The entrepreneur’s mode of entry: what’s matter?
The French case
First draft
Françoise Bastié
Sylvie Cieply±
Pascal Cussy+
Abstract: An extensive literature focuses on the decision to become an entrepreneur. At the
micro level, this literature on occupational choices analyses the transition process from the
statute of being employed into the statute of being self-employed. In this framework,
entrepreneurship corresponds to the start of new ventures. However, entrepreneurship cannot
be reduced to this mode of entry and the role of takeovers must be taken into account. In this
paper, we focus on the determinants of entry’s mode. More precisely, we seek to investigate
the process by which an entrepreneur decides to create a new venture or to buy an existing
firm. We consider two categories of determinants: individuals’ characteristic and market
specific factors. Our estimates of entry’s mode with the SINE database appear to be broadly
consistent with our hypothesis concerning managerial skills, financial capital and marketspecific factors.
Key-words: takeover, buyout, ex nihilo creation, human capital, social capital,
financial capital, market-specific factors
JEL Classification: D21, D82, L29.

CREM Caen, UMR CNRS 6211, U.F.R. de Sciences Economiques et de Gestion, Université de Caen,
esplanade de la paix, 14032 Caen cedex, France, francoise.bastie@unicaen.fr.
±
CREM Caen, UMR CNRS 6211, I.U.P. Banque Assurance, Université de Caen, esplanade de la paix, 14032
Caen cedex, France, sylvie.cieply@unicaen.fr.
+
CREM Caen, UMR CNRS 6211, U.F.R. de Sciences Economiques et de Gestion, Université de Caen,
esplanade de la paix, 14032 Caen cedex, France, pascal.cussy@unicaen.fr.
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Page 1
1. Introduction
A broad range of determinants explain entrepreneurship. Verheul, Audretsch, Thurik
et alii. (2002) provide a useful “eclectic theory” that shows how various these determinants
are. First, at the macro level, entrepreneurship is influenced by a range of environmental
factors, such as economic development, globalisation, population growth, population
composition by the age structure, the proportion of immigrants or women and policy
measures. Then, at the meso level, market-specific factors influence entrepreneurship; profit
opportunities in some sectors can indeed drive individuals to entry. The development of the
service economy, the decrease of vertical integration and conglomeration since the mid1970’s and the development of clustering and regional industries lead to a decrease in mean
firm size and may foster entrepreneurship. Finally, at the micro-level, an extensive literature
focuses on the individual’ decision to become an entrepreneur. This academic research
analyses the transition process from the statute of being employed into the statute of being
self-employed. At the micro level, in quite all these studies, entrepreneurship corresponds to
the start of new ventures. However, entrepreneurship cannot be reduced to this unique mode
of entry. Parker and Van Praag (2006, pp. 1-2) assert: “… starting a new firm from scratch is
not the only way individuals can become entrepreneurs. They can also take over an existing
firm, including family business if they come from a business-owning family. One can
therefore separate the mode of entry from the entry decision itself”.
This question of the entrepreneur’s mode of entry is important in Western Europe.
European entrepreneurs are indeed ageing so that, according to the European Commission
(European commission, 2006); a third of European entrepreneurs should withdraw from their
businesses over the next ten years. As a result, these business transfers should concern an
annual average of 690.000 firms, affecting some 2.8 million employees. This radical increase
in expected business transfers in the coming years should particularly affect the numerous
family businesses, which form a key part of the European business community. This evidence
leads the European Commission to recommend measures to support business transfers in
order to avoid firms’ disappearances not because of a lack of competitiveness, but merely
because of obstacles in the tax and legal environment or because of the lack of successors.
Our research focuses upon this specific question of entry’s mode chosen by
entrepreneurs. More precisely, we seek to investigate the process by which an entrepreneur
decides to create a new venture or to buy an existing firm. This research belongs to the
growing literature on occupational choice. This work finds a strong support in Parker and Van
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Praag (2006) who develop the first theoretical and empirical model on mode of entry chosen
by entrepreneurs. Their model predicts how several individual and firm-specific
characteristics influence entrepreneurs’ mode of entry. In this model, ex nihilo new ventures
are associated with higher levels of schooling and wealth whereas managerial experience, new
venture start-up capital requirements and risks promote takeovers. Entrepreneurs whose
parents run a family firm should invest the least in schooling insofar as schooling reduces
search costs. Moreover, these entrepreneurs have the lowest probability of needing to search
for a business opportunity outside their family. An empirical investigation on a sample of
Dutch entrepreneurs provides broad support for this theory. In this model, which fits very
well with the case of family firms, starting up a new venture from scratch is only perceived as
an alternative to buying out an existing firm, in particular parents’ one : “ [...] entrepreneurs
who are unmatched with any firm are forced to give up on entrepreneurship [...]”.The focus is
the human capital associated with the choice of entry’s mode, and more precisely the number
of schooling years before becoming entrepreneurs by either taking over an established
business or starting a new venture.
Our focus differs from Parker and Van Praag (2006). We indeed consider the case of
an individual with given personal characteristics in a given environment who decides to
become self employed. This individual must choose a mode of entry. To really consider the
choice of an entrepreneur, we do not consider the cases of family transfers and Management
Buy Out (MBO). In these two cases, entrepreneurs do not really choose a mode of entry: they
reach an opportunity of buyout linked with either their family or the chance linked to their
past experience without having ever considered the option to create a new firm from the
scratch. Finally, we exclude all cases which concern the internal market of firms’ transfers
and we only focus on external transfers of firms we compare with ex nihilo starts of new
ventures.
Our study seeks to extend prior research in 3 ways. First, we underline the diversity of
entry’s modes for entrepreneurs in the French context. Second, we test, on a sample of
individual data; the lessons given by the theory of occupational choice on the way
entrepreneurs choose to enter: start up or buy out. Third, we look for the respective roles of
individuals’ characteristics, projects’ nature and sectorial determinants on individuals’ choice
of entry’s mode.
The paper is organized as follows. Section 2 defines determinants of entry’s modes. We
relate our analysis on the theory of occupational choice which explains why individuals
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become self-employed. Section 3 describes the data (the French Sine database produced by
the French National Institute of Statistical and Economic Studies), the variables we used and
the models that are used to distinguish the kind of entry. The empirical models are tested
from a sample of new firms (ex nihilo new firms or takeovers) which had been set up or taken
over in 1998. Section 4 discusses the results of the empirical models. The results reveal that
factors both at the micro and meso levels determine the way individuals become
entrepