality theory and immigrant entrepreneurship
Intersectionality theory suggests that the effects of a person’s background and
experiences cannot be assessed without accounting for the intersecting contextual factors that
create each person’s unique social profile (Crenshaw, 1989; Mindes and Lewin, 2024; Thatcher
et al., 2023). Contextual factors generate unique opportunities and challenges, such as access to
capital, networks, resources, and markets. As Murzacheva et al. (2020) show, gender differences
in human capital and spatially concentrated deprivation interact to compound disadvantages for
female entrepreneurs. Citing the intersection of different resource profiles, market conditions,
3 We also used propensity score matching and coarsened exact matching methods as robustness checks, as we discuss subsequently.

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and immigration policies in operation at the time they receive residency status, Wang and Warn
(2018) identify differences in the opportunity structures and choices of entrepreneurial activities
among various Chinese immigrants in Australia. Immigrants from the same country may also
have different experiences, depending on whether they come from a rural or urban area or move
to a region with a larger or smaller immigrant population.
Such an intersectionality-oriented framework can depict the complex, interconnected
ways in which an individual background and experiences intersect with the context, but the
experiences are also never static. It remains necessary to account for changing profiles, policies,
cultural norms, locations, and other contextual factors. In addition, though intersectionality
theory has been applied to identify and understand the disadvantages for various social groups,
such as Black women (Carter et al., 2015; Gill and Ganesh, 2007; Martinez Dy et al., 2017), it
also might help explain positive outcomes for other social groups. Contextual advantages arise
for various groups, such as Black female entrepreneurs who target niche beauty markets
(Harvey, 2005), gay men in the fashion industry (Stokes, 2015), a privileged religious
background that compensates for other intersectional deficits (Tlaiss and McAdam, 2021), and
successful Indian tech entrepreneurs in the United States (Kerr and Kerr, 2020; Saxenian, 2002;
Toussaint-Comeau, 2008).
2.2 Higher education as a resource and identity
While prospective entrepreneurs require human, financial, and social capital to succeed
(Blumberg and Pfann, 2016; Viinikainen et al., 2017), Huggins and colleagues (2014) argue that
their higher education is the most prominent factor in determining their ability to establish viable
businesses. Arguably, human capital obtained through education is the strongest driver of
entrepreneurship (Millán et al., 2014), such that “Few other explanatory variables, including

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ethnicity, family background, social capital, business strategy, or organizational structure of the
venture, possess as much explanatory power” (Parker, 2009, p. 582).
From an intersectionality theory perspective, we propose that higher education might
have a particularly significant effect on the entrepreneurial journeys of immigrants for four main
reasons. First, most immigrants possess limited financial resources, which is especially true for
recent waves of immigrants (Love and Schmidt, 2015). Fairlie and Lofstrom (2015) argue that
inadequate access to financial resources constitutes a primary barrier to potential entrepreneurs.
Although the available evidence remains limited, it suggests that for immigrants starting new
lives with limited financial resources, education is critically significant because they are even
more reliant on their skills, along with those of others whom they can sign in to join their cause.
In a sense, formal education thus becomes a prerequisite for acquiring, absorbing, and using
information to identify opportunities (Kolstad and Wiig, 2015).
Second, education provides relevant signals to other actors in entrepreneurial ecosystems,
such that it can attract others who possess their own high levels of human capital. The
entrepreneur’s education, as a signal of their human capital, thus might both attract financial
capital (Dheer, 2018) and amplify its efficacy (Kolstad and Wiig, 2015). Entrepreneurs with
more skills are likely to attract employees with more skills, for example (Eriksson and Rataj,
2019). These effects are likely intensified among immigrant entrepreneurs, who are not yet
deeply embedded in the entrepreneurial ecosystems in their new host country environments.
Third, in a related sense, higher education helps immigrants build connections and
navigate challenging new social environments. With a case study, Griffin-el and Olabisi (2018)
describe how skilled immigrants from poor neighboring countries work to reconfigure their
resources and relational positions to overcome unfavorable conditions in South Africa.

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Immigrants without requisite human capital instead struggle to comply with local regulations or
secure resources to launch new ventures (Konstos, 2003). Greater human capital can support
cultural acclimation into new environments too; in studies conducted in various host nations,
consistent evidence shows that educated immigrants attain greater success assimilating into the
host social and economic markets (Apler and Tosteby, 2019; Haberfeld et al., 2020).
Fourth, higher education is pivotal in shaping individual identities, affecting people’s
values, beliefs, and worldviews. As people progress through various educational experiences,
they develop a sense of self that is intrinsically linked to their academic achievements,
intellectual pursuits, and social interactions in educational settings (Higgins, 2011). This
educational identity extends beyond the classroom and formal qualifications to affect thought
processes, cultural awareness, career choices, social circles, and perceptions of and engagement
with the wider world. In countries that value educational attainments, higher education often
provides a defining characteristic that determines social status, opportunities, and personal
relationships. This element of their identity is so significant that people advertise it by adorning
their bodies with clothes, hats, and other memorabilia featuring logos of their alma mater.4
Finally, education also serves as a proxy for social class (Archer et al., 2002; Strong, 2007). As
an integral way for how people define themselves and are defined by others in society, education
acquires even particular relevance for immigrants, as a means to mitigate adaptation challenges
and overcome language, culture, or professional recognition barriers (Li and Sımpson, 2013).
4 Possession of a college degree is also used to segment electorates. After the last U.S. Presidential election, many
pundits claimed the existence of a post-racial world. They sought to explain the outcomes by asserting that college
education represents an important differentiator that separates the population into social classes. Therefore, the
election results reflected the intersectional effects of social class, ethnicity, and attitudes (e.g., toward immigration),
even more so than race or gender. Regardless of the validity of this claim, it highlights the centrality of education to
individual identities.

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Relatedly, some scholars argue that despite the potential empowerment enabled by higher
education, educated immigrants still encounter challenges in labor markets, especially when it
concerns upward mobility to senior management roles (Belford and Lahiri-Roy, 2018). Saxenian
(2002) found that immigrants were more likely to be in professional (technical) roles rather than
in managerial ones. This has led some scholars (e.g., Dheer, 2018) to posit that the prevalence of
such a glass ceiling may push immigrants toward pursuing an entrepreneurial route on the
socioeconomic ladder. Fairlie and Lofstrom (2015) concur and provide anecdotal evidence that
attributes differences in entrepreneurial activity among Asian and Hispanic entrepreneurs to
differences in their education levels.
In summary, higher education is a vital resource in and of itself, but it also serves as a
conduit for acquiring the financial and social capital necessary for entrepreneurship. It is
foundational for identity too such that it often serves as a primary motivation for human action,
including venture creation (Boyd et al., 2021; Leitch and Harrison, 2016; Mmbaga et al., 2020).
Thus, and in line with prior research (Peroni et al., 2016; Vinogradov and Kolvereid, 2007), we
expect higher education to serve as a critical entry point into entrepreneurial ecosystems in
knowledge-based economies, as depicted in Figure 1. Formally, as a baseline hypothesis, we
predict:
Hypothesis 1: Human capital acquired by immigrants through higher education is
positively associated with new venture creation.
***Insert Figure 1 about here ***
2.3 Entrepreneurial mindmaps and immigrant entrepreneurship.
Hofstede states that “each person carries a certain amount of mental programming that is stable
over time and causes that person to display more or less the same behavior in similar situations
(1981, p. 15). While these predictions are far from perfect, they do leave lasting mental

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mindmaps that guide individuals’ behaviors. Recognizing this, entrepreneurship scholars have
called for examining the combined effects of cultural mindmaps and human capital, we know of
no such studies (Kim et al., 2006).5 We propose a way to advance intersectionality theory,
namely, by examining the effect of the entrepreneurial mindmaps that the culture of immigrants’
home countries have imprinted on them on their new venture creation in the United States.
Immigrants also might be generally predisposed to new venture creation by virtue of their
immigration experience (Hormiga and Bolívar-Cruz, 2014; Senor and Singer, 2009, 2009), but in
addition, educated immigrants from countries that assign a higher value to entrepreneurialism
should be uniquely and especially likely to initiate new venture creations in the U.S. context for
four main reasons.
First, because of its influence on beliefs, assumptions, motives, and behaviors, culture has
a lasting impact on entrepreneurial intentions. New venture formation requires considerable time
and effort and involves multiple processes, from ideation and planning to building stakeholder
coalitions and obtaining resources, so it represents a risky, conscious decision (Krueger et al.,
2000; Moriano et al., 2012). Prior exposure to entrepreneurship can make this decision seem less
daunting and more appealing though (Krueger, 1993), which should strengthen entrepreneurial
intentions (Kolvereid and Isaksen, 2006). If the prior exposure is routine and deeply embedded in
the societal culture, prospective entrepreneurs should be even more likely to regard new venture
formation as a “normal” career option. Because culture imposes lasting mindmaps (Hofstede,
1984), we expect entrepreneurial norms to travel with immigrants to new host countries.
5 The effects of culture, customs, traditions, and norms persist because of their embeddedness (Hayton et al., 2002;
Popli et al., 2024; Zhao and Wry, 2016). Most research that demonstrates such persistence investigates the effects of
individualism, power distance, masculinity, or uncertainty avoidance dimensions, which also yields mixed evidence.
Composite constructs thus might be more appropriate for conceptualizing entrepreneurial culture (Hayton et al., 2002;
Hayton and Cacciotti, 2013). Accordingly, we focus on elements of culture that relate to innovation, creativity, risk-
taking, and wealth orientation. Such a value-based approach captures the essence of entrepreneurship and provides a
more direct conceptualization of entrepreneurial culture (Davidsson, 2016; Stephan and Uhlaner, 2010).

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Second, immigrants often confront unfavorable job markets (Park et al., 2022). This
barrier may deter some people from immigrating at all, but it can strengthen the resolve of those
who still decide to migrate to explore new venture creation as an alternative, attractive career
option, especially if they come from entrepreneurial cultures. They likely will be alert to
entrepreneurial opportunities, which is another key determinant of new venture formation (Foss
and Klein, 2010; Kirzner, 1999; Shane, 2003; Valliere, 2013).
Third, immigrants from all backgrounds may be predisposed to entrepreneurship as a
career option, yet some of them may assess the challenges more positively, while others regard
them as relatively more daunting (Moriano et al., 2012). Different predispositions significantly
alter the probability of actually initiating the transition to an entrepreneurial career. Thus,
immigrants from entrepreneurial cultures appear likelier to initiate new venture creation. As
Vinogradov and Kolvereid (2007) indicate, cultural attributes of the home countries can
influence immigrants’ new venture creation in their host countries.
Fourth, educated immigrants, in particular, are in an improved position to integrate
knowledge and experiences from their native and host countries. They navigate a third space
(Higgins, 2011), decentered from their identities in both home and host cultures. The
transcultural identity they develop blends ties to their home culture with the competencies
needed in the host culture. In obtaining higher education, they probably would have gained
exposure to more intercultural interactions, which encourages identity expansion (Higgins,
2011), broader perspectives, better acculturation, and multifaceted identities, all of which can
facilitate success in globalized, interconnected societies (Li and Sımpson, 2013). Therefore, we
reason that for educated immigrants from cultures that emphasize self-reliance and risk-taking,
identity expansion will increase their likelihood of identifying and exploring entrepreneurial

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opportunities. In turn, an entrepreneurial culture should have both direct and indirect
(moderating) effects on new venture creation:
Hypothesis 2: Entrepreneurial mindmaps imprinted by the entrepreneurial culture in
immigrants’ home countries (a) are related to new venture creation and (b) amplify the
effect of human capital on new venture creation.
2.4 Embedded effects of host country institutions on immigrant entrepreneurship
Intersectionality theory suggests that “entrepreneurship research needs to go beyond
ontological individualism by situating the entrepreneur within the context in which they operate”
(Lassalle and Shaw, 2021, p. 1501). Using structuration concepts, we might anticipate that social
interactions take place in the context of the prevailing institutions (Garud et al., 2014; Suddaby et
al., 2015; Van Burg et al., 2022). Institutions refer to the rules of the game in a society that
constrain and shape human interaction (North, 1990), including immigrant entrepreneurship. The
institutional environment represented by different countries or regions—as manifested by the
migration experience (Hormiga and Bolívar-Cruz, 2014), entry mode for immigrants (Kerr and
Kerr, 2020), legal status in the host country (Lofstrom, 2002), and the availability of public
financial support (Millán et al., 2014)—exerts various effects on outcomes such as the type of
entrepreneurship and innovation (Simón-Moya et al., 2014).
Building on such research, we consider host country attitudes toward immigration and the
level of welcome experienced by immigrants. All the immigrants in our study migrate to the
United States, so we take a subnational view and predict that the effect of subnational institutions
can be gauged by the regulations in effect. Their varying policy measures should have
differential implications for immigrants and natives. For the purposes of this study, we leverage
state-level E-Verify policies; not all states mandate E-Verify reporting, so variability across
states enables us to determine if formal institutional differences within the destination country

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affect immigrant entrepreneurship. Notably, most employers voluntarily participate in E-Verify,
but only some states have passed legislation mandating it6 and establishing punitive actions on
noncompliant employers, which have direct impacts on the labor market.
Mandatory E-Verify signals a generally immigrant-unfriendly policy, which leads to
contractions in the labor market, in both supply and demand (Amuedo-Dorantes and Bansak,
2012; Bohn et al., 2015). In particular, E-Verify affects employment and earnings rates among
Hispanic non-naturalized immigrant workers (Orrenius and Zavodny, 2021) but has no effect on
labor market outcomes for U.S. natives. In this way, E-Verify mandates appear to enhance labor
market outcomes for workers who tend to compete with unauthorized immigrants (Orrenius and
Zavodny, 2015). For example, employment rises among male Mexican immigrants who are
naturalized citizens, and earnings rise among U.S.-born Hispanic men. Enrolling in the program
is costly for employers, in terms of compliance and difficulty in hiring workers (Orrenius et al.,
2020). Furthermore, entrepreneurs in mandatory E-Verify states (e.g., Arizona) report increased
worker turnover.7 Despite these clear effects of E-Verify mandates on labor markets, we know of
no research that specifies the impact on entrepreneurship by immigrants.
Considering its contracting effect on the labor market, E-Verify could create an
unintended positive externality for entrepreneurial markets. That is, their ineligibility to
participate in the labor markets might force some immigrants to pursue self-employment options
or make new venture creation appear more attractive. Thus, we predict that E-Verify may drive
some immigrants out of local job markets, but it also should exert both direct and indirect
positive effects on entrepreneurship (Figure 1). As direct effects, it signals to both employers and
6 The employee social security numbers that employers enter into the E-Verify system gets matched against Social Security
Administration and Department of Homeland Security records, and any discrepancy communicated to the employer must be
corrected within 10 days. Otherwise, the employer must terminate the employee. In addition to discouraging employment of
undocumented workers, only states that mandate E-Verify gain the right to conduct audits.
7 See https://www.E-Verify.gov/sites/default/files/everify/data/EVerifyArizonaMandExperience2012.pdf

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employees a preference for native workers, as well as higher compliance costs, which should
have particularly strong impacts on immigrants who perceive the hostility of the labor market8
and who possess the human capital needed to venture out on their own.
With regard to the indirect effects, we anticipate a more complex effect of E-Verify
systems. As we have noted, intersectionalities are embedded in institutional contexts, and
identity is a contingent process that depends on the dialectic between individuals and their
environments in the sense that “individuals are influenced by the social, historical, economic,
cultural, linguistic, and political constraints of their environment.” Identity also gets “crafted and
re-crafted” through “interactions between the individual, society, and culture” on an ongoing
basis (Lewis, 2015, p. 665). Therefore, the intersectional effects of immigrants’ human capital
and their entrepreneurial mindmaps should depend on the embedded context of E-Verify, as a
host country institution. Notably, Fairlie and Lofstrom (2015) noted that Mexican entrepreneurs
exhibit higher business formation rates in Mexico than do those in the United States. Further,
these rates also vary across states, with immigrant businesses concentrating in California, New
York, Florida, and Texas. We argue that this interesting puzzle might be solved by addressing
how institutional power structures in host countries (i.e., E-Verify mandates at the subnational
level) amplify or inhibit the new venture creation efforts displayed by educated immigrants with
a cultural predisposition toward entrepreneurship. That is, educated immigrants from
entrepreneurial backgrounds have stronger incentives to leverage their skills and identities to
pursue entrepreneurship when faced with unwelcoming labor market conditions.
Two recent studies provide indicative evidence in support of this assertion. Capelleras et
al. (2019) suggested that social acceptance of entrepreneurship and entrepreneurial role models
8 We also consider the potential impact of E-Verify on non-entrepreneur employees and run separate event studies for immigrants
and natives to test for any labor displacement effects associated with the policy. These results are discussed subsequently.

Immigrant entrepreneurship
17
further strengthen the positive effect of education and experience on entrepreneurship. Similarly,
Mata and Alves (2018) find that firms created by immigrants have a lower rate of survival than
those founded by natives. However, their work experience in the host country and the size of the
immigrant’s national community improve the survival chances of immigrant ventures (Huggins
et al., 2017). This suggests that the unique intersection of higher education, cultural mindmaps,
and institutional contexts can create advantages for certain immigrant populations rather than just
disadvantages. Educated immigrants from entrepreneurial backgrounds are better able to
leverage their skills and identities to pursue entrepreneurship when faced with less welcoming
labor market conditions in the host country. We, therefore, expect that a positive intersection of
immigrants’ human capital and their entrepreneurial culture mindmaps with host countries’
regulatory environment should result in maximum entrepreneurial activity. Formally, the positive
intersection of immigrants’ human capital and entrepreneurial culture mindmaps with host
countries’ regulatory environment should result in maximal entrepreneurial activity. Therefore,
Hypothesis 3a: E-Verify mandates are positively related to the new venture creation by
migrants.
Hypothesis 3b: E-Verify mandates in the host country further moderate, i.e., amplify or
weaken, the intersectional relationship impact of human capital and entrepreneurial
mindmaps on new venture creation. Specifically, a combination of high human capital and
entrepreneurial mindmaps with the challenging host environment (due to E-Verify) leads
to the highest rates of new venture creation by immigrants.
3. Empirical strategy
3.1 Data
The data for this study come from multiple sources. The main source is CEPR, which
converts raw data available through the National Bureau of Economic Research, the Bureau of
Labor Statistics, and the Census Bureau to derive detailed information about populations’
demographic characteristics, education, and labor market status. The reliability and

Immigrant entrepreneurship
18
representativeness of CEPR data match those of Census surveys. The CEPR variables are also
consistent, though this database also includes a few unique variables, such as a “rural” indicator
variable that we include in the model. With appropriate sample weights, we correct for sample
imbalances in the estimations. However, CEPR data only run till 2019.
Since CEPR uses the census data, it also adopts a 4-8-4 rotation pattern: Each household
is surveyed for 4 consecutive months, not interviewed for 8 months, and then interviewed for 4
months. Different members can be interviewed in each instance. Although the data for this study
do not include any repeat respondents in the same year, a household might recur with a different
respondent. We treat these respondents as independent. Therefore, we rely on pooled individual-
level data without longitudinal data about the respondent.
The study period spans 2005 to 2019, during which time most states that mandated E-
Verify implemented this requirement. For the timing of E-Verify mandates, we rely on the Cato
Institute. Notably, including years beyond 2019 does not add any further information to the
model because E-Verify no longer exhibits any variations thereafter. This choice also avoids the
structural break in the data due to COVID-19. As Haltiwanger (2022) reports, applications for
new businesses dropped drastically in the first half of 2020 but surged in the second half,
accompanied by massive employment contractions and contractions among existing small
businesses. Increases in new business were also uneven by sector. Therefore, analyzing pre-
pandemic years jointly with the post-pandemic years would be inappropriate for our model,
further justifying 2019 as the end year for this study.
The cultural index data come from the World Values Survey, a nationwide survey
conducted in waves, such that representative samples of adult populations in different countries
complete surveys in different years. These samples do not exhibit any continuity across waves;

Immigrant entrepreneurship
19
respondent identification numbers refer to different respondents and not the same person.
Therefore, we use time series rather than panel data in this data set.
Merging various datasets requires conformity in naming conventions. Thus, we carefully
compared the names listed in the data sets for people’s countries of birth and adjusted them as
needed. Similarly, only some data sources provide state-level information about the District of
Columbia, so we dropped it for consistency. Annex 1 offers more comprehensive details related
to the data sources, variables, and other data set considerations.
3.2 Variables
3.2.1 Immigrant entrepreneurship. Respondents who self-identify as entrepreneurs and
report hiring at least one other employee (besides themselves) constitute entrepreneurs for this
study. We thus preclude certain types of self-employments (e.g., gig jobs). Assuming
responsibility for paying someone else’s salary is both risky and entrepreneurial. For the
immigrant entrepreneurship binary variable in particular, we assign a value of 1 if the self-
identified entrepreneur immigrated from another country (and 0 otherwise), as revealed by their
self-reported country of birth.9 Information about respondents’ parents’ citizenship helps confirm
their immigrant status.
In our database, 7.08% of the total population are entrepreneurs, and among this set of
entrepreneurs, 7.06% are immigrants. We also note state-level disparities. For example, in
Wisconsin and Ohio, only about 3% of entrepreneurs are immigrants; in California and Arizona,
the rate varies between 16% and 25%. Year-on-year changes also emerge from the data: The
percentage of entrepreneurs who were immigrants in North Carolina increased from less than 6%
in 2010 to almost 10% in 2014. In terms of ethnic composition, 45.70% of immigrant
9 Country of birth provides an appropriate measure, because the data set does not include information about the length of time the
person has spent in the United States or the years since their immigration.

Immigrant entrepreneurship
20
entrepreneurs identify as Hispanic, 28.01% as Asian, 23.04% as White, and 3.05% as Black.
Mexico is the country of origin with the largest number of immigrant entrepreneurs, followed by
Korea, India, China, the Philippines, Vietnam, Cuba, Germany, and Poland. In terms of
education, the largest percentage of immigrant entrepreneurs are high school graduates, followed
by college graduates, and then some college. About 36% of immigrant entrepreneurs are women.
Some studies predict a qualitative difference between unincorporated and incorporated
businesses (Fisher and Lewin, 2021; Mindes and Lewin, 2024), so we ran separate estimates for
these business categories,10 identified according to the CEPR database. Unincorporated
businesses are either sole proprietorships (owned by a single individual) or partnerships (owned
by two or more people).11 These easily, inexpensively formed businesses represent the simplest
and most common legal business structure used to start a business, and they make no distinction
between the business and the owner. However, such complete control also imposes unlimited
personal liability and makes raising capital harder. Incorporated businesses instead tend to be
associated with opportunity entrepreneurship and signal success and assimilation (Levine and
Rubinstein, 2017; Mindes and Lewin, 2024).
3.2.2 Higher education. We use a College Plus indicator variable that takes a value of 1
when the respondent has attained a college-level or advanced education and 0 otherwise.
3.2.3 Entrepreneurial mindmaps. We developed an Entrepreneurial (culture) Index,
which captures a respondent’s entrepreneurial mindmaps based on their country of origin. We
build this index from the Schwartz survey responses from the World Value Survey. We drop any
years and countries with missing data. We use the latest survey available for the countries that do
10 We thank an anonymous reviewer for suggesting this analysis.
11 Unincorporated businesses make up a large proportion of U.S. businesses. According to the IRS, there were more than 25 million
sole proprietorships, versus about 3 million partnerships, in operation in 2021.

Immigrant entrepreneurship
21
not have recent surveys. A variable that is important in the CEPR data for constructing the
cultural index value is the respondent’s country of birth. In some cases, respondents’ country of
birth is not specific but stated as a continent, for instance, Africa or Europe. We have dropped
such data points. While the World Values Survey covers 58 countries, it does not provide
information on some countries like Saudi Arabia, Slovakia, and Myanmar. We dropped data for
countries for which we cannot define a value in the cultural index, which is about six percent of
the data.
Using factor analysis, we combine eight variables to construct the index: how important it
is for the respondent to think of new ideas and be creative, live in secure surroundings, be
successful and rich, be adventurous and take risks, behave properly, embrace tradition, and do
something good for society. This value-based approach offers a relatively direct measure of
entrepreneurial culture (Stephan and Uhlaner, 2010). Each variable takes a score from 1 to 6,
where 1 represents “very much like me” and 6 is “not at all like me.” For five of the eight
variables, a lower score implies a high propensity for entrepreneurship. For the remaining three,
a lower score suggests a lower propensity. We recode the latter three variables and combine them
with the rest of the Schwartz variables to ensure comparability.
Because entrepreneurial culture is a latent variable, making comparisons across groups or
time requires measurement invariance. Therefore, we adopt the same factor loadings across time
in the factor analysis. We also rotate the factors using the Varimax option. According to
Cronbach’s test, the variables in the factor relate to the latent variable. The scale reliability
coefficient exceeds 70%, which verifies the index’s reliability. Finally, we rescale the index to a
0–100 scale, where higher values imply a more entrepreneurial culture.

Immigrant entrepreneurship
22
3.2.4 Host country institutions. The E-Verify indicator variable identifies states with
legislation in place for mandatory reporting, such that it offers a proxy for immigration-specific
regulations at the state level. A few states had started requiring E-Verify as early as 2007, but
most of those requirements were specific to government contractors or firms of a certain size. By
2012, many states had made it mandatory for all employers to use E-Verify. In detail, the first
state to mandate its use was Arizona in 2007, followed by Mississippi and South Carolina in
2008; Utah in 2010; and Alabama, Georgia, North Carolina, and Tennessee in 2011.12 As noted
previously, we obtained data related to the state-level E-Verify legislation from the Cato
Institute, which compiles this information from various state policies, then cross-verified them
with prior studies of E-Verify (e.g., Orrenius et al., 2020).13
3.3. Control variables
In line with prior research (Dheer, 2018; Fairchild, 2010; Kerr and Kerr, 2020), we
control for each respondent’s age, race, education, gender, marital status, country of origin, and
number of children. Although an immigrant's primary language could be an important predictor
of entrepreneurial success, we lack a reliable measure of the language spoken by the immigrant.
Such demographic factors can strengthen or weaken the probability of starting new ventures. We
control for family income too. When procuring external financing is costly or challenging,
family income and savings can determine entrepreneurship (Dyer et al., 2014; Gentry and
Hubbard, 2000). Furthermore, we control for state-, industry-, and year-fixed effects. In
particular, we include an indicator of each state’s past unemployment rate.
12 An additional state, Florida, mandated it in 2024; the results of which remain to be seen.
13 National Census surveys provide the main sources of secondary data about immigration; they identify immigrants
only as foreign-born. Limited literature on undocumented immigrants uses aggregation techniques to make inferences,
but they likely underestimate true population numbers (e.g., Orrenius and Zavodny, 2015). Data limitations prevent
us from differentiating legal and illegal immigrants.

