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In this detailed information relating to entrepreneurship and immigration evidence from gem luxembourg.
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81
Economie et Statistiques
Working papers du STATEC
mai 2015
Auteurs: Chiara PERONI,
Cesare RIILLO, Francesco
SARRACINO
Entrepreneurship and immigra-
tion: evidence from GEM Luxem-
bourg
Abstract
This study analyses the role of immigration background and education in creating new
business initiatives in Luxembourg, a country where 44% of the resident population is
immigrant. We investigate the features of entrepreneurs and of the Luxembourgish System
of Entrepreneurship using the Global Entrepreneurship Monitoring surveys of 2013 and
2014. We study the effect of immigration through all the stages of entrepreneurial process:
interest in starting a new business, effectively starting, running a new business and man-
aging an established business. We adopt a sequential logit to model entrepreneurial pro-
cess as a sequence of stages. We find that first generation immigrants are more interested
in starting a new business than non-immigrants, but they do not differ in subsequent en-
trepreneurial phases. This is relevant among highly educated people. We argue that poli-
cies to attract highly educated immigrants can promote entrepreneurial initiatives in Lux-
embourg.
Key-words: entrepreneurship; immigration; education; sequential logit; GEM; Luxembourg.
Entrepreneurship and immigration: evidence
from GEM Luxembourg
?
Chiara Peroni
†
, Cesare Riillo
‡
, and Francesco Sarracino
§
April 2, 2015
Abstract
This study analyses the role of immigration background and education
in creating new business initiatives in Luxembourg, a country where 44%
of the resident population is immigrant. We investigate the features of en-
trepreneurs and of the Luxembourgish System of Entrepreneurship using
the Global Entrepreneurship Monitoring surveys of 2013 and 2014. We
study the e?ect of immigration through all the stages of entrepreneurial
process: interest in starting a new business, e?ectively starting, running
a new business and managing an established business. We adopt a se-
quential logit to model entrepreneurial process as a sequence of stages.
We ?nd that ?rst generation immigrants are more interested in starting a
new business than non-immigrants, but they do not di?er in subsequent
entrepreneurial phases. This is relevant among highly educated people.
We argue that policies to attract highly educated immigrants can promote
entrepreneurial initiatives in Luxembourg.
Key-words: entrepreneurship; immigration; education; sequential logit;
GEM; Luxembourg.
?
The opinions and views expressed in this paper are those of the authors and do not re?ect
in any way those of STATEC.
†
Institut national de la statistique et des ´etudes ´economiques du Grand-Duch´e du Luxem-
bourg (STATEC).
‡
Institut national de la statistique et des ´etudes ´economiques du Grand-Duch´e du Luxem-
bourg (STATEC).
§
Institut national de la statistique et des ´etudes ´economiques du Grand-Duch´e du Luxem-
bourg (STATEC); Laboratory for Comparative Social Research (LCSR), National Research
University Higher School of Economics (Russia).
1
1 Introduction
Entrepreneurship, broadly de?ned as “the process whereby individuals create
new ?rms”(Reynolds et al., 2000), is regarded as an important contributor to
innovation and technological progress, a driver of productivity and ultimately
of economic growth (Schumpeter, 1934; Audretsch, 2007; Braunerhjelm et al.,
2010; Wennekers and Thurik, 1999). Moreover, successful entrepreneurs favour
knowledge spillovers, and create new jobs. The IT boom of the 90s, and in par-
ticular the emergence of highly innovative, fast expanding and highly pro?table
IT ?rms, has largely contributed to revive the attention of policy makers and
academics on entrepreneurship. Governments have become increasingly active
in designing policies to foster the entrepreneurial e?orts. In parallel, data collec-
tions projects have been launched to assess the largely anedoctical evidence on
the link between entrepreneurship and growth and to provide support to policy
actions. One of such initiatives, GEM aims to collect internationally compa-
rable data to deepen the understanding of entrepreneurial activities and their
link with countries’ economic performances. The project rests on a conceptual
framework that seeks to explain variations in countries’ growth rates studying
the entrepreneurial process (Reynolds et al., 2005). To do so, GEM models en-
trepreneurship as a process rather than a “single phase” decision. The process
comprises several phases: interest in starting a new business, intention to start,
e?ectively starting, survival of the new ?rm (for a description of the various
phases, see Amoros and Bosma, 2014).
GEM data are collected through surveys on individuals conducted at coun-
try level. These data have been used mainly to study individual determinants
of entrepreneurial involvement as well as links between entrepreneurship and
economic growth (for a survey of the literature using GEM data, one can see
Alvarez et al., 2014). Recent GEM waves have also focused on special topics
such as the role of job satisfaction and well-being on entrepreneurial e?orts, as
well as the entrepreneurial attitudes of migrants (Xavier et al., 2013; Amoros
and Bosma, 2014).
This study investigates the role of the immigration background in entrepreneurial
activities using GEM data for Luxembourg. Descriptive GEM-based evidence
shows that in Luxembourg the chance of becoming an entrepreneur is much
higher for migrants than for nationals (Fletcher et al., 2014). Here, we further
investigate this issue and extend this analysis as follows. We analyse the interac-
tions of individual aspects — skills, education, previous experiences, attitudes,
income, networks — with the immigrant status, from the propensity to start
a business to running an established one, using a sequential logit model (Tutz,
1991). This method allows us to model entrepreneurship as a sequential process,
thus better re?ecting the GEM framework, and to study the di?erent barriers
that immigrants might face at di?erent stages of the entrepreneurial process.
This paper is structured as follows. Section 2 links the study to the existing
empirical and theoretical literature, and gives background information on Lux-
embourg. Section 3 describes the data used in this analysis, Section 3 describes
the method used to obtain empirical results presented in Section 5, while Section
6 gives concluding remarks and policy implications.
2
2 Background
Population movements and entrepreneurship are regarded as drivers of economic
growth, but so far have been mainly analysed separately. Economists have re-
cently turned to investigate the economic contribution of immigrants (Wennek-
ers and Thurik, 1999; Hunt and Gauthier-Loiselle, 2010; Peri, 2012; Kerr et al.,
2013), suggesting a positive impact of migrants on innovation activities and pro-
ductivity. At the aggregate level, Peri (2012) ?nds that immigration increases
total factor productivity, but negatively a?ects the skill-bias of the labour force.
Kerr et al. (2013) analyse the impact of immigration at ?rm-level using matched
employees-employers data, and ?nd that skilled immigration expands skilled em-
ployment and ?rms innovation rates. Hunt and Gauthier-Loiselle (2010) show
that skilled immigrants have improved innovation performance in the US over
the period 1990-2000. These authors focus on direct involvement of immigrants
in research and development activities, and measure innovation by patents per
capita; interestingly, they note that the presence of immigrants may be linked
to innovation through the provision of management and entrepreneurship skills
(this is referred to as the immigrants’ indirect contribution to innovation).
Empirical evidence on the link between immigration and entrepreneurship is
scarce, possibly due to di?culties in observing immigrants’ contribution to en-
trepreneurial activities. Nonetheless, anedoctical evidence suggests a strong con-
tribution of immigrants to entrepreneurship (Wadhwa, 2011; Hohn et al., 2012).
Basic statistics reported by Xavier et al. (2013) and OECD (2010) show that mi-
grants are more likely to engage in entrepreneurial activities than non-migrants.
Among the few studies exploring the link immigration-entrepreneurship, Con-
stant and Zimmermann (2006) study the impact of ethnicity and immigration
status on self-employment decisions using the 2000 wave of the German Socio-
Economic Panel (SOEP). They show that the percentage of self-employed work-
ers is low in Germany and more so among non-natives, despite immigrants
self-employed earn a lot more than their salary workers counterparts. Overall,
?gures suggest that nationals and immigrants in Germany become entrepreneurs
largely for the same reasons. Using GEM data collected for Spain, Irastorza and
Pena (2007) ?nd that immigrants are more likely to become entrepreneurs than
natives. Batista and Umblijs (2014) analyse the relationship between risk pref-
erences and migrant entrepreneurship using data from a survey on immigrants
in the greater Dublin area; they ?nd that willingness to take risks, experience
and being part of migrants enclaves are signi?cant predictors of entrepreneur-
ship among immigrants.
This study expands this literature analyzing the role of immigrants in creat-
ing new business initiatives in Luxembourg. Before discussing the Luxembourg
case, the following gives a brief account of the theories explaining why migrants
play a speci?c role in the entrepreneurial e?ort.
2.1 Immigrants and entrepreneurship
The theories seeking to explain the relationship between immigration and en-
trepreneurial involvement can be categorised in two broad groups: the ?rst
group relies on speci?c features of immigrants to explain di?erences in the
propensity to start a business compared to non-immigrants; the second group
focuses on the institutional and cultural environment of the host country.
3
According to the theories in the ?rst group, immigrants have higher chances
to start a new business because various kind of disadvantages (linguistic, racial,
educational) steer their willingness to become entrepreneurs (Light, 1979; Bor-
jas, 1986; Coate and Tennyson, 1992; Clark and Drinkwater, 2000; Parker, 2004;
Fregetto, 2004). Some scholars argue that immigrants opt for self-employment
to avoid low paid jobs — or those jobs perceived as preventing their upward
mobility (Paulson and Townsend, 2005; Rissman, 2006). Other researchers em-
phasise the role of cultural traits. The main idea is that immigrants “inherit”
the cultural traits of their countries of origin; whenever these traits determine
a preference for self-employment, they result into higher chances of engaging
in entrepreneurship (Masurel et al., 2004; Hofstede, 2007; Chrysostome, 2010).
Some scholars have extended the latter model to account for the role of social
networks linked to the country of origin (this is sometime referred to as the
human capital theory). It is argued that such networks provide migrants with
easy access to the resources — labor, capital, information and family support
— needed to start a business (Sanders and Nee, 1996; Peters, 2002; Basu and
Altinay, 2002).
Linked to the human capital theory, the middleman minority and the ethnic
enclave theories (Nestorowicz, 2012) also belong to the ?rst group of theories.