Immigrant entrepreneurship
23
At a network level, we seek to account for the potential effect of immigrants’ social or
ethnic networks (Toussaint-Comeau, 2008) by combining a measure of the ethnic enclave with a
measure of network quality,
𝑁𝑒𝑡𝑤𝑜𝑟𝑘!,# =𝐸𝑡ℎ𝑛𝑖𝑐𝐸𝑛𝑐𝑙𝑎𝑣𝑒!,# ×𝑁𝑒𝑡𝑤𝑜𝑟𝑘𝑄𝑢𝑎𝑙𝑖𝑡𝑦#,
where the ethnic enclave variable for area 𝑖 (metropolitan if the respondent lives in a
metropolitan area and state otherwise) is defined as follows:
𝐸𝑡ℎ𝑛𝑖𝑐𝐸𝑛𝑐𝑙𝑎𝑣𝑒!"# =𝑙𝑛
$%&'()*+,*-(+-.(*,)+&*/)+%-*#*!0*1)(1*! 2+31.*-+-%.13!+0*!0*1)(1*!
4
$%&'()*+,*-(+-.(*,)+&*/)+%-*# 2+31.*-+-%.13!+0*!0*35(*6+%03)7
4,
and network quality reflects the deviation of the average self-employment rate of the ethnic
group relative to the national average self-employment rate,
𝑁𝑒𝑡𝑤𝑜𝑟𝑘𝑄𝑢𝑎𝑙𝑖𝑡𝑦#=𝐴𝑣.𝑆𝑒𝑙𝑓9𝐸𝑚𝑝𝑙𝑦𝑚𝑒𝑛𝑡9𝑟𝑎𝑡𝑒#−𝑁𝑎𝑡𝑖𝑜𝑛𝑎𝑙9𝐴𝑣.𝑆𝑒𝑙𝑓𝐸𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡9𝑟𝑎𝑡𝑒.
With a dummy variable, we control for the effect of arriving in the United States as a
child versus as an adult. To create this variable, we had to make certain assumptions, given the
constraints of the CEPR data, which do not provide the exact year of arrival but instead indicate
a span of years. For example, if an immigrant arrived before 1950, the arrival time horizon for
the CEPR data is 10 years (1940–1949). Therefore, we choose the middle of the 10-year range to
indicate the year of arrival, which still enables us to recognize whether each immigrant arrived as
a child or an adult.
3.4 Methodology
To understand the self-selection of immigrants into entrepreneurship, we limit the study
population to those who have (vs. have not) started a business. In addition to the estimates of the
main specification, our robustness checks focus on businesses with two or more employees. The
data are organized in repeated cross-sections. The CPS person-level final basic weight represents

Immigrant entrepreneurship
24
the survey weight in all estimations. The baseline specification, with coefficients estimated by
maximum likelihood, is as follows:
𝑷𝒓#$𝑰𝒎𝒎𝑬𝒏𝒕𝒓𝒆𝒊,𝒕 = 𝟏/𝑿𝟏, 𝑿𝟐… 1 = 𝐆𝒊,𝒕#(𝜷𝟏𝑪𝒐𝒍𝒍𝒆𝒈𝒆𝒑𝒍𝒖𝒔 + #𝜷𝟐𝒉𝒐𝒔𝒕_𝒊𝒏𝒔𝒕𝒊𝒕𝒖𝒕𝒊𝒐𝒏𝒂𝒍_𝒄𝒉𝒂𝒓𝒂𝒄𝒕𝒆𝒓𝒊𝒔𝒕𝒊𝒄 +
𝜷𝟑𝒉𝒐𝒎𝒆_𝒊𝒏𝒔𝒕𝒊𝒕𝒖𝒕𝒊𝒐𝒏𝒂𝒍_𝒄𝒉𝒂𝒓𝒂𝒄𝒕𝒆𝒓𝒊𝒔𝒕𝒊𝒄) + 𝐙𝒊,𝒕𝛗 + 𝐅𝐄𝒊,𝒕𝜸 + 𝜺𝒊,𝒕 ), (1)
where 𝐺(𝑧)= Φ(𝑧)=∫Φ(𝑣)𝑑𝑣
$
%& , such that Φ(𝑧)='
√)* 𝑒'()
), and Z!,+ is a vector of immigrant
control variables.
To observe any potential moderating effects of culture at the cross-national level and
government policy at the subnational level, we also introduce two- and three-way interactions
among human capital (education), the entrepreneurial culture of immigrants’ home country, and
the mandatory E-Verify requirements at the state level in the host country. Rather than existing
qualitative methods for studying intersectionality, this study adopts the best practice
recommendations of Block et al. (2023), who provide an excellent description of how to use an
interaction method to study intersectional effects in quantitative research.
To sharpen identification of the E-Verify effects, we conduct a quasi-experimental,
difference-in-difference (DiD) analysis. Thus, we avoid assigning treatments randomly, as would
be the case in a randomized controlled trial, which is preferable for examining causality. In
practice, DiD applications rarely meet all the requirements of a canonical DiD setup, but this
approach offers the next best option when randomization is infeasible (Athey and Imbens, 2017;
Li et al., 2024). As we detailed previously, states’ adoption of E-Verify was staggered, which
means we cannot use a traditional two-period, two-group estimator either (two-way fixed
effects). We need a multiperiod or multiple treatment effects estimator that can measure the
group-time average treatment effect.
Our study cannot accommodate an identifying assumption in which the average outcome
among treated and control groups would follow parallel trends in the absence of treatment. In our

Immigrant entrepreneurship
25
study setting, the parallel trends assumption likely would hold only if conditioned on observed
covariates like education, marital status, family income, and gender. Time-varying confounding
factors, like macroeconomic policies, might also affect treated states differently. Additionally,
the parallel trend assumption could be sensitive to the functional form of the outcome function.
Notably, none of the states that passed mandatory E-Verify legislation reversed it; once states are
treated, they remain treated.
We adopt a methodology proposed by Callaway and Sant’Anna (2021; hereafter, CS), which
relaxes these assumptions and allows for both multiple periods and variations in treatment
timing, as needed to reflect the staggered implementation of E-Verify by different states in
different years. The CS method addresses various multiperiod aggregation schemes and accounts
for heterogeneous treatment effects across dimensions, such that we can determine if the
treatment effect is heterogeneous due to the time of adoption (e.g., 2007 by Arizona relative to
2012 by Alabama) and learn if the treatment effect changes over time. Therefore, we can conduct
an event study for the multiple-period staggered treatment. In addition, CS relaxes the strict
parallel trends assumption by conditioning on covariates. A conditional parallel trends
assumption cannot be tested; CS instead proposes an augmented conditional parallel trends
assumption that tests the “placebo” treatment effect for periods before treatment, for each
group.14 For heterogeneous treatment effects, the pre-trends test may be rejected, even if parallel
trends hold in the pre-treatment period. Among the various empirical methodologies proposed
for event studies, the CS methodology also offers greater flexibility and transparency. For
example, unlike de Chaisemartin and D'Haultfoeuille (2020), CS stress the role of covariates.
14 Testing for preexisting trends has several limitations (https://psantanna.com/files/RSBP_DiD_Review.pdf), including “pre-test
bias” (Roth et al., 2022).

Immigrant entrepreneurship
26
The method accommodates repeated cross-sectional; hence considering that our data are
organized in repeated cross-sections, using the CS methodology enables us to estimate causal
effects with more accuracy.
In detail, CS proposes two control groups, depending on the assumptions underlying the
estimation. One group consists of those who never get treated, and the other contains those yet to
be treated. States that mandate the use of E-Verify are likely to have some unobservable
similarities, so we choose to use the not-yet-treated group as the control group. Doing so avoids
confounding differences across states, such as those due to state-level taxation or labor policies.
The premise of our conditional parallel trend assumption is that, in the absence of specific
policies or institutions that influence immigrants, the decision to become an entrepreneur while
living in a state is influenced mostly by the overall economy. Variation reflects individual
characteristics, like education, marital status, and family income. In support of our conditional
parallel trends assumption, we note that in 2010, the Bureau of Economic Analysis reported a
sharp decline in entrepreneurship.15 We observe a similar decline in immigrant entrepreneurship
in treated states too.
We combine states that mandated E-Verify in the same year into groups and estimate the
average treatment effect for each of these four groups. The average treatment effect on the
treated (ATT) for a group g at a particular time t is given by
𝑨𝑻𝑻(𝒈, 𝒕)= #𝔼[𝒀𝒕(𝒈)− 𝒀𝒕(𝟎)|𝑮𝒈= 𝟏],
where 𝑌
+(𝑔) is the potential outcome observed at time t in group g, and 𝑌
+(0) is the potential
outcome of the not-yet-treated group at time t. The estimation assumes limited treatment
anticipation and conditional parallel trends and also includes other controls. Because the motive
15 https://www.bls.gov/bdm/entrepreneurship/entrepreneurship.htm

Immigrant entrepreneurship
27
for adopting E-Verify mandates largely entailed deterring unauthorized immigrants, it seems
unlikely that the states anticipated or sought an increase in immigrant entrepreneurship. In turn, it
would be difficult for the E-Verify policy to be anticipated by immigrant entrepreneurs.
We also observe the post-treatment effect graphically. For this assessment, we extend our
study period to the years prior to the first treatment in 2007. That is, starting in 2005, we conduct
an event study for immigrant entrepreneurs who employ two or more employees. Reflecting on
Fisher and Lewin’s (2021) evidence that the founders of unincorporated versus incorporated
businesses exhibit different education levels and earning potential, we test the effects of E-Verify
separately for these two groups.
4. Results
In Table 1, Panel 1a details the demographic differences between immigrants and natives
by entrepreneurship status. Natives account for a higher percentage of entrepreneurs; immigrant
entrepreneurs are younger and more likely to be married. A higher percentage of native
entrepreneurs have college degrees compared with their immigrant counterparts (9.49% vs.
7.87%), and their family incomes are higher. Panel 1a also provides comparisons with native and
immigrant workers in the employed population. Then, in Panel 1b, we present the correlations of
all the main independent variables for immigrant entrepreneurs.
***Insert Tables 1 and 2 here ***
Table 2 contains the results of our probit regressions in which we assess the likelihood of
becoming an entrepreneur. It reveals that age is positively associated with the probability that an
immigrant becomes an entrepreneur. Immigrants who are male, married, and inhabitants of rural
areas are also significantly more likely to start businesses. However, the number of children does
not affect this probability, and the log of family income is negatively related to it. Having

Immigrant entrepreneurship
28
supportive networks increases the likelihood of starting a new business in all regression models,
though the coefficient is small. More importantly, the coefficient for higher education is also
positive and significant (varies between .31 and .14). Thus, we find support for Hypothesis 1.16
The entrepreneurial culture coefficient is also positive and significant, though small.
Immigrants who were born in more entrepreneurial countries exhibit a stronger tendency to
create firms in the host country, the United States. These estimates provide support for
Hypothesis 2a, with a small magnitude. Concerning the E-Verify mandates, the positive and
significant coefficient varies between .044 and .089, such that states that mandate E-Verify are
associated with a higher probability of immigrant entrepreneurship.17
When we estimate the two-way (Hypotheses 2b and 3b) and three-way (Hypothesis 4)
interactions, we find that the interaction between the entrepreneurial culture of the home country
and human capital, measured by college (or higher) education, is positive and significant.
However, the moderating effect of human capital on subnational regulations, measured by the
interaction between E-Verify and college (or higher) education, is negative and significant at
10% (Column 5 in Table 2); it becomes non-significant in Column 7. Both E-Verify and college
(or higher) education are indicator variables, so these interaction results must be interpreted
using margins plots (Arora et al., 2024; Brambor et al., 2006; Clark et al., 2022)
In Figure 2, the lines that represent the presence or absence of E-Verify mandates depict
the interaction term from Column 7 in Table 2. The orange and green lines represent college-
16 As robustness tests, we conducted more nuanced analyses with five educational categories (less than high school,
high school, some college, college degree, and advanced degree). These results, available upon request, reveal a
gradient effect, such that each successive education level indicates a progressively stronger, positive association with
entrepreneurship, from high school (9.4 percentage points) and some college (16 percentage points) to college (23
percentage points) and advanced degrees (28.7 percentage points). This analysis provides further evidence in support
of our main theoretical arguments.
17 We used an indicator variable for driver’s licenses provided without a social security number by states, as an
alternative proxy for state-level, immigrant-unfriendly policies; it was not significant.

Immigrant entrepreneurship
29
educated and non-college-educated immigrants in states that mandate E-Verify; the red and blue
line indicates the same groups in states that do not. Immigrants born in a country with a higher
entrepreneurial culture and higher education levels exhibit a high probability of becoming
entrepreneurs. The effect of this interaction is small in terms of the estimated coefficient, but the
margins plot reveals that the probability of immigrants starting a business is higher when the
entrepreneurial index increases, in support of Hypothesis 2b.
*** Insert Figure 2 here ***
A comparison of the orange and green lines with the red and blue lines further indicates
that the probability of college-educated immigrants being entrepreneurs is greater than that of
non-college-educated immigrants, in both states that mandate E-Verify and those that do not.
Therefore, education moderates the relationship between host country subnational regulations
and new venture creation, as we posited in Hypothesis 3b. These interactions, in turn, lead us to
conclude that the home country’s entrepreneurial culture and human capital both promote
immigrant entrepreneurship. That is, entrepreneurial culture in the country of birth and higher
education form a key determinant of starting a business, irrespective of state-level regulations. In
addition, host country regulations and entrepreneurial culture jointly moderate the relationship
between higher education and new venture creation, creating the highest positive effect on new
venture creation. Even in the face of unfavorable state-level institutions, immigrants can adapt
and start businesses, achieved through their higher education and the entrepreneurial culture that
they carried from their home countries.18
18 Running the probit model and DiD estimates with natives reveals that some controls remain significant, but we do
not find evidence that the intersectionality of the three main factors affects native entrepreneurship. The DiD
estimation does not establish causality. These results are available on request.

Immigrant entrepreneurship
30
Leveraging the different implementation times for E-Verify across states, we further
explore causality by estimating a DiD model,19 the results of which we present in Table 3. The
specifications include the control variables from the probit estimations (i.e., age, age squared,
being a male, being married, having a child, rural origin, business tax rank, race, and networks
[log]). We present the ATT for the four groups that underwent the treatment, defined as E-Verify
mandates in the years 2007, 2008, 2010, and 2011. We use the CS doubly robust estimation
methodology.
*** Insert Table 3 here ***
The results show a positive effect on entrepreneurship in 2007 when E-Verify was first
mandated in Alabama. Immigrant-founded firms with more employees exhibit a larger and
positive causal effect, in line with research that documents larger firms’ comparative compliance
advantages (Orrenius et al., 2020). The second group to mandate E-Verify in 2008 (Mississippi
and South Carolina) continues to show the positive effect of larger firms on immigrant
entrepreneurship. The year 2010 seems peculiar; its coefficient is negative.20 It is also a
watershed year in the post-recession period, as noted by the Bureau of Labor Statistics.21 New
establishment growth rates were very low in 2010, after which trends reversed in terms of new
establishments and employment creation. We witness a similar change in direction in our
sample, and the results become even stronger when we break it down into incorporated versus
unincorporated entrepreneurs.
19 As a robustness check, we consider causality by using propensity score matching and coarsened exact matching,
such that the samples were matched on observable variables for migrants using the control variables. The ATT
values for E-Verify were similar to the DiD estimation results. These results are available on request.
20 We observe a negative effect for native entrepreneurs as well.
21 https://www.bls.gov/bdm/entrepreneurship/entrepreneurship.htm

Immigrant entrepreneurship
31
Figure 3 (Panels 3a and 3b) depicts the dynamic effects of the ATT, before and after the
E-Verify treatment. As these plots show, the average treatment effects on immigrants vary with
the length of exposure to the treatment. A distinct increase in the number of immigrant
entrepreneurs occurs after the E-Verify mandates, in further support for Hypothesis 3a.
*** Insert Figure 3 here ***
With Table 4 and Figure 4, we detail how entrepreneurs running unincorporated versus
incorporated businesses respond differently to the E-Verify mandate. Unincorporated
entrepreneurship increases immediately; incorporated entrepreneurship increases after a lag.
*** Insert Table 4 and Figure 4 here ***
When we repeat these estimations using the methodology proposed by de Chaisemartin
and D'Haultfoeuille (2020), the results are similar. Although our focus is on the group average
treatment, we include the overall weighted ATT as well.22 A pretest, checking for pre-trends by
estimating the chi-square statistics of the null hypothesis that all pretreatment ATTs for all
groups equal 0, can be rejected. However, as Roth et al. (2022) note, with heterogeneous
treatment effects, the pre-trends test might be rejected even if parallel trends hold in the pre-
treatment period.
We have argued that the E-Verify mandate led to a contracted labor force, which
promoted necessity-driven entrepreneurship by immigrants. Census data cannot support tests of
this predicted chain of effects, but by using a DiD analysis, we find evidence in support of this
phenomenon by studying the impact of the E-Verify mandates on immigrant employment among
non-entrepreneurs, as we detail in Annex 2. It shows that employment decreased after the E-
Verify mandates. These results complement Orrenius and Zavodny's (2015) findings that, in
22 The overall average treatment effect aggregates the group–time average treatment effects into an overall effect of participating
in the treatment.

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32
mandatory E-Verify states, the employment rates for unauthorized Mexican immigrant men
worsen, whereas the effects for legal Mexican immigrants and U.S.-born Hispanic men are
positive. Similarly, Amuedo-Dorantes and Bansak (2012) identify lower employment
probabilities among likely unauthorized immigrants in mandatory E-Verify states.
Although examining gender and minority status was not a primary focus for our study,
we conducted additional analyses to explore these intersectional factors. Being a woman and
belonging to a minority group (cf. Hispanics and Asians) reduces the likelihood of becoming an
entrepreneur. However, education and entrepreneurial cultures mitigate these effects in these
demographic groups as well. The intersection of gender and racial/ethnic minority status could
create compounded disadvantages, but such disadvantages are not insurmountable. We noticed
that women are less likely than men to become entrepreneurs (Annex 3), but higher education
and cultural scores also increased their likelihood of becoming entrepreneurs. These
supplementary findings related to intersectional identity dynamics provide important contextual
information for continued research.
We also conducted a multinomial logistic regression to explore the effect of the
intersectional factors on immigrants’ decision to be wage workers, unincorporated entrepreneurs,
or incorporated entrepreneurs.23 In so doing, we seek to compare necessity- versus opportunity-
driven forces that nudge immigrants to pursue entrepreneurship. The results summarized in
Annex 4 reveal that higher education has a positive but statistically nonsignificant effect (coef =
.105, p = .327) on the likelihood of engaging in unincorporated entrepreneurship, compared with
wage work. Education alone thus does not provide a strong differentiator in terms of identifying
immigrants who will choose an unincorporated new venture over wage employment. However,
23 We thank an anonymous reviewer for suggesting this analysis.