The former, developed at the beginning of the 1970s, rests on the observation
that successful migrant-led business initiatives are more commonly observed
in areas with relatively large shares of immigrants. The features of migrants’
business activities – such as agents, money lenders, rent collectors, and brokers
– favoured the view of immigrants as “middlemen”, i.e. intermediaries between
market actors (Nestorowicz, 2012). This model sees the immigrant and the host
community in a symbiotic equilibrium between con?ict and dependence due
to economic success (Aldrich and Waldinger, 1990; Terjesen and Elam, 2009;
Nestorowicz, 2012).
The ethnic enclave theory focuses on the existence of immigrant enclaves in
the host society.
1
The main idea is that immigrants have increased opportunities
to start new businesses in areas where existing activities are run by individuals
belonging to the same ethnic group (Altinay, 2008).
2
The theory posits that
enclaves bene?t the entrepreneurial initiative due to the high intra-group sol-
idarity, shared values, norms and attitudes that facilitate economic activities
(Auster and Aldrich, 1984; Zhou and Logan, 1989). This stream of research has
also investigated the conditions favouring the settling of enclaves, and whether
the existence of an enclave is socially desirable. Two conditions have been iden-
ti?ed for the emergence of economic enclaves: i. access to su?cient start-up
capital, usually through immigrants’ networks and connections with the coun-
try of origin; ii. a steady arrival of new labour force within the enclave (Portes
and Jensen, 1989; Portes and Shafer, 2007). Enclaves, however, are perceived as
“separated” from the resident population, a condition that may favour feelings
of hostility, discrimination, and ultimately con?ict between the immigrant and
the non-immigrant population. This theory has received considerable attention
as, with some extensions and re?nements, it proved to be able to explain some
observed patterns (Sanders and Nee, 1987; Waldinger, 1993; Light et al., 1994).
1
Ethnic enclaves can be de?ned as self-contained minority communities nested in metropoli-
tan areas (Wilson and Martin, 1982)
2
In a well-known study, Wilson and Portes (1980) found that Cuban immigrants working
for Cuban employers in Miami experienced signi?cant returns to their human capital.
4
The second group of theories explains migrants involvement in entrepreneur-
ship focusing on the interaction between migrants’ individual features and the
institutions and characteristics of the hosting societies and markets. Waldinger
et al. (1990) proposed the so-called “interactive model” according to which im-
migrants’ entrepreneurial involvement is the outcome of the interaction between
immigrants’ own resources and societies’ opportunity structures. The latter are
historically-shaped circumstances, such as market conditions that do not require
mass production or distribution, characterised by decreasing return to scale in
which ethnic goods are in demand. These conditions allow the mobilization
of immigrants’ characteristics – named as ethnic strategies – towards the en-
trepreneurial initiative (P¨ utz, 2003; Volery, 2007). More recently, Kloosterman
and Rath (2001) re?ned the interactive model to account for country-speci?c
institutional frameworks. These authors developed the “mixed embeddedness”
model suggesting that while immigrants belong to ethnic networks, they are
also embedded (entrenched) in speci?c market conditions, socio-economic and
politico-institutional environments. The interactive and the mixed embedded-
ness model have received considerable attention in the literature, and have been
extended to account for gender di?erences, the role of family business, of subur-
ban ethnic clusters, of cultural characteristics, and to account for the evolution
of institutions and market conditions (Light and Rosenstein, 1995; Bonacich,
1993; Rath, 2002; P¨ utz, 2003; Portes and Rumbaut, 2006; Li, 1998; Klooster-
man, 2010). Finally, these studies have contributed to identify a set of control
variables – such as managerial and other individual abilities, family background,
occupational status, ?nancial constraints, and economic activity – for studying
determinants of self-employment (Aliaga-Isla and Rialp, 2013).
5
Figure 1: Total early-stage entrepreneurial activity (TEA) 2013.
Percentage of adults engaged in entrepreneurial activities on active population
(18-64 years of age). Note: adapted from GEM Global Report 2013.
2.2 The focus on Luxembourg
Luxembourg’s demographic structure makes it an interesting case for the study
of national systems of innovation and entrepreneurship. Since 1990, the resident
population has increased by more than one third from immigration.
3
At the
same time, increased demand and supply of labour have driven the expansion
of domestic employment.
4
In this context, Jean et al. (2007) and Barone (2009)
document that the country has been successful in implementing policies for
promoting skilled immigration.
In Luxembourg, the share of immigrants in the resident population is higher
than in any other European country (see ?gure 2). As of the ?rst of January
2013 about 45% of the Luxembourgish resident population is constituted of im-
migrants coming from more than 100 di?erent countries (STATEC, 2012). Thus,
Luxembourg o?ers a unique combination in terms of high share of immigrants
and of high diversity among ethnic groups. In addition, the country anticipates
some tendencies that are expected to a?ect other European countries in the
coming years (see ?gure 3). Population projections by EUROSTAT show that
by 2061 a majority of EU countries are expected to signi?cantly increase the
share of non-nationals on their resident population (Lanzieri, 2011).
5
Accord-
3
Luxembourg en chi?res, STATEC, 2014 can be found on:http://www.statistiques.public.lu/en/publications/series/lux-figures/index.html
4
On labour force statistics in Luxembourg one can see
data and publications on STATEC’s website, in particularhttp://www.statistiques.public.lu/en/population-employment/index.html. One can
also see the various issues of the Rapport travail et coh´esion sociale, published regularly by
STATEC.
5
In six other European countries – namely, Cyprus, Austria, Germany, Great Britain,
Ireland, and Belgium – people with an immigration background will account for more than
6
Figure 2: Share of non-nationals in the resident population, 1 January 2013.
Source: authors’ own elaboration, Eurostat data.
ing to such predictions, the challenges that Luxembourg faces in 2013 - 2014
will become relevant also to other countries. Hence, lessons on the relationship
between immigration and determinants of entrepreneurial activity drawn from
Luxembourg are also of more general relevance.
These facts suggest that the Luxembourg case may contribute to a better
understanding of the role of migration on innovation activities and entrepreneur-
ship. According to GEM, Luxembourg is the seventh country with the highest
share of people involved in TEA after United States, Canada, Singapore, Israel,
Netherlands, and Ireland. (This is shown in ?gure 1, which depicts population’s
involvement in early-stage entrepreneurial activities.) Moreover, existing evi-
dence suggests that immigrants play a special part in entrepreneurship: Ries
(2006) reports that foreigners account for the 75% of entrepreneurs in Luxem-
bourg.
In the light of the evidence from previous studies and of the features of the
Luxembourgish socio-demographic composition, we test the following hypothe-
sis:
1. immigrants have higher chances than nationals to be willing to engage in
entrepreneurial process;
2. condition upon the willingness to engage in entrepreneurial process, the
chances that an immigrant will start a new company are not signi?cantly
di?erent from those for nationals;
3. higher educated immigrants have higher chances to start a new business
in Luxembourg.
3 Data
We use data from the Adult Population Surveys of Global Entrepreneurship
Monitor (GEM) survey. GEM is a rich, internationally harmonized source of
30% of the resident population.
7
Figure 3: Share of foreign background persons in the EU Member States in 2011
and projected in 2061.
Source: Lanzieri (2011).
individual-level information about people’s motives and aspirations towards en-
trepreneurship. This survey is currently administered in more than 100 countries
world-wide, covering more than 75% of the world population.
In 2013 and 2014 the National Statistical O?ce of Luxembourg (STATEC),
together with the University of Luxembourg and the Centre de Recherche Publique
“Henri Tudor” administered the ?rst two waves of the GEM survey in Luxem-
bourg. In both waves, a nationally representative sample of about 2000 people
replied to a questionnaire about entrepreneurial activity, aspirations and atti-
tudes. The aim of the survey was to collect information about the attitudes and
behaviours leading to the creation of entrepreneurial activities, along with a set
of socio-demographic and economic variables.
Data have been collected on a single sample of the population with an age
comprised between 18 and 64 years. Approximately half of the sample has
being interviewed using ?xed line telephone, and the remaining half ?lled-in
an on-line survey. In the latter case, individuals have been randomly selected
from a data-base with over 14,000 e-mail addresses. These methods do not cast
particular doubts about the selection of the sample as virtually every household
in Luxembourg has a land-line and more than 92% of the population has internet
access.
We pool the waves of 2013 and 2014 to retrieve individual level information
about immigration status, entrepreneurship activities, entrepreneurial attitudes,
gender, age, education of the respondents and sector of economic activity of the
new business. Pooling the waves increases the sample size and allows more
precise estimations.
8
3.1 Dependent variables
Our empirical strategy follows the GEM model. This model describes the en-
trepreneurship process as composed by the following sequence of stages:
1. Inactive;
2. Potential (expecting to start a new business within the next three years);
3. Nascent entrepreneur (involved in setting up a business);
4. New entrepreneur (owner-manager of ?rm younger than 42 months that
pays wages during last three months);
5. Established entrepreneur (owner-manager of ?rm older than 42 months
that pays wages during last three months).
Individuals who wish to establish a ?rm cross the various stages. Crossing
stages depends on subjective and institutional factors that allow an individ-
ual to become a potential entrepreneur, to decide to start a ?rm, to set it up
and to lead an established company. The various phases are observed via re-
spondents’ self-declarations of involvement in entrepreneurial activity. In other
words, respondents are asked to situate their company in a speci?c phase of the
entrepreneurial process. Based on these answers, we built a set of four dummy
variables, one for each phase of the process. These variables take value 1 if the
respondent is in a speci?c or higher phase and zero otherwise. This is illustrated
in ?gure 4.
3.2 Variables of interest
The main independent variable is the migratory background of the respondents.
We distinguish the respondents in nationals (individuals born in Luxembourg
with both Luxembourgian parents), ?rst generation immigrants (individuals
born abroad) and second generation immigrants (individuals born in Luxem-
bourg with at least one foreign parent).
The distinction in ?rst and second generation immigrant is relevant because
the attitudes, behaviors and motives of immigrant entrepreneurs may di?er sig-
ni?cantly between ?rst and second generation immigrants. For example, it is
plausible that the second generation of immigrants reports more similar features
to the nationals than to the ?rst generation. This might be due to the fact that
the second generation is born and grows up in Luxembourg, and therefore it
gets educated and socialized as nationals (Callens et al., 2014). However, there
are also reasons to believe that the second generation is not di?erent from the
?rst one. This argument is based on the recent work by Algan and Cahuc (2010)
showing that trust in others is an individual trait partly inherited by parents,
thus depending on trust prevalent in the country of origin. Since trust in others
is an important factor shaping people’s attitudes and intentions to invest in
an economic activity, it is plausible to expect that eventual di?erences between
?rst generation immigrants and nationals are also mirrored in the second gen-
eration. Descriptive statistics in table 1 show that ?rst generation migrants are
more active in entrepreneurial activities than nationals and second-generation
migrants over all stages of the entrepreneurial process.
9
Table 1: Entrepreneurship activities by immigration background
Non-immigrants First Second Total
generation generation
Inactive 0.840 0.734 0.805 0.803
Potential entrepreneur or more 0.160 0.266 0.195 0.197
Nascent entrepreneur or more 0.0894 0.132 0.102 0.104
New entrepreneur or more 0.0483 0.0669 0.0548 0.0548
Established entrepreneur 0.0303 0.0387 0.0244 0.0314
Percentage of population engaged in entrepreneurial activities by immigration back-
ground on totals.
3.3 Control variables
The entrepreneurial attitude is measured by three dummy variables. Each vari-
able takes value 1 if the respondent:
1. knows someone who started a business;
2. perceives himself as skilled and experienced enough to start a new business;
3. fears to fail in starting a new business.
The attitude towards starting a new business are particularly relevant only
in ?rst phases of entrepreneurship process (up to e?ectively starting a new
business) and are not implemented when investigating later phases. It is worth
noticing that the fear of failure allows to control for individual risk aversion. This
is particularly important to address the self-selection concern due to the fact
that more risk prone individuals can also be more likely to become immigrants
and to start new businesses.
To account for individual socio-economic conditions, we control for age, gen-
der, education, occupation and income of the respondent. Age is measured as
a continuous variable ranging from 18 to 64 years. Gender is a dummy vari-
able set to 1 if the respondent is male and 0 otherwise. Education is observed
by a set of dummy variables respectively set to 1 if the respondent declares to
have one of the following levels of education classi?ed in line with the Inter-
national Standard Classi?cation of Education. Retained education categories
are a) lower secondary; b) upper secondary and craftsman; c) tertiary (e.g.
bachelor and higher). Employment status, implemented only in the ?rst two
phases of entrepreneurial process, is measured with a categorical variable that
10
takes the following values: a)full-time, b) part-time, c) Self-employed d) Seeking
employment e) Others (e.g. students retired etc..). The availability of private
?nancial resources to fund the business is observed through respondent’s self-
declaration of belonging to one of the following income classes: 0-40,000; 40,001-
60,000; 60,001-80,000; 80,001-100,000; more than 100,000. In later phases of
entrepreneurial process, individual’s income can be seen as a measure of the
pro?tability of the business.
The sectors of economic activities are observed according to the International
Standard Industrial Classi?cation (ISIC). Sectors are aggregated on the basis of
knowledge intensity as de?ned by (EUROSTAT, 2008). Retained categories are:
knowledge intensive services, Low knowledge intensive services and others (e.g.
agriculture, manufacturing). Finally, to account for time e?ect, we include a
dummy variable for each year when the survey was conducted (2013 and 2014).
All variables are interacted with the immigration variable to capture the possible
di?erent in?uence on the probability to become an entrepreneur for people with
di?erent migratory backgrounds. Descriptive statistics are reported in table 4
in the annex.
4 Methodology
This section presents the empirical strategy used in this analysis. As noted in
previous sections, the GEM framework models entrepreneurship as a process
comprising several stages. These include the intention to start a new business,
the involvement in new ventures, and the survival of new ?rms. Thus, each
entrepreneur passes through intermediate steps before setting up an established
business; at each stage, the entrepreneur can stop or proceed to the next phase.
Figure 4 gives a graphical representation of the entrepreneurship model. To
account for the GEM setting, we adopt a variant of the sequential model of
Tutz (1991) proposed by Buis (2010).
Figure 4: Sequential entrepreneurial model.
No active
Potential
Stop
Nascent
Stop
New
Stop
Established
Stop
Here, the idea is that only part of the population is potentially interested to
start a new business, and among them only a fraction will e?ectively start a
new business. This framework allows us to establish whether the probability
11
to successfully proceed over subsequent stages di?er over immigration status
(nationals; ?rst generation; second generation). The probabilities p that an
individual proceeds through the various stages are as follows:
p
1i
=
e
(?
1
+?
1
Imm.+?
1
X
i1
)
1 + e
(?
1
+?
1
Imm.+?
1
X
i1
)
(1)
p
2i
=
e
(?
2
+?
2
Imm.+?
2
X
i2
)
1 + e
(?
2
+?
2
Imm.+?
1
X
i2
)
if phase
1i
= 1 (2)
p
3i
=
e
(?
3
+?
3
Imm.+?
3
X
i3
)
1 + e
(?
3
+?
3
Imm.+?
1
X
i3
)
if phase
2i
=1 (3)
p
4i
=
e
(?
4
+?
4
Imm.+?
4
X
i4
)
1 + e
(?
4
+?
4
Imm.+?
1
X
i4
)
if phase
3i
=1 (4)
Where i denotes the individual, and Imm. the immigration background. One
can see that this model is composed by 5 phases, resulting in 4 transitions from
inactive to established enterpreneurs. Entrepreneurs can move to a new phase
only if they have achieved the previous stage (see ?gure 4). The transition-
speci?c intercept is ?
k
, with k = 1, 2, . . . , 4; ?
k
, the coe?cient of the immigration
status, is the coe?cient of interest; X is a vector of control variables.
The model above is estimated by ?tting logistic regressions for each transi-
tion, using the sub-sample constituted by individuals who have achieved that
stage (Tutz, 1991). As factors a?ecting the transition probabilities may vary
over the sequence, we do not restrict the set of control variables to be the same
at each phase.
6
To capture possible di?erences between immigrants and non-
immigrants for various levels of the control variables, we also include interaction
e?ects of the immigration variable Imm. with all control variables.
5 Results
We ?nd that the willingness to engage in entrepreneurial activities is higher
for ?rst generation migrants than for Luxembourgish nationals. At subsequent
stages of the entrepreneurial process, however, the behaviour of migrants and
non-migrants does not di?er signi?cantly. Table 2 reports marginal e?ects of the
migration background on the probabilities of engaging in entrepreneurial activi-
ties. One can see that the probability that a ?rst generation migrants becomes a
potential entrepreneur is 7 percentage points higher than for non-migrants (?rst
column). Among potential entrepreneurs, however, the probability to start a
new business does not di?er signi?cantly over migration backgrounds. Similar
results are found for the subsequent steps of the entrepreneurial process, i.e.
running and successfully establishing a new ?rm.
6
Questionnaire provides information about the sector of economic activity only after the
starting of the new venture. Therefore only the last two phases include these controls.
12
A possible explanation for this result is that individuals that are more willing
to take risks are more likely to migrate. In other words, it is plausible to expect
that our results are due to self-selection of “risk-lover” people among migrants.
To account for this source of endogeneity, we control for the respondents’ fear
of failure. Indeed, the fear of failure may be regarded as a measure of the risk
aversion of the respondents (Batista and Umblijs, 2014).
The average marginal e?ects on the transition probabilities for all variables
in the model are reported in the Annex A.
7
Table 2: Average marginal e?ects at di?erent entrepreneurial steps.
Potential Nascent New Established
First generation 0.0706
???
-0.0517 0.00403 -0.0373
(0.000) (0.307) (0.947) (0.632)
Second generation 0.0265 -0.0799 -0.0158 -0.145
(0.209) (0.205) (0.836) (0.326)
Observations 2022 377 336 183
p-values in parentheses.
Non-immigants is the reference category.
?
p < 0.1,
??
p < 0.05,
???
p < 0.01.
5.1 The role of education
This section focuses on the e?ects of variables describing the educational level
of individuals on entrepreneurial activities. This is relevant to Luxembourg
because of the important share of highly educated immigrants living in the
country. This analysis may help to better understand how human capital af-
fects the relation between immigration background and entrepreneurship. The
idea is that innovative businesses, often concentrated in high-tech and high-
knowledge industries, usually require speci?c skills and highly trained people.
The availability of such skills may be crucial in determining both the probability
to become entrepreneurs as well as the survival of new ventures.
To investigate this aspect, we re-estimate the likelihood of transitioning
across entrepreneurship phases taking into account di?erent educational lev-
els. Results are shown in table 3. Highly educated ?rst generation immigrants
are more likely to become potential entrepreneurs. In particular, ?rst gener-
ation immigrants with tertiary education are more likely to be potential en-
trepreneurs than non-immigrants with tertiary education (about 14 percentage
points), while we do not ?nd any statistical di?erence across educational levels
and migration status in successive steps.
Second generation immigrants with lower secondary education are less likely
to become nascent entrepreneurs compared to non-immigrants with comparable
educational level. However, second generation immigrants with upper secondary
and craftsman education are more likely to involve in start-ups.
Summarizing, highly educated immigrants are more likely to be potential
entrepreneurs than less educated ones. This result holds after controlling for
7
Model estimates are available upon request from the authors.
13
the fear of failure and for having the skills and experience to run a company.
After individuals become entrepreneurs, the di?erences among immigrants and
non-immigrants, as well as among individuals with di?erent educational level,
disappear. This result may be interpreted as the outcome of the interplay
of two di?erent conditions: on one side, the role of higher education which
acts as an engine of entrepreneurial involvement; on the other, the role of the
national system of entrepreneurship. The latter supports the establishment of
new companies and provides equal opportunities for those who start a company,
independently from their educational or migration background.