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33
college education has a strong, positive, highly significant effect on the likelihood of
incorporated entrepreneurship, with a coefficient approximately 12 times greater than that for
unincorporated businesses. This stark contrast supports the distinction between necessity
entrepreneurship (often unincorporated) and opportunity entrepreneurship (typically
incorporated).
The entrepreneurial cultural index also has a positive, significant effect on both
unincorporated and incorporated self-employment. The effect is stronger for incorporated
businesses. Immigrants from countries with stronger entrepreneurial cultures appear more likely
to pursue formal business structures in support of our theory that entrepreneurial mindmaps from
the home country continue to influence entrepreneurial behaviors in the host country.
Finally, E-Verify has a positive effect on unincorporated entrepreneurship, whereas its
effect on incorporated businesses is non-significant. Restrictive labor market policies seem to
push immigrants toward necessity entrepreneurship (unincorporated) but potentially discourage
more formal business ventures. Higher education also appears to moderate this effect of E-Verify
on unincorporated entrepreneurship, such that we find a significant interaction between higher
education and entrepreneurial culture that increases the likelihood of both types of venturing.
Overall, incorporated entrepreneurship is more strongly associated with higher education and
family income, implying opportunity entrepreneurship, whereas unincorporated entrepreneurship
reflects more necessity-driven motivations.
5. Discussion
Immigration in general and immigrant entrepreneurship in particular have garnered
substantial attraction from academics, policymakers, and popular media. In addition to the
notable statistics we provided in the introduction section of this article, we note that immigrants

Immigrant entrepreneurship
34
are not just more likely to be entrepreneurs but also are vastly over-represented among Nobel
laureates, patent filers, high-tech startup founders, and venture capital-backed firms (Chodavadia
et al., 2024; Fairlie and Lofstrom, 2015). Their outsized representation and influence suggest a
pressing need to identify determinants of immigrants’ inclination to start new ventures. To
address such theoretically and practically relevant questions, we seek to establish institutionally
embedded, intersectional effects of immigrant entrepreneurs’ human capital and entrepreneurial
mindmaps in the context created by their host country’s subnational institutional environment.
With a dynamic centering approach (Collins, 2008), we select categories that are relevant to
immigrants’ new venturing efforts. In turn, we use education as a proxy for identity and
resourcefulness, home country entrepreneurial culture to denote comfort with self-reliance, and
E-Verify mandates to represent institutional power systems in place. We also consider the effects
of gender and ethnicity out of recognition of their importance to intersectionality theory.
This study thus makes three key contributions. First, the crux of the intersectionality
theory is that “human experience is jointly shaped by multiple social positions (e.g., race,
gender), and cannot be adequately understood by considering social positions independently”
(Bauer et al., 2021, p. 1). Therefore, studies examining intersectional contexts traditionally focus
on micro-level factors, such as race, gender, and ethnicity (Thatcher et al., 2023). We take a
distinct approach and instead blend micro-factors with more macro-level factors to understand
the intersectional effect of home and host country institutional environments in which immigrant
entrepreneurship occurs. This novel perspective reveals that immigrants’ education and cultural
mindmaps, embedded within the state-level institutional environments, intersect in a way that
bears critically on their entrepreneurial activities. Specifically, an entrepreneurial culture in the
immigrants’ countries of birth leaves a lasting, persistent impression long after they emigrate to

Immigrant entrepreneurship
35
the United States. Their human capital also provides one of the most important predictors of new
venture creation, which offers a variation on intersectional perspectives that tend to focus on
“how different inequality regimes overlap to create distinct experiences for members of social
groups” (Lewis et al., 2024, p. 13). By addressing intersectional factors that represent advantages
and lead to more positive outcomes (new venture creation), the current study can serve as a
crucial inflection point for intersectionality literature and augment research that defines
“foreignness” (Fuad et al., 2024) or other contextual factors (Stokes, 2015; Tlaiss and McAdam,
2021) as advantages for entrepreneurs.
Second, our work accounts for and addresses some recent criticisms of intersectionality
theory for overemphasizing inborn characteristics such as race and gender (Thatcher et al.,
2023). Intersectionality theory should be broadened to encompass dynamic contextual factors
that shape people’s lived experiences (Anthias, 2013; Lassalle and Shaw, 2021; Sinkovics and
Reuber, 2021). We adopt such an approach and incorporate factors that people can control, at
least to some degree. It also recognizes that the “globalizing era provides opportunities for
people to develop and enact new identities that are no longer necessarily tied to traditionally
defined ethnolinguistic, national, or cultural identities” (2011, p. 19). As Leitch and Harrison
(2016) suggest, we avoid defining identity as an entity and instead acknowledge it as work, such
that we show how immigrants can craft and recraft their entrepreneurial identities. In this sense,
identity is not an objective phenomenon but a fluid consequence of social interactions. People
need not remain confined within the constraints of their innate identities. Rather, they can shape
and reshape their identities, potentially with the assistance of relevant policies, to achieve desired
positive outcomes. Policymakers thus might adopt a similar view in their efforts to identify and

Immigrant entrepreneurship
36
implement facilitators of new venture creation (Barnard et al., 2019; Brzozowski et al., 2014;
Kalnins and Chung, 2006; Robinson and Fernhaber, 2024).
Third, for immigrant entrepreneurship research, we document persistent impacts of
cultural mindmaps among immigrant entrepreneurs. Their willingness to assume risk and engage
in entrepreneurial tasks depends on both their formal education and the informal mindmaps they
have developed on the basis of the deep-rooted cultures, customs, and traditions transmitted in
their birth country. This study offers evidence of a notable intersectional identity: an “educated
immigrant go-getter.” This identity seemingly accounts for the pervasive evidence of widespread
immigrant entrepreneurship in the United States. Concurrently, we note the significance of
formal institutional factors. Again, we hope policymakers use the findings of this study to design
interventions aimed at attracting, retaining, training, mentoring, and supporting immigrant
entrepreneurs.
This study also makes an empirical contribution. Most intersectionality research dealing
with immigrant entrepreneurship relies on qualitative methodologies. Such studies can generate
rich insights (e.g., Mindes and Lewin, 2024); we complement them with robust, quantitative
evidence. With a novel dataset derived from multiple sources, we estimate a probabilistic model
with fixed effects to examine the hypothesized effects on new venture creation across the United
States. The DiD analysis also establishes causality as well as offers evidence pertaining to the
unique considerations linked to incorporated versus unincorporated entrepreneurship. According
to McNamara and Schleicher (2024, p. 1499), any “methodological advancement or refinement
should offer new insights over and above existing methodological approaches, which in turn
provides opportunities for extending the cumulative body of knowledge in the field.” We submit

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37
that our methodology reveals a means to achieve these contributions and also is replicable, with
greater external validity.
6. Limitations and further research
Our study is not free of limitations. First, prior research shows that entrepreneurs with
specialized knowledge and networks (e.g., venture capitalists) are more likely to achieve success
(e.g., Amornsiripanitch et al., 2023; Kolstad and Wiig, 2013). Due to data limitations, we can
only account for social networks of immigrant entrepreneurs that reflect specific birth
nationalities. We cannot dig deeper into specific connections (e.g., with other entrepreneurs).
Second, extant research identifies substantial heterogeneity across immigrant groups (e.g.,
Mindes et al., 2022; Mindes and Lewin, 2024; Portes and Rumbaut, 2024). Such potential
differences (e.g., opportunity versus necessity entrepreneurship) should be explored further
(Ndofor and Priem, 2011; Tao et al., 2021; Vandor and Franke, 2016). Related research could
explore how immigrants use transnational ties, specialized knowledge, or brokerage
opportunities to recognize and exploit opportunities (e.g., Dheer, 2018; Duan et al., 2021; Light
et al., 2017). Another direction for research could be to discern if varying degrees of
discrimination create distinct, intersectional effects for immigrants. Are there some specific
levels of blocked mobility, discrimination, or exclusion at which these effects acquire greater (or
lesser) significance?
Third, culture is generally considered stable, but it is not constant. Societies change over
time, and exposure to new cultures and diverse assimilation and adaption capabilities affect
immigrants’ entrepreneurship capacity (Cho et al., 2013; Martinez Dy, 2020). We control for
some such factors, but research using primary data sources could examine how these factors
influence immigrants’ decision heuristics. Such efforts should recognize and account for ever-

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38
changing institutions and their dynamic impacts on entrepreneurship. Fourth, despite our
extensive theoretical and empirical efforts, we did not examine any potential mediating
mechanisms. Nor did we consider alternative cultural dimensions (Ralston, 2008; Ralston et al.,
2018). We call for research that explicitly examines potential boundary conditions for embedded
intersectional effects (e.g., changes in the socio-economic landscapes, global events like
pandemics), which could influence immigrants and their entrepreneurship.
7. Conclusion
This study provides robust evidence of the institutionally embedded, intersectional impact of
immigrants’ education and entrepreneurial mindmaps on new venture creation in host countries.
In turn, it establishes more nuanced insights into the effect of human capital than has been
depicted in prior entrepreneurial research. The results of this study can inform immigration
policy too such that they show how policymakers might seek to augment the contributions of
immigrant entrepreneurs and enhance the positive spillovers from innovation and business
creation.
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Figure 1. Immigrant Entrepreneurship: Institutionally Embedded Intersectionalities
Sub-national institutional environment (E-Verify)
“Educated entrepreneurial immigrant” intersectionality
Figure 2. Three-way Intersectional effects between Human Capital, Sub-National
Regulations, and Home Country Entrepreneurial Culture
Notes: The horizontal axis measures the
Entrepreneurial cultural index of the immigrants’
home countries. The vertical axis measures the
probability of being an entrepreneur. This graph
uses data for immigrants only. The variable
college plus indicates if the entrepreneur has at
least a college degree. E-Verify indicates if the
state has mandatory E-verify requirement.
College education
(as a resource and an
identity)
Entrepreneurial
mindmaps (Home
country entrepreneurial
culture as a resource
and an identity)

Immigrant entrepreneurship
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Figure 3. Impact of E-Verify on Immigrant Entrepreneurs. Probability of Entrepreneurship Pre- and Post-
Mandator
Graph 3a.
Graph 3b.
Notes: Graphs 3a and 3b represent event studies for immigrant entrepreneurship, for firms of
all sizes and firms with more than 2 employees respectively. The graphs show how average
treatment effects on immigrant firms vary with the length of exposure to the treatment. The
treatment here refers to the state making E-Verify mandatory for all firms.
Figure 4. Impact of E-Verify on Immigrant Entrepreneurs (Unincorporated and Incorporated): Probability
of Entrepreneurship Pre- and Post-Mandatory E-Verify (An Event Analysis)
Graph 4a.
Graph 4b.
Notes: Graphs 4a and 4b represent event studies for immigrant entrepreneurship, for unincorporated and
incorporated firms respectively. The graphs show how average treatment effects on immigrant firms vary with the
length of exposure to the treatment. The treatment here refers to the state making E-Verify mandatory for all
firms.