Table 3: Average marginal e?ects over education levels.
Potential Nascent New Established
?rst generation
Lower secondary -0.0133 -0.202 0.189 0.155
(0.635) (0.242) (0.376) (0.541)
Upper Secondary and craftsman 0.0367 -0.0231 0.120 -0.0678
(0.225) (0.798) (0.285) (0.618)
Tertiary 0.136
???
-0.0450 -0.102 -0.0490
(0.000) (0.490) (0.180) (0.617)
second generation
Lower secondary 0.0604 -0.240
?
-0.154 -0.210
(0.115) (0.060) (0.554) (0.283)
Upper Secondary and craftsman 0.0122 0.0552 0.230
??
0.0346
(0.670) (0.552) (0.012) (0.754)
Tertiary 0.0256 -0.135 -0.158 -0.267
(0.476) (0.122) (0.149) (0.266)
Observations 2022 377 336 183
p-values in parentheses.
Non-immigants is the reference category.
?
p < 0.1,
??
p < 0.05,
???
p < 0.01.
14
6 Conclusions
Entrepreneurship is an important driver of economic growth, which is attracting
increasing interest from academic and policy makers alike. This study explores
entrepreneurship features focusing on the role of immigrants in promoting new
business initiatives in host countries. We analyze the e?ects of the immigration
background on di?erent phases of the entrepreneurial process, from being inter-
ested in starting a company to running an established one. We consider di?erent
types of immigration background, and distinguish between ?rst and second gen-
eration immigrants. The analysis is performed on pooled data from the Global
Entrepreneurship Monitoring surveys of 2013 and 2014 for Luxembourg.
Controlling for a set of individual characteristics (fear of failure, skills, age,
sex, education, occupation, income) and ?rm features (sector of activity), the
econometric results show that ?rst generation immigrants are more interested
in starting a new business than nationals. This e?ect is stronger for highly
educated individuals. At subsequent stages of the entrepreneurial process, the
immigration e?ect disappears. In other words, aftre having showed more in-
terest in starting a business than nationals, immigrants do not have higher
chances to succeed in starting a business and running a start-up and an estab-
lished business. This result is consistent with previous evidence from Germany
(Constant and Zimmermann, 2006). In more general terms, our ?ndings suggest
that there is a large potential of entrepreneurship among ?rst generation im-
migrants, especially among highly educated people, possibly because this group
is better equipped to start innovative business with higher intensity of knowl-
edge. Since highly innovative ?rms are more likely to positively contribute to
long-term growth of a country, policies aiming to attract highly educated immi-
grants are desirable. Furthermore, such policies should integrate the National
Entrepreneurship System. This implication has general relevance, because ex-
isting evidence suggests that population and migration trends in Luxembourg
anticipate the trends for other developed countries.
The nature of this study is essentially exploratory and some important is-
sues are left for future investigation. The current research can be expanded
in several directions First, a cross-country study could investigate if immigra-
tion and entrepreneurship follow similar patterns in other developed countries.
Second, immigration background may in?uence not only the entrepreneurship
steps, but also the choice the sector, the type, the innovativeness and the size
of established business. This is relevant for countries’ competitiveness.
15
A Annex
Table 4: Descriptive statistics
variable mean sd min max obs missing (%)
Non immigrants 0.519 0.500 0 1 4070 0.00221
First generation 0.279 0.449 0 1 4070 0.00221
Second generation 0.202 0.401 0 1 4070 0.00221
Inactive 0.803 0.397 0 1 4079 0
Potential entrepreneur or more 0.197 0.397 0 1 4079 0
Nascent entrepreneur or more 0.104 0.305 0 1 4079 0
New entrepreneur or more 0.0549 0.228 0 1 4079 0
Established entrepreneur 0.0316 0.175 0 1 4079 0
Lower secondary 0.189 0.391 0 1 3980 0.0243
Upper Secondary and craftsman 0.399 0.490 0 1 3980 0.0243
Tertiary 0.412 0.492 0 1 3980 0.0243
Knowing someone who started a business 0.339 0.474 0 1 3979 0.0245
Knowledge, skill and experience 0.403 0.491 0 1 3646 0.106
Fear of failure 0.528 0.499 0 1 3811 0.0657
Female 0.528 0.499 0 1 4079 0
Age 42.69 12.75 18 64 4079 0
0-40,000 0.210 0.407 0 1 3131 0.232
40,001-60,000 0.232 0.422 0 1 3131 0.232
60,001-80,000 0.213 0.410 0 1 3131 0.232
80,001-100,000 0.150 0.357 0 1 3131 0.232
more than 100,000 0.195 0.396 0 1 3131 0.232
Full time work 0.570 0.495 0 1 3158 0.226
Part-time work 0.104 0.306 0 1 3158 0.226
Self-employed 0.0291 0.168 0 1 3158 0.226
Seeking employment 0.0203 0.141 0 1 3158 0.226
Other occupation 0.276 0.447 0 1 3158 0.226
Manufacturing and others 0.149 0.356 0 1 397 0.0637
Knowledge Intensive Services 0.501 0.501 0 1 397 0.0637
Low Knowledge Intensive Services 0.350 0.478 0 1 397 0.0637
Year – – 0 1 4079 0
16
Table 5: Marginal e?ects after sequential logit for the probability of being a
potential entrepreneur.
Variables Coe?cients p-values
Non-immigrants ref.
First generation 0.0706
???
(0.000)
Second generation 0.0265 (0.209)
Lower secondary ref.
Upper Secondary and craftsman 0.0376
?
(0.075)
Tertiary 0.0683
???
(0.002)
Knowing other entrepreneurs 0.122
???
(0.000)
Skills and experience 0.200
???
(0.000)
Fear of failure -0.0543
???
(0.001)
Full-time ref.
Part-time 0.00321 (0.914)
Self-employed 0.523
???
(0.000)
Seeking employment 0.198
???
(0.008)
Other occupations 0.00785 (0.702)
Female 0.0110 (0.491)
Age -0.00269
???
(0.000)
0-40,000 ref.
40,001-60,000 -0.0183 (0.463)
60,001-80,000 -0.0321 (0.193)
80,001-100,000 -0.0119 (0.675)
more than 100,000 -0.00937 (0.726)
Year 0.00927 (0.538)
Observations 2022
y1 377
y0 1645
ll0 -972.6
ll -708.9
R2 0.271
p-values in parentheses; ref. denotes the reference category for dummies.
?
p < 0.1,
??
p < 0.05,
???
p < 0.01.
17
Table 6: Marginal e?ects after sequential logit for the probability of being a
nascent entrepreneur.
Variables Coe?cients p-values
Non-immigrants ref.
First generation -0.0517 (0.307)
Second generation -0.0799 (0.205)
Lower secondary ref.
Upper Secondary and craftsman -0.0783 (0.373)
Tertiary -0.136 (0.114)
Knowing other entrepreneurs 0.109
??
(0.035)
Skills and experience 0.231
???
(0.000)
Fear of failure 0.000346 (0.994)
Full-time ref.
Part-time 0.387
???
(0.000)
Self-employed 0.524
???
(0.000)
Seeking employment 0.00390 (0.971)
Other occupations 0.00352 (0.956)
Female -0.102
??
(0.035)
Age -0.000918 (0.677)
0-40,000 ref.
40,001-60,000 -0.0244 (0.727)
60,001-80,000 0.0667 (0.335)
80,001-100,000 0.0144 (0.848)
more than 100,000 0.0331 (0.648)
Year -0.0276 (0.551)
Observations 377
Success 182
Failure 195
ll0 -261.1
ll -188.1
R2 0.280
p-values in parentheses; ref. denotes the reference category for dummies.
?
p < 0.1,
??
p < 0.05,
???
p < 0.01.
18
Table 7: Marginal e?ects after sequential logit for the probability of being a
new entrepreneur.
Variables Coe?cients p-values
Non-immigrants ref.
First generation 0.00403 (0.947)
Second generation -0.0158 (0.836)
Lower secondary ref.
Upper Secondary and craftsman 0.0338 (0.762)
Tertiary -0.0450 (0.686)
Female 0.0520 (0.343)
Age 0.00446
?
(0.062)
0-40,000 ref.
40,001-60,000 0.184
??
(0.033)
60,001-80,000 0.0115 (0.901)
80,001-100,000 0.0414 (0.672)
more than 100,000 0.175
??
(0.048)
Other sectors ref.
Knowledge Intensive Services -0.0204 (0.816)
Low Knowledge Intensive Services -0.144 (0.108)
Year 0.121
??
(0.024)
Observations 336
Success 183
Failure 153
ll0 -231.6
ll -206.3
R2 0.109
p-values in parentheses; ref. denotes the reference category for dummies.
?
p < 0.1,
??
p < 0.05,
???
p < 0.01.
19
Table 8: Marginal e?ects after sequential logit for the probability of being an
established entrepreneur.
Variables Coe?cients p-values
Non-immigrants ref.
First generation -0.0373 (0.632)
Second generation -0.145 (0.326)
Lower secondary ref.
Upper Secondary and craftsman 0.0878 (0.473)
Tertiary 0.182
?
(0.095)
Female 0.0947 (0.180)
Age 0.0126
???
(0.000)
0-40,000 ref.
40,001-60,000 0.0365 (0.715)
60,001-80,000 -0.0559 (0.656)
80,001-100,000 0.0481 (0.679)
more than 100,000 0.110 (0.277)
Other sectors ref.
Knowledge Intensive Services -0.247
??
(0.045)
Low Knowledge Intensive Services -0.130 (0.253)
Year 0.102 (0.171)
Observations 183
Success 105
Failure 78
ll0 -124.8
ll -96.62
R2 0.226
p-values in parentheses; ref. denotes the reference category for dummies.
?
p < 0.1,
??
p < 0.05,
???
p < 0.01.
20
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doc_912123815.pdf
In this detailed information relating to entrepreneurship and immigration evidence from gem luxembourg.
Les articles publiés dans la série "Économie et statistiques" n'engagent que leurs auteurs. Ils ne reflètent pas forcément les
vues du STATEC et n'engagent en rien sa responsabilité.
81
Economie et Statistiques
Working papers du STATEC
mai 2015
Auteurs: Chiara PERONI,
Cesare RIILLO, Francesco
SARRACINO
Entrepreneurship and immigra-
tion: evidence from GEM Luxem-
bourg
Abstract
This study analyses the role of immigration background and education in creating new
business initiatives in Luxembourg, a country where 44% of the resident population is
immigrant. We investigate the features of entrepreneurs and of the Luxembourgish System
of Entrepreneurship using the Global Entrepreneurship Monitoring surveys of 2013 and
2014. We study the effect of immigration through all the stages of entrepreneurial process:
interest in starting a new business, effectively starting, running a new business and man-
aging an established business. We adopt a sequential logit to model entrepreneurial pro-
cess as a sequence of stages. We find that first generation immigrants are more interested
in starting a new business than non-immigrants, but they do not differ in subsequent en-
trepreneurial phases. This is relevant among highly educated people. We argue that poli-
cies to attract highly educated immigrants can promote entrepreneurial initiatives in Lux-
embourg.
Key-words: entrepreneurship; immigration; education; sequential logit; GEM; Luxembourg.
Entrepreneurship and immigration: evidence
from GEM Luxembourg
?
Chiara Peroni
†
, Cesare Riillo
‡
, and Francesco Sarracino
§
April 2, 2015
Abstract
This study analyses the role of immigration background and education
in creating new business initiatives in Luxembourg, a country where 44%
of the resident population is immigrant. We investigate the features of en-
trepreneurs and of the Luxembourgish System of Entrepreneurship using
the Global Entrepreneurship Monitoring surveys of 2013 and 2014. We
study the e?ect of immigration through all the stages of entrepreneurial
process: interest in starting a new business, e?ectively starting, running
a new business and managing an established business. We adopt a se-
quential logit to model entrepreneurial process as a sequence of stages.
We ?nd that ?rst generation immigrants are more interested in starting a
new business than non-immigrants, but they do not di?er in subsequent
entrepreneurial phases. This is relevant among highly educated people.
We argue that policies to attract highly educated immigrants can promote
entrepreneurial initiatives in Luxembourg.
Key-words: entrepreneurship; immigration; education; sequential logit;
GEM; Luxembourg.
?
The opinions and views expressed in this paper are those of the authors and do not re?ect
in any way those of STATEC.
†
Institut national de la statistique et des ´etudes ´economiques du Grand-Duch´e du Luxem-
bourg (STATEC).
‡
Institut national de la statistique et des ´etudes ´economiques du Grand-Duch´e du Luxem-
bourg (STATEC).
§
Institut national de la statistique et des ´etudes ´economiques du Grand-Duch´e du Luxem-
bourg (STATEC); Laboratory for Comparative Social Research (LCSR), National Research
University Higher School of Economics (Russia).
1
1 Introduction
Entrepreneurship, broadly de?ned as “the process whereby individuals create
new ?rms”(Reynolds et al., 2000), is regarded as an important contributor to
innovation and technological progress, a driver of productivity and ultimately
of economic growth (Schumpeter, 1934; Audretsch, 2007; Braunerhjelm et al.,
2010; Wennekers and Thurik, 1999). Moreover, successful entrepreneurs favour
knowledge spillovers, and create new jobs. The IT boom of the 90s, and in par-
ticular the emergence of highly innovative, fast expanding and highly pro?table
IT ?rms, has largely contributed to revive the attention of policy makers and
academics on entrepreneurship. Governments have become increasingly active
in designing policies to foster the entrepreneurial e?orts. In parallel, data collec-
tions projects have been launched to assess the largely anedoctical evidence on
the link between entrepreneurship and growth and to provide support to policy
actions. One of such initiatives, GEM aims to collect internationally compa-
rable data to deepen the understanding of entrepreneurial activities and their
link with countries’ economic performances. The project rests on a conceptual
framework that seeks to explain variations in countries’ growth rates studying
the entrepreneurial process (Reynolds et al., 2005). To do so, GEM models en-
trepreneurship as a process rather than a “single phase” decision. The process
comprises several phases: interest in starting a new business, intention to start,
e?ectively starting, survival of the new ?rm (for a description of the various
phases, see Amoros and Bosma, 2014).
GEM data are collected through surveys on individuals conducted at coun-
try level. These data have been used mainly to study individual determinants
of entrepreneurial involvement as well as links between entrepreneurship and
economic growth (for a survey of the literature using GEM data, one can see
Alvarez et al., 2014). Recent GEM waves have also focused on special topics
such as the role of job satisfaction and well-being on entrepreneurial e?orts, as
well as the entrepreneurial attitudes of migrants (Xavier et al., 2013; Amoros
and Bosma, 2014).
This study investigates the role of the immigration background in entrepreneurial
activities using GEM data for Luxembourg. Descriptive GEM-based evidence
shows that in Luxembourg the chance of becoming an entrepreneur is much
higher for migrants than for nationals (Fletcher et al., 2014). Here, we further
investigate this issue and extend this analysis as follows. We analyse the interac-
tions of individual aspects — skills, education, previous experiences, attitudes,
income, networks — with the immigrant status, from the propensity to start
a business to running an established one, using a sequential logit model (Tutz,
1991). This method allows us to model entrepreneurship as a sequential process,
thus better re?ecting the GEM framework, and to study the di?erent barriers
that immigrants might face at di?erent stages of the entrepreneurial process.
This paper is structured as follows. Section 2 links the study to the existing
empirical and theoretical literature, and gives background information on Lux-
embourg. Section 3 describes the data used in this analysis, Section 3 describes
the method used to obtain empirical results presented in Section 5, while Section
6 gives concluding remarks and policy implications.
2
2 Background
Population movements and entrepreneurship are regarded as drivers of economic
growth, but so far have been mainly analysed separately. Economists have re-
cently turned to investigate the economic contribution of immigrants (Wennek-
ers and Thurik, 1999; Hunt and Gauthier-Loiselle, 2010; Peri, 2012; Kerr et al.,
2013), suggesting a positive impact of migrants on innovation activities and pro-
ductivity. At the aggregate level, Peri (2012) ?nds that immigration increases
total factor productivity, but negatively a?ects the skill-bias of the labour force.
Kerr et al. (2013) analyse the impact of immigration at ?rm-level using matched
employees-employers data, and ?nd that skilled immigration expands skilled em-
ployment and ?rms innovation rates. Hunt and Gauthier-Loiselle (2010) show
that skilled immigrants have improved innovation performance in the US over
the period 1990-2000. These authors focus on direct involvement of immigrants
in research and development activities, and measure innovation by patents per
capita; interestingly, they note that the presence of immigrants may be linked
to innovation through the provision of management and entrepreneurship skills
(this is referred to as the immigrants’ indirect contribution to innovation).
Empirical evidence on the link between immigration and entrepreneurship is
scarce, possibly due to di?culties in observing immigrants’ contribution to en-
trepreneurial activities. Nonetheless, anedoctical evidence suggests a strong con-
tribution of immigrants to entrepreneurship (Wadhwa, 2011; Hohn et al., 2012).
Basic statistics reported by Xavier et al. (2013) and OECD (2010) show that mi-
grants are more likely to engage in entrepreneurial activities than non-migrants.
Among the few studies exploring the link immigration-entrepreneurship, Con-
stant and Zimmermann (2006) study the impact of ethnicity and immigration
status on self-employment decisions using the 2000 wave of the German Socio-
Economic Panel (SOEP). They show that the percentage of self-employed work-
ers is low in Germany and more so among non-natives, despite immigrants
self-employed earn a lot more than their salary workers counterparts. Overall,
?gures suggest that nationals and immigrants in Germany become entrepreneurs
largely for the same reasons. Using GEM data collected for Spain, Irastorza and
Pena (2007) ?nd that immigrants are more likely to become entrepreneurs than
natives. Batista and Umblijs (2014) analyse the relationship between risk pref-
erences and migrant entrepreneurship using data from a survey on immigrants
in the greater Dublin area; they ?nd that willingness to take risks, experience
and being part of migrants enclaves are signi?cant predictors of entrepreneur-
ship among immigrants.
This study expands this literature analyzing the role of immigrants in creat-
ing new business initiatives in Luxembourg. Before discussing the Luxembourg
case, the following gives a brief account of the theories explaining why migrants
play a speci?c role in the entrepreneurial e?ort.
2.1 Immigrants and entrepreneurship
The theories seeking to explain the relationship between immigration and en-
trepreneurial involvement can be categorised in two broad groups: the ?rst
group relies on speci?c features of immigrants to explain di?erences in the
propensity to start a business compared to non-immigrants; the second group
focuses on the institutional and cultural environment of the host country.
3
According to the theories in the ?rst group, immigrants have higher chances
to start a new business because various kind of disadvantages (linguistic, racial,
educational) steer their willingness to become entrepreneurs (Light, 1979; Bor-
jas, 1986; Coate and Tennyson, 1992; Clark and Drinkwater, 2000; Parker, 2004;
Fregetto, 2004). Some scholars argue that immigrants opt for self-employment
to avoid low paid jobs — or those jobs perceived as preventing their upward
mobility (Paulson and Townsend, 2005; Rissman, 2006). Other researchers em-
phasise the role of cultural traits. The main idea is that immigrants “inherit”
the cultural traits of their countries of origin; whenever these traits determine
a preference for self-employment, they result into higher chances of engaging
in entrepreneurship (Masurel et al., 2004; Hofstede, 2007; Chrysostome, 2010).
Some scholars have extended the latter model to account for the role of social
networks linked to the country of origin (this is sometime referred to as the
human capital theory). It is argued that such networks provide migrants with
easy access to the resources — labor, capital, information and family support
— needed to start a business (Sanders and Nee, 1996; Peters, 2002; Basu and
Altinay, 2002).