Immigrant entrepreneurship
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Table 1a: Descriptive statistics
Entrepreneurs
Employed elsewhere
% of total population
7.08
57.67
Natives
Immigrants
Natives
Immigrants
% of Category
92.94
7.06
91.67
8.33
College Plus = 1 (%)
37.28
33.67
33.62
34.02
Age
50.12
46.08
41.72
45.47
Married = 1 (%)
70.22
75.13
53.58
64.41
Family Income (in the last 12
months)
50,000-
59,999
35,000-
39,999
50,000-
59,999
40,000-
49,999
Female = 1 (%)
35.52
34.96
50.22
43.30
Rural = 1 (%)
28.24
6.77
21.15
6.77
Number of Children
<1
>1 & <2
<1
>1 & <2
Notes: The last two columns include all those employed but not entrepreneurs (unemployed excluded).
Table 1b: Correlations’ matrix
Variables
1
2
3
4
5
6
7
8
1
Age
1.000
2
Male
-0.037*
1.000
3
Married
0.255*
0.044*
1.000
4
Log. Family income
-0.039*
0.051*
0.264*
1.000
5
Rural origin
0.059*
0.004*
0.032*
-0.097*
1.000
6
Network
-0.003*
0.002*
0.003*
0.005*
-0.009*
1.000
7
College Plus
0.055*
-0.005*
0.162*
0.248*
-0.102*
0.019*
1.000
8
Entrepreneurial Cultural index
0.044*
-0.007*
-0.058*
0.043*
0.086*
0.054*
0.026*
1.000
9
E-Verify
0.005*
-0.004*
-0.002*
-0.036*
0.017*
0.000
-0.014*
0.014*

49
Table 2. Effects of education, cultural mindmaps, and E-Verify on immigrant
entrepreneurship
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Control variables:
Industry effects
Yes
Yes
Yes
Yes
Yes
Yes
Yes
State effects
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Year
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Black
-0.327***
-0.219***
-0.181***
-0.181***
-0.186***
-0.188***
-0.189***
(0.053)
(0.037)
(0.042)
(0.040)
(0.041)
(0.042)
(0.041)
Hispanic
-0.291***
-0.254***
-0.212***
-0.214***
-0.229***
-0.213***
-0.213***
(0.036)
(0.037)
(0.036)
(0.037)
(0.036)
(0.035)
(0.035)
Asian
-0.128***
-0.141***
-0.171***
-0.172***
-0.188***
-0.189***
-0.189***
(0.021)
(0.023)
(0.023)
(0.023)
(0.020)
(0.020)
(0.020)
Other
-0.040
-0.039
-0.045
-0.042
-0.039
-0.019
-0.020
(0.152)
(0.129)
(0.128)
(0.132)
(0.118)
(0.117)
(0.118)
Age
0.056***
0.055***
0.055***
0.055***
0.055***
0.055***
0.055***
(0.003)
(0.003)
(0.003)
(0.003)
(0.003)
(0.003)
(0.003)
Age squared
-0.000***
-0.000***
-0.000***
-0.000***
-0.000***
-0.000***
-0.000***
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
Male
0.123***
0.120***
0.124***
0.124***
0.124***
0.125***
0.125***
(0.010)
(0.010)
(0.010)
(0.010)
(0.010)
(0.010)
(0.010)
Married
0.090***
0.089***
0.091***
0.090***
0.091***
0.090***
0.090***
(0.011)
(0.011)
(0.011)
(0.011)
(0.011)
(0.011)
(0.011)
Family income (log)
-0.033*
-0.046**
-0.048***
-0.047**
-0.047**
-0.047**
-0.047**
(0.019)
(0.018)
(0.018)
(0.019)
(0.019)
(0.019)
(0.019)
Rural origin
0.155***
0.162***
0.157***
0.148***
0.148***
0.147***
0.146***
(0.037)
(0.036)
(0.037)
(0.033)
(0.033)
(0.033)
(0.034)
Network
0.027***
0.027***
0.027***
0.027***
0.027***
0.027***
0.027***
(0.003)
(0.003)
(0.002)
(0.002)
(0.002)
(0.002)
(0.002)
Main effects:
College plus (CP)
0.147***
0.147***
0.146***
0.150***
0.316***
0.301***
(0.013)
(0.013)
(0.014)
(0.014)
(0.051)
(0.051)
Entrepreneurial cultural index (ECI)
0.005***
0.005***
0.005***
0.007***
0.007***
(0.001)
(0.001)
(0.001)
(0.001)
(0.001)
E-Verify
0.076*
0.087**
0.089**
0.044*
(0.040)
(0.042)
(0.042)
(0.036)
Interactions:
CP x E-Verify
-0.047*
-0.052**
0.176
(0.025)
(0.026)
(0.187)
CP x ECI
0.004***
0.003***
(0.001)
(0.001)
E-Verify * ECI
0.004
(0.004)
CP x E-Verify x ECI
-0.006
(0.005)
Constant
-3.028***
-3.015***
-3.248***
-3.250***
-3.252***
-3.336***
-3.325***
(0.101)
(0.102)
(0.105)
(0.105)
(0.104)
(0.097)
(0.098)
Observations
213,895
213,895
213,895
213,895
213,895
213,895
213,895
Notes: The dependent variable is migrants who are entrepreneurs. Robust standard errors in parentheses
clustered by state of destination *** p<0.01, ** p<0.05, * p<0.10.

50
Table 3. Impact of E-Verify on Immigrant vs Native Entrepreneurs. Average Treatment
Effect by State-Year Groups
Comparison Group: NOT YET Treated (With Controls)
(1)
(2)
Immigrant Entrepreneurs
Immigrant Entrepreneurs (More than 2
employees)
ATT by group
G2007
0.048***
0.049***
(0.007)
(0.007)
G2008
0.036
0.034***
(0.011)
(0.011)
G2010
-0.004
-0.003
(0.003)
(0.003)
G2011
0.000
-0.001
(0.011)
(0.011)
Average TT
ATT
0.015
0.015
(0.012)
(0.012)
Notes: Additional controls include age, age squared, being a male, being married, having a child, rural origin,
business tax rank, race, and networks (log). The control group is the not-yet-treated group. G2007 includes
states that made E-Verify mandatory in 2007 (Arizona), G2008 includes states that made E-Verify mandatory
in 2008 (Mississippi, South Carolina), G2010 includes states that made E-Verify mandatory in 2010 (Utah),
G2011 includes states that made E-Verify mandatory in 2011 (Alabama, Georgia, North Carolina, Tennessee)
Robust standard errors in parentheses clustered by state of destination *** p<0.01, ** p<0.05, * p<0.1.
Table 4. Average Treatment Effect for Immigrants by State-Year Groups
(1)
(2)
Immigrant Entrepreneurs
Unincorporated
Immigrant Entrepreneurs Incorporated
ATT by group
G2007
0.056***
-0.007
(0.004)
(0.006)
G2008
0.005
0.031***
(0.018)
(0.008)
G2010
-0.001
-0.003**
(0.002)
(0.001)
G2011
-0.005
0.006*
(0.009)
(0.003)
Average TT
ATT
0.012
0.004
(0.014)
(0.004)
Notes: Additional controls include age, age squared, being a male, being married, having a child, rural origin,
race, and networks (log). The control group is the not-yet-treated group. G2007 includes states that made E-
Verify mandatory in 2007 (Alabama), G2008 includes states that made E-Verify mandatory in 2008
(Mississippi, South Carolina), G2010 includes states that made E-Verify mandatory in 2010 (Utah), G2011
includes states that made E-Verify mandatory in 2011 (Alabama, Georgia, North Carolina, Tennessee) Robust
standard errors in parentheses clustered by state of destination *** p<0.01, ** p<0.05, * p<0.1.

Immigrant entrepreneurship
51
ONLINE ANNEXES
ANNEX 1: DATA AND MEASURES
1A. Our primary data on immigrants is obtained from CEPR data from 2005 to 2019. CEPR
converts the raw data available through the National Bureau of Economic Research, the Bureau
of Labor Statistics, and the Census Bureau’s CPS data and provides detailed information on
individuals' demographic characteristics, education, and labor-market status. It has information
on immigrant status, a dummy for entrepreneurship, country of birth, and other information
about the respondent. This is our primary dataset; most variables are from the CEPR.
Variable
Source
Notes
Dummy for Entrepreneurship
Current Population
Survey (CPS) from CEPR
Dummy for Immigration
Current Population
Survey (CPS) from CEPR
E-Verify Indicator
Compiled by CATO
institute
Entrepreneurial Index
Constructed using
Schwartz variables from
World Values Survey
See details below
Other individual-level demographic
controls (age, education, race,
gender, state of residence)
Current Population
Survey (CPS) from CEPR
Country of birth
Current Population
Survey (CPS) from CEPR
State Tax Rates
Tax Foundation
Not reported in the main
results as tax rates were
not significant.
Country-level Measures of
governance and quality of
institutions
World Bank’s Doing
Business Report
World Governance Index
Not reported in the main
results as these variables
were not significant.