Linked to the human capital theory, the middleman minority and the ethnic
enclave theories (Nestorowicz, 2012) also belong to the ?rst group of theories.
The former, developed at the beginning of the 1970s, rests on the observation
that successful migrant-led business initiatives are more commonly observed
in areas with relatively large shares of immigrants. The features of migrants’
business activities – such as agents, money lenders, rent collectors, and brokers
– favoured the view of immigrants as “middlemen”, i.e. intermediaries between
market actors (Nestorowicz, 2012). This model sees the immigrant and the host
community in a symbiotic equilibrium between con?ict and dependence due
to economic success (Aldrich and Waldinger, 1990; Terjesen and Elam, 2009;
Nestorowicz, 2012).
The ethnic enclave theory focuses on the existence of immigrant enclaves in
the host society.
1
The main idea is that immigrants have increased opportunities
to start new businesses in areas where existing activities are run by individuals
belonging to the same ethnic group (Altinay, 2008).
2
The theory posits that
enclaves bene?t the entrepreneurial initiative due to the high intra-group sol-
idarity, shared values, norms and attitudes that facilitate economic activities
(Auster and Aldrich, 1984; Zhou and Logan, 1989). This stream of research has
also investigated the conditions favouring the settling of enclaves, and whether
the existence of an enclave is socially desirable. Two conditions have been iden-
ti?ed for the emergence of economic enclaves: i. access to su?cient start-up
capital, usually through immigrants’ networks and connections with the coun-
try of origin; ii. a steady arrival of new labour force within the enclave (Portes
and Jensen, 1989; Portes and Shafer, 2007). Enclaves, however, are perceived as
“separated” from the resident population, a condition that may favour feelings
of hostility, discrimination, and ultimately con?ict between the immigrant and
the non-immigrant population. This theory has received considerable attention
as, with some extensions and re?nements, it proved to be able to explain some
observed patterns (Sanders and Nee, 1987; Waldinger, 1993; Light et al., 1994).
1
Ethnic enclaves can be de?ned as self-contained minority communities nested in metropoli-
tan areas (Wilson and Martin, 1982)
2
In a well-known study, Wilson and Portes (1980) found that Cuban immigrants working
for Cuban employers in Miami experienced signi?cant returns to their human capital.
4
The second group of theories explains migrants involvement in entrepreneur-
ship focusing on the interaction between migrants’ individual features and the
institutions and characteristics of the hosting societies and markets. Waldinger
et al. (1990) proposed the so-called “interactive model” according to which im-
migrants’ entrepreneurial involvement is the outcome of the interaction between
immigrants’ own resources and societies’ opportunity structures. The latter are
historically-shaped circumstances, such as market conditions that do not require
mass production or distribution, characterised by decreasing return to scale in
which ethnic goods are in demand. These conditions allow the mobilization
of immigrants’ characteristics – named as ethnic strategies – towards the en-
trepreneurial initiative (P¨ utz, 2003; Volery, 2007). More recently, Kloosterman
and Rath (2001) re?ned the interactive model to account for country-speci?c
institutional frameworks. These authors developed the “mixed embeddedness”
model suggesting that while immigrants belong to ethnic networks, they are
also embedded (entrenched) in speci?c market conditions, socio-economic and
politico-institutional environments. The interactive and the mixed embedded-
ness model have received considerable attention in the literature, and have been
extended to account for gender di?erences, the role of family business, of subur-
ban ethnic clusters, of cultural characteristics, and to account for the evolution
of institutions and market conditions (Light and Rosenstein, 1995; Bonacich,
1993; Rath, 2002; P¨ utz, 2003; Portes and Rumbaut, 2006; Li, 1998; Klooster-
man, 2010). Finally, these studies have contributed to identify a set of control
variables – such as managerial and other individual abilities, family background,
occupational status, ?nancial constraints, and economic activity – for studying
determinants of self-employment (Aliaga-Isla and Rialp, 2013).
5
Figure 1: Total early-stage entrepreneurial activity (TEA) 2013.
Percentage of adults engaged in entrepreneurial activities on active population
(18-64 years of age). Note: adapted from GEM Global Report 2013.
2.2 The focus on Luxembourg
Luxembourg’s demographic structure makes it an interesting case for the study
of national systems of innovation and entrepreneurship. Since 1990, the resident
population has increased by more than one third from immigration.
3
At the
same time, increased demand and supply of labour have driven the expansion
of domestic employment.
4
In this context, Jean et al. (2007) and Barone (2009)
document that the country has been successful in implementing policies for
promoting skilled immigration.
In Luxembourg, the share of immigrants in the resident population is higher
than in any other European country (see ?gure 2). As of the ?rst of January
2013 about 45% of the Luxembourgish resident population is constituted of im-
migrants coming from more than 100 di?erent countries (STATEC, 2012). Thus,
Luxembourg o?ers a unique combination in terms of high share of immigrants
and of high diversity among ethnic groups. In addition, the country anticipates
some tendencies that are expected to a?ect other European countries in the
coming years (see ?gure 3). Population projections by EUROSTAT show that
by 2061 a majority of EU countries are expected to signi?cantly increase the
share of non-nationals on their resident population (Lanzieri, 2011).
5
Accord-
3
Luxembourg en chi?res, STATEC, 2014 can be found on:http://www.statistiques.public.lu/en/publications/series/lux-figures/index.html
4
On labour force statistics in Luxembourg one can see
data and publications on STATEC’s website, in particularhttp://www.statistiques.public.lu/en/population-employment/index.html. One can
also see the various issues of the Rapport travail et coh´esion sociale, published regularly by
STATEC.
5
In six other European countries – namely, Cyprus, Austria, Germany, Great Britain,
Ireland, and Belgium – people with an immigration background will account for more than
6
Figure 2: Share of non-nationals in the resident population, 1 January 2013.
Source: authors’ own elaboration, Eurostat data.
ing to such predictions, the challenges that Luxembourg faces in 2013 - 2014
will become relevant also to other countries. Hence, lessons on the relationship
between immigration and determinants of entrepreneurial activity drawn from
Luxembourg are also of more general relevance.
These facts suggest that the Luxembourg case may contribute to a better
understanding of the role of migration on innovation activities and entrepreneur-
ship. According to GEM, Luxembourg is the seventh country with the highest
share of people involved in TEA after United States, Canada, Singapore, Israel,
Netherlands, and Ireland. (This is shown in ?gure 1, which depicts population’s
involvement in early-stage entrepreneurial activities.) Moreover, existing evi-
dence suggests that immigrants play a special part in entrepreneurship: Ries
(2006) reports that foreigners account for the 75% of entrepreneurs in Luxem-
bourg.
In the light of the evidence from previous studies and of the features of the
Luxembourgish socio-demographic composition, we test the following hypothe-
sis:
1. immigrants have higher chances than nationals to be willing to engage in
entrepreneurial process;
2. condition upon the willingness to engage in entrepreneurial process, the
chances that an immigrant will start a new company are not signi?cantly
di?erent from those for nationals;
3. higher educated immigrants have higher chances to start a new business
in Luxembourg.
3 Data
We use data from the Adult Population Surveys of Global Entrepreneurship
Monitor (GEM) survey. GEM is a rich, internationally harmonized source of
30% of the resident population.
7
Figure 3: Share of foreign background persons in the EU Member States in 2011
and projected in 2061.
Source: Lanzieri (2011).
individual-level information about people’s motives and aspirations towards en-
trepreneurship. This survey is currently administered in more than 100 countries
world-wide, covering more than 75% of the world population.
In 2013 and 2014 the National Statistical O?ce of Luxembourg (STATEC),
together with the University of Luxembourg and the Centre de Recherche Publique
“Henri Tudor” administered the ?rst two waves of the GEM survey in Luxem-
bourg. In both waves, a nationally representative sample of about 2000 people
replied to a questionnaire about entrepreneurial activity, aspirations and atti-
tudes. The aim of the survey was to collect information about the attitudes and
behaviours leading to the creation of entrepreneurial activities, along with a set
of socio-demographic and economic variables.
Data have been collected on a single sample of the population with an age
comprised between 18 and 64 years. Approximately half of the sample has
being interviewed using ?xed line telephone, and the remaining half ?lled-in
an on-line survey. In the latter case, individuals have been randomly selected
from a data-base with over 14,000 e-mail addresses. These methods do not cast
particular doubts about the selection of the sample as virtually every household
in Luxembourg has a land-line and more than 92% of the population has internet
access.
We pool the waves of 2013 and 2014 to retrieve individual level information
about immigration status, entrepreneurship activities, entrepreneurial attitudes,
gender, age, education of the respondents and sector of economic activity of the
new business. Pooling the waves increases the sample size and allows more
precise estimations.
8
3.1 Dependent variables
Our empirical strategy follows the GEM model. This model describes the en-
trepreneurship process as composed by the following sequence of stages:
1. Inactive;
2. Potential (expecting to start a new business within the next three years);
3. Nascent entrepreneur (involved in setting up a business);
4. New entrepreneur (owner-manager of ?rm younger than 42 months that
pays wages during last three months);
5. Established entrepreneur (owner-manager of ?rm older than 42 months
that pays wages during last three months).
Individuals who wish to establish a ?rm cross the various stages. Crossing
stages depends on subjective and institutional factors that allow an individ-
ual to become a potential entrepreneur, to decide to start a ?rm, to set it up
and to lead an established company. The various phases are observed via re-
spondents’ self-declarations of involvement in entrepreneurial activity. In other
words, respondents are asked to situate their company in a speci?c phase of the
entrepreneurial process. Based on these answers, we built a set of four dummy
variables, one for each phase of the process. These variables take value 1 if the
respondent is in a speci?c or higher phase and zero otherwise. This is illustrated
in ?gure 4.
3.2 Variables of interest
The main independent variable is the migratory background of the respondents.
We distinguish the respondents in nationals (individuals born in Luxembourg
with both Luxembourgian parents), ?rst generation immigrants (individuals
born abroad) and second generation immigrants (individuals born in Luxem-
bourg with at least one foreign parent).
The distinction in ?rst and second generation immigrant is relevant because
the attitudes, behaviors and motives of immigrant entrepreneurs may di?er sig-
ni?cantly between ?rst and second generation immigrants. For example, it is
plausible that the second generation of immigrants reports more similar features
to the nationals than to the ?rst generation. This might be due to the fact that
the second generation is born and grows up in Luxembourg, and therefore it
gets educated and socialized as nationals (Callens et al., 2014). However, there
are also reasons to believe that the second generation is not di?erent from the
?rst one. This argument is based on the recent work by Algan and Cahuc (2010)
showing that trust in others is an individual trait partly inherited by parents,
thus depending on trust prevalent in the country of origin. Since trust in others
is an important factor shaping people’s attitudes and intentions to invest in
an economic activity, it is plausible to expect that eventual di?erences between
?rst generation immigrants and nationals are also mirrored in the second gen-
eration. Descriptive statistics in table 1 show that ?rst generation migrants are
more active in entrepreneurial activities than nationals and second-generation
migrants over all stages of the entrepreneurial process.
9
Table 1: Entrepreneurship activities by immigration background
Non-immigrants First Second Total
generation generation
Inactive 0.840 0.734 0.805 0.803
Potential entrepreneur or more 0.160 0.266 0.195 0.197
Nascent entrepreneur or more 0.0894 0.132 0.102 0.104
New entrepreneur or more 0.0483 0.0669 0.0548 0.0548
Established entrepreneur 0.0303 0.0387 0.0244 0.0314
Percentage of population engaged in entrepreneurial activities by immigration back-
ground on totals.
3.3 Control variables
The entrepreneurial attitude is measured by three dummy variables. Each vari-
able takes value 1 if the respondent:
1. knows someone who started a business;
2. perceives himself as skilled and experienced enough to start a new business;
3. fears to fail in starting a new business.
The attitude towards starting a new business are particularly relevant only
in ?rst phases of entrepreneurship process (up to e?ectively starting a new
business) and are not implemented when investigating later phases. It is worth
noticing that the fear of failure allows to control for individual risk aversion. This
is particularly important to address the self-selection concern due to the fact
that more risk prone individuals can also be more likely to become immigrants
and to start new businesses.
To account for individual socio-economic conditions, we control for age, gen-
der, education, occupation and income of the respondent. Age is measured as
a continuous variable ranging from 18 to 64 years. Gender is a dummy vari-
able set to 1 if the respondent is male and 0 otherwise. Education is observed
by a set of dummy variables respectively set to 1 if the respondent declares to
have one of the following levels of education classi?ed in line with the Inter-
national Standard Classi?cation of Education. Retained education categories
are a) lower secondary; b) upper secondary and craftsman; c) tertiary (e.g.
bachelor and higher). Employment status, implemented only in the ?rst two
phases of entrepreneurial process, is measured with a categorical variable that
10
takes the following values: a)full-time, b) part-time, c) Self-employed d) Seeking
employment e) Others (e.g. students retired etc..). The availability of private
?nancial resources to fund the business is observed through respondent’s self-
declaration of belonging to one of the following income classes: 0-40,000; 40,001-
60,000; 60,001-80,000; 80,001-100,000; more than 100,000. In later phases of
entrepreneurial process, individual’s income can be seen as a measure of the
pro?tability of the business.
The sectors of economic activities are observed according to the International
Standard Industrial Classi?cation (ISIC). Sectors are aggregated on the basis of
knowledge intensity as de?ned by (EUROSTAT, 2008). Retained categories are:
knowledge intensive services, Low knowledge intensive services and others (e.g.
agriculture, manufacturing). Finally, to account for time e?ect, we include a
dummy variable for each year when the survey was conducted (2013 and 2014).
All variables are interacted with the immigration variable to capture the possible
di?erent in?uence on the probability to become an entrepreneur for people with
di?erent migratory backgrounds. Descriptive statistics are reported in table 4
in the annex.
4 Methodology
This section presents the empirical strategy used in this analysis. As noted in
previous sections, the GEM framework models entrepreneurship as a process
comprising several stages. These include the intention to start a new business,
the involvement in new ventures, and the survival of new ?rms. Thus, each
entrepreneur passes through intermediate steps before setting up an established
business; at each stage, the entrepreneur can stop or proceed to the next phase.
Figure 4 gives a graphical representation of the entrepreneurship model. To
account for the GEM setting, we adopt a variant of the sequential model of
Tutz (1991) proposed by Buis (2010).
Figure 4: Sequential entrepreneurial model.
No active
Potential
Stop
Nascent
Stop
New
Stop
Established
Stop
Here, the idea is that only part of the population is potentially interested to
start a new business, and among them only a fraction will e?ectively start a
new business. This framework allows us to establish whether the probability
11
to successfully proceed over subsequent stages di?er over immigration status
(nationals; ?rst generation; second generation). The probabilities p that an
individual proceeds through the various stages are as follows:
p
1i
=
e
(?
1
+?
1
Imm.+?
1
X
i1
)
1 + e
(?
1
+?
1
Imm.+?
1
X
i1
)
(1)
p
2i
=
e
(?
2
+?
2
Imm.+?
2
X
i2
)
1 + e
(?
2
+?
2
Imm.+?
1
X
i2
)
if phase
1i
= 1 (2)
p
3i
=
e
(?
3
+?
3
Imm.+?
3
X
i3
)
1 + e
(?
3
+?
3
Imm.+?
1
X
i3
)
if phase
2i
=1 (3)
p
4i
=
e
(?
4
+?
4
Imm.+?
4
X
i4
)
1 + e
(?
4
+?
4
Imm.+?
1
X
i4
)
if phase
3i
=1 (4)
Where i denotes the individual, and Imm. the immigration background. One
can see that this model is composed by 5 phases, resulting in 4 transitions from
inactive to established enterpreneurs. Entrepreneurs can move to a new phase
only if they have achieved the previous stage (see ?gure 4). The transition-
speci?c intercept is ?
k
, with k = 1, 2, . . . , 4; ?
k
, the coe?cient of the immigration
status, is the coe?cient of interest; X is a vector of control variables.
The model above is estimated by ?tting logistic regressions for each transi-
tion, using the sub-sample constituted by individuals who have achieved that
stage (Tutz, 1991). As factors a?ecting the transition probabilities may vary
over the sequence, we do not restrict the set of control variables to be the same
at each phase.
6
To capture possible di?erences between immigrants and non-
immigrants for various levels of the control variables, we also include interaction
e?ects of the immigration variable Imm. with all control variables.
5 Results
We ?nd that the willingness to engage in entrepreneurial activities is higher
for ?rst generation migrants than for Luxembourgish nationals. At subsequent
stages of the entrepreneurial process, however, the behaviour of migrants and
non-migrants does not di?er signi?cantly. Table 2 reports marginal e?ects of the
migration background on the probabilities of engaging in entrepreneurial activi-
ties. One can see that the probability that a ?rst generation migrants becomes a
potential entrepreneur is 7 percentage points higher than for non-migrants (?rst
column). Among potential entrepreneurs, however, the probability to start a
new business does not di?er signi?cantly over migration backgrounds. Similar
results are found for the subsequent steps of the entrepreneurial process, i.e.
running and successfully establishing a new ?rm.
6
Questionnaire provides information about the sector of economic activity only after the
starting of the new venture. Therefore only the last two phases include these controls.
12
A possible explanation for this result is that individuals that are more willing
to take risks are more likely to migrate. In other words, it is plausible to expect
that our results are due to self-selection of “risk-lover” people among migrants.
To account for this source of endogeneity, we control for the respondents’ fear
of failure. Indeed, the fear of failure may be regarded as a measure of the risk
aversion of the respondents (Batista and Umblijs, 2014).
The average marginal e?ects on the transition probabilities for all variables
in the model are reported in the Annex A.
7
Table 2: Average marginal e?ects at di?erent entrepreneurial steps.
Potential Nascent New Established
First generation 0.0706
???
-0.0517 0.00403 -0.0373
(0.000) (0.307) (0.947) (0.632)
Second generation 0.0265 -0.0799 -0.0158 -0.145
(0.209) (0.205) (0.836) (0.326)
Observations 2022 377 336 183
p-values in parentheses.
Non-immigants is the reference category.
?
p < 0.1,
??
p < 0.05,
???
p < 0.01.
5.1 The role of education
This section focuses on the e?ects of variables describing the educational level
of individuals on entrepreneurial activities. This is relevant to Luxembourg
because of the important share of highly educated immigrants living in the
country. This analysis may help to better understand how human capital af-
fects the relation between immigration background and entrepreneurship. The
idea is that innovative businesses, often concentrated in high-tech and high-
knowledge industries, usually require speci?c skills and highly trained people.
The availability of such skills may be crucial in determining both the probability
to become entrepreneurs as well as the survival of new ventures.
To investigate this aspect, we re-estimate the likelihood of transitioning
across entrepreneurship phases taking into account di?erent educational lev-
els. Results are shown in table 3. Highly educated ?rst generation immigrants
are more likely to become potential entrepreneurs. In particular, ?rst gener-
ation immigrants with tertiary education are more likely to be potential en-
trepreneurs than non-immigrants with tertiary education (about 14 percentage
points), while we do not ?nd any statistical di?erence across educational levels
and migration status in successive steps.
Second generation immigrants with lower secondary education are less likely
to become nascent entrepreneurs compared to non-immigrants with comparable
educational level. However, second generation immigrants with upper secondary
and craftsman education are more likely to involve in start-ups.
Summarizing, highly educated immigrants are more likely to be potential
entrepreneurs than less educated ones. This result holds after controlling for
7
Model estimates are available upon request from the authors.
13
the fear of failure and for having the skills and experience to run a company.
After individuals become entrepreneurs, the di?erences among immigrants and
non-immigrants, as well as among individuals with di?erent educational level,
disappear. This result may be interpreted as the outcome of the interplay
of two di?erent conditions: on one side, the role of higher education which
acts as an engine of entrepreneurial involvement; on the other, the role of the
national system of entrepreneurship. The latter supports the establishment of
new companies and provides equal opportunities for those who start a company,
independently from their educational or migration background.