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52
1B. Everify
• We use E-Verify as a proxy for immigration-specific regulations at the state level. E-
Verify is the US federal system of verifying employment eligibility of newly hired
employees by electronically matching information provided by employees on Form I-9,
against records available to the Social Security Administration (SSA) and the Department
of Homeland Security (DHS). Any discrepancy is communicated to the employer, who
has ten days to verify and correct any anomalies. At the end of the ten days, the employer
must terminate the employee if the discrepancy is unresolved. Thus, mandatory use of E-
Verify is legislation that state governments use to discourage the employment of
undocumented workers. States that mandate the use of E-Verify by employers can
conduct audits. Though most employers voluntarily provide employee papers on E-
Verify, the states that have not mandated it cannot conduct audits, and the states that have
passed legislation mandating E-Verify reporting can enforce punitive actions on non-
compliant employers.
• E-Verify had staggered adoption across the mandating states. A handful of states had
started requiring E-Verify reporting as early as 2007. However, these requirements were
limited to either government contractors or limited to firms of a certain size. By 2012,
many states had made it mandatory for all employers to use E-Verify. The first state to
mandate the use of E-Verify was Arizona in 2007, followed by Mississippi and South
Carolina in 2008, Utah in 2010, Alabama, Georgia, North Carolina, and Tennessee in
2011. None of the states that passed the mandatory E-Verify legislation has reversed it–
once states are treated, they remain treated. The last state to mandate E-Verify is Florida
in 2024, the impact of which can only be assessed in a few years after we have a few
years of observations. We obtain the data on state-level E-Verify legislation from the
Cato Institute that compiles this information from various state policies and cross-verify
with the ones used by prior literature on E-Verify, for instance, Orrenius et al. (2020).
1C. Entrepreneurship Mindmaps (cultural index) from the Schwartz variables from the
World Values Survey.
The World Valueƒ Survey is conducted in waves every six years in various countries but in
different years. The survey is available for different years in different countries. We used the
survey responses to create a cultural index that ranges from 0 to 100. Following were the eight
variables considered for constructing this index:
1) It is important to this person to think up new ideas and be creative.
2) It is important to this person to be rich.
3) It is important to this person living in secure surroundings.
4) It is important to this person being very successful.
5) It is important to this person adventure and taking risks.

Immigrant entrepreneurship
53
6) It is important to this person to always behave properly.
7) It is important to this person tradition.
8) It is important to this person to do something for the good of society.
The scale of these variables ranges from 1 to 6, where one represents “very much like me” to 6,
“not at all like me.” A lower score for five of these variables represents a high propensity for
entrepreneurship. For the other three, a lower score shows a lower propensity for
entrepreneurship. We recoded the latter three variables to make combining them with the rest of
the Schwartz variables easier. The variables also have values ranging from -1 to -5 that
correspond to a missing answer – don’t know, no answer etc. We replace negative values with
“.” for missing. The WVS Longitudinal 6 wave aggregate includes WVS 1981-1984, WVS
1990-1994, WVS 1995-1998, WVS 2000-2004, WVS 2005-2009 data, and WVS 2010-2014.
The EVS waves include EVS 1981-1984, EVS 1991-1994, EVS 1999 and EVS 2008.
We drop years and countries with missing data. There are countries for which we do not have
recent surveys. For instance, the survey for Albania was last done in 1999. We have kept only
the most recent survey. Some countries have results from two surveys during the 2005–2019
timespan that we study. For some countries, the only year the survey is available is post-2012.
For instance, the only year the survey is available for Algeria is 2014. Since culture and values
remain unchanged for decades, we impute the same number for Algeria for the rest of the years.
Responses are missing for some countries in some years. Missing responses vary between 0.2
percent to 8 percent.
For the factor analysis, we use the same factor loadings across time. If the studied variables are
latent variables measured by multiple indicators, a comparison across groups or time requires
measurement invariance to ensure that the concept remains comparable across groups or time. If
there are changes in societal attitudes and contexts over time, this could impact the stability of
the index. Given the short period of five years of our analyses, we do not expect such changes as
prior research has reported cultural dimensions to be highly stable over time (Beugelsdijk et al.,
2015; Hofstede, 1984; Ridgeway, 1997).
We used the weight provided by individual countries converted to a common N, 1000. It is not
population weighted. The purpose of S017 is to compensate for small deviations in the resulting
sample with respect to one or several dimensions considered important to get reliable results.
These dimensions can be the sex-age distribution, the rural-urban distribution, or even the
respondent’s educational distribution. The weighting method is a decision made by each
participant country. Whatever the criteria chosen, the procedure to compute weighting factors is
similar. Usually, a matrix is defined with the estimated proportion of each combination of
categories that the sample should present. This estimation may come from the Census, country
statistics, etc. This is the target distribution matrix. Then, the actual distribution of each
combination of categories is calculated for the fielded sample. The weight is, by definition, the
matrix of factors that should be multiplied by the fielded sample matrix to get the target
distribution matrix.

Immigrant entrepreneurship
54
We rotate the factors using the varimax option, which is the most commonly used orthogonal
rotation. We apply the Cronbach's test to verify that the variables included in the factor relate to
the latent variable. Cronbach’s alpha test provides the Scale reliability Coefficient, which should
equal or exceed 70 percent. We get a Scale reliability coefficient greater than 70 percent, thus
verifying the index’s reliability. Since the sample size is large and the scores too have many
items associated with them, we accept this reliability score. Schwartz variables are widely used,
and the content and construct validity of the indicators have been established by previous
researchers (e.g., Krueger et al., 2013; Morales et al., 2019; Schwartz and Bilsky, 1987).
Finally, we use min-max normalization to rescale the index to a 0 to 100 scale. A min-max
normalization is highly influenced by the maximum and minimum values. Therefore, it is best
used when the distribution of the measure is normal. Since the measure of culture generated is
normally distributed, we use the min-max normalization.

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55
Annex 2. Impact of E-Verify. Average Treatment Effect for Immigrants Employed
Elsewhere by State-Year Groups.
Comparison Group: NOT YET Treated (With Controls)
(1)
Immigrants Employed Elsewhere
ATT by group
G2007
-0.153***
(0.012)
G2008
-0.021*
(0.011)
G2010
0.032***
(0.004)
G2011
0.022
(0.016)
Average TT
ATT
-0.028
(0.039)
Notes: Additional controls include age, age squared, being a male, being married, having a child, rural origin, business tax
rank, race, and networks (log). The control group is the not yet treated group. G2007 includes states that made E-Verify
mandatory in 2007 (Arizona), G2008 includes states that made E-Verify mandatory in 2008 (Mississippi, South Carolina),
G2010 includes states that made E-Verify mandatory in 2010 (Utah), G2011 includes states that made E-Verify
mandatory in 2011 (Alabama, Georgia, North Carolina, Tennessee) Robust standard errors in parentheses clustered by
state of destination *** p<0.01, ** p<0.05, * p<0.1.
Notes: The graph represents the event study for immigrants, who are employed but not entrepreneurs. The event
study includes firms of all sizes. The graph shows how the average treatment effect on immigrant firms varies with
the length of exposure to the treatment. The treatment here refers to the state making E-Verify mandatory for all
firms.

Immigrant entrepreneurship
56
Annex 3. Intersectional effects with the inclusion of gender

Immigrant entrepreneurship
57
Annex 4. Effects of education and home and host institutions on unincorporated and
incorporated immigrant entrepreneurship. Multinomial logistic regression
Unincorporated
Incorporated
Control variables:
Industry effects
Yes
Yes
State effects
Yes
Yes
Year
Yes
Yes
Black
-0.338***
-0.299**
(0.111)
(0.121)
Hispanic
-0.188***
-0.803***
(0.063)
(0.092)
Asian
-0.386***
-0.256***
(0.034)
(0.075)
Other
0.079
0.256
(0.299)
(0.355)
Age
0.091***
0.159***
(0.006)
(0.010)
Age squared
-0.001***
-0.001***
(0.000)
(0.000)
Male
0.104***
0.492***
(0.031)
(0.029)
Married
0.099***
0.353***
(0.026)
(0.039)
Family income (log)
-0.258**
0.727**
(0.026)
(0.060)
Rural origin
0.309***
0.232***
(0.076)
(0.104)
Network
0.042***
0.058***
(0.004)
(0.006)
Main effects:
College plus (CP)
0.105
0.322***
(0.107)
(0.041)
Entrepreneurial cultural index (ECI)
0.011***
0.015***
(0.002)
(0.002)
E-Verify
0.136***
-0.134
(0.032)
(0.243)
Interactions:
CP x E-Verify
0.059**
-0.010
(0.031)
(0.522)
CP x ECI
0.001***
0.000***
(0.000)
(0.001)
E-Verify * ECI
0.002***
0.011
(0.000)
(0.007)
CP x E-Verify x ECI
0.015*
-0.004
(0.008)
(0.013)
Constant
-5.564***
-11.173***
(0.230)
(0.489)
Observations
213,895
213,895
Notes: The population comprises only immigrants. The base category consists of respondents working for a wage.
Robust standard errors in parentheses clustered by state of destination *** p<0.01, ** p<0.05, * p<0.10.