Table 3: Average marginal e?ects over education levels.
Potential Nascent New Established
?rst generation
Lower secondary -0.0133 -0.202 0.189 0.155
(0.635) (0.242) (0.376) (0.541)
Upper Secondary and craftsman 0.0367 -0.0231 0.120 -0.0678
(0.225) (0.798) (0.285) (0.618)
Tertiary 0.136
???
-0.0450 -0.102 -0.0490
(0.000) (0.490) (0.180) (0.617)
second generation
Lower secondary 0.0604 -0.240
?
-0.154 -0.210
(0.115) (0.060) (0.554) (0.283)
Upper Secondary and craftsman 0.0122 0.0552 0.230
??
0.0346
(0.670) (0.552) (0.012) (0.754)
Tertiary 0.0256 -0.135 -0.158 -0.267
(0.476) (0.122) (0.149) (0.266)
Observations 2022 377 336 183
p-values in parentheses.
Non-immigants is the reference category.
?
p < 0.1,
??
p < 0.05,
???
p < 0.01.
14
6 Conclusions
Entrepreneurship is an important driver of economic growth, which is attracting
increasing interest from academic and policy makers alike. This study explores
entrepreneurship features focusing on the role of immigrants in promoting new
business initiatives in host countries. We analyze the e?ects of the immigration
background on di?erent phases of the entrepreneurial process, from being inter-
ested in starting a company to running an established one. We consider di?erent
types of immigration background, and distinguish between ?rst and second gen-
eration immigrants. The analysis is performed on pooled data from the Global
Entrepreneurship Monitoring surveys of 2013 and 2014 for Luxembourg.
Controlling for a set of individual characteristics (fear of failure, skills, age,
sex, education, occupation, income) and ?rm features (sector of activity), the
econometric results show that ?rst generation immigrants are more interested
in starting a new business than nationals. This e?ect is stronger for highly
educated individuals. At subsequent stages of the entrepreneurial process, the
immigration e?ect disappears. In other words, aftre having showed more in-
terest in starting a business than nationals, immigrants do not have higher
chances to succeed in starting a business and running a start-up and an estab-
lished business. This result is consistent with previous evidence from Germany
(Constant and Zimmermann, 2006). In more general terms, our ?ndings suggest
that there is a large potential of entrepreneurship among ?rst generation im-
migrants, especially among highly educated people, possibly because this group
is better equipped to start innovative business with higher intensity of knowl-
edge. Since highly innovative ?rms are more likely to positively contribute to
long-term growth of a country, policies aiming to attract highly educated immi-
grants are desirable. Furthermore, such policies should integrate the National
Entrepreneurship System. This implication has general relevance, because ex-
isting evidence suggests that population and migration trends in Luxembourg
anticipate the trends for other developed countries.
The nature of this study is essentially exploratory and some important is-
sues are left for future investigation. The current research can be expanded
in several directions First, a cross-country study could investigate if immigra-
tion and entrepreneurship follow similar patterns in other developed countries.
Second, immigration background may in?uence not only the entrepreneurship
steps, but also the choice the sector, the type, the innovativeness and the size
of established business. This is relevant for countries’ competitiveness.
15
A Annex
Table 4: Descriptive statistics
variable mean sd min max obs missing (%)
Non immigrants 0.519 0.500 0 1 4070 0.00221
First generation 0.279 0.449 0 1 4070 0.00221
Second generation 0.202 0.401 0 1 4070 0.00221
Inactive 0.803 0.397 0 1 4079 0
Potential entrepreneur or more 0.197 0.397 0 1 4079 0
Nascent entrepreneur or more 0.104 0.305 0 1 4079 0
New entrepreneur or more 0.0549 0.228 0 1 4079 0
Established entrepreneur 0.0316 0.175 0 1 4079 0
Lower secondary 0.189 0.391 0 1 3980 0.0243
Upper Secondary and craftsman 0.399 0.490 0 1 3980 0.0243
Tertiary 0.412 0.492 0 1 3980 0.0243
Knowing someone who started a business 0.339 0.474 0 1 3979 0.0245
Knowledge, skill and experience 0.403 0.491 0 1 3646 0.106
Fear of failure 0.528 0.499 0 1 3811 0.0657
Female 0.528 0.499 0 1 4079 0
Age 42.69 12.75 18 64 4079 0
0-40,000 0.210 0.407 0 1 3131 0.232
40,001-60,000 0.232 0.422 0 1 3131 0.232
60,001-80,000 0.213 0.410 0 1 3131 0.232
80,001-100,000 0.150 0.357 0 1 3131 0.232
more than 100,000 0.195 0.396 0 1 3131 0.232
Full time work 0.570 0.495 0 1 3158 0.226
Part-time work 0.104 0.306 0 1 3158 0.226
Self-employed 0.0291 0.168 0 1 3158 0.226
Seeking employment 0.0203 0.141 0 1 3158 0.226
Other occupation 0.276 0.447 0 1 3158 0.226
Manufacturing and others 0.149 0.356 0 1 397 0.0637
Knowledge Intensive Services 0.501 0.501 0 1 397 0.0637
Low Knowledge Intensive Services 0.350 0.478 0 1 397 0.0637
Year – – 0 1 4079 0
16
Table 5: Marginal e?ects after sequential logit for the probability of being a
potential entrepreneur.
Variables Coe?cients p-values
Non-immigrants ref.
First generation 0.0706
???
(0.000)
Second generation 0.0265 (0.209)
Lower secondary ref.
Upper Secondary and craftsman 0.0376
?
(0.075)
Tertiary 0.0683
???
(0.002)
Knowing other entrepreneurs 0.122
???
(0.000)
Skills and experience 0.200
???
(0.000)
Fear of failure -0.0543
???
(0.001)
Full-time ref.
Part-time 0.00321 (0.914)
Self-employed 0.523
???
(0.000)
Seeking employment 0.198
???
(0.008)
Other occupations 0.00785 (0.702)
Female 0.0110 (0.491)
Age -0.00269
???
(0.000)
0-40,000 ref.
40,001-60,000 -0.0183 (0.463)
60,001-80,000 -0.0321 (0.193)
80,001-100,000 -0.0119 (0.675)
more than 100,000 -0.00937 (0.726)
Year 0.00927 (0.538)
Observations 2022
y1 377
y0 1645
ll0 -972.6
ll -708.9
R2 0.271
p-values in parentheses; ref. denotes the reference category for dummies.
?
p < 0.1,
??
p < 0.05,
???
p < 0.01.
17
Table 6: Marginal e?ects after sequential logit for the probability of being a
nascent entrepreneur.
Variables Coe?cients p-values
Non-immigrants ref.
First generation -0.0517 (0.307)
Second generation -0.0799 (0.205)
Lower secondary ref.
Upper Secondary and craftsman -0.0783 (0.373)
Tertiary -0.136 (0.114)
Knowing other entrepreneurs 0.109
??
(0.035)
Skills and experience 0.231
???
(0.000)
Fear of failure 0.000346 (0.994)
Full-time ref.
Part-time 0.387
???
(0.000)
Self-employed 0.524
???
(0.000)
Seeking employment 0.00390 (0.971)
Other occupations 0.00352 (0.956)
Female -0.102
??
(0.035)
Age -0.000918 (0.677)
0-40,000 ref.
40,001-60,000 -0.0244 (0.727)
60,001-80,000 0.0667 (0.335)
80,001-100,000 0.0144 (0.848)
more than 100,000 0.0331 (0.648)
Year -0.0276 (0.551)
Observations 377
Success 182
Failure 195
ll0 -261.1
ll -188.1
R2 0.280
p-values in parentheses; ref. denotes the reference category for dummies.
?
p < 0.1,
??
p < 0.05,
???
p < 0.01.
18
Table 7: Marginal e?ects after sequential logit for the probability of being a
new entrepreneur.
Variables Coe?cients p-values
Non-immigrants ref.
First generation 0.00403 (0.947)
Second generation -0.0158 (0.836)
Lower secondary ref.
Upper Secondary and craftsman 0.0338 (0.762)
Tertiary -0.0450 (0.686)
Female 0.0520 (0.343)
Age 0.00446
?
(0.062)
0-40,000 ref.
40,001-60,000 0.184
??
(0.033)
60,001-80,000 0.0115 (0.901)
80,001-100,000 0.0414 (0.672)
more than 100,000 0.175
??
(0.048)
Other sectors ref.
Knowledge Intensive Services -0.0204 (0.816)
Low Knowledge Intensive Services -0.144 (0.108)
Year 0.121
??
(0.024)
Observations 336
Success 183
Failure 153
ll0 -231.6
ll -206.3
R2 0.109
p-values in parentheses; ref. denotes the reference category for dummies.
?
p < 0.1,
??
p < 0.05,
???
p < 0.01.
19
Table 8: Marginal e?ects after sequential logit for the probability of being an
established entrepreneur.
Variables Coe?cients p-values
Non-immigrants ref.
First generation -0.0373 (0.632)
Second generation -0.145 (0.326)
Lower secondary ref.
Upper Secondary and craftsman 0.0878 (0.473)
Tertiary 0.182
?
(0.095)
Female 0.0947 (0.180)
Age 0.0126
???
(0.000)
0-40,000 ref.
40,001-60,000 0.0365 (0.715)
60,001-80,000 -0.0559 (0.656)
80,001-100,000 0.0481 (0.679)
more than 100,000 0.110 (0.277)
Other sectors ref.
Knowledge Intensive Services -0.247
??
(0.045)
Low Knowledge Intensive Services -0.130 (0.253)
Year 0.102 (0.171)
Observations 183
Success 105
Failure 78
ll0 -124.8
ll -96.62
R2 0.226
p-values in parentheses; ref. denotes the reference category for dummies.
?
p < 0.1,
??
p < 0.05,
???
p < 0.01.
20
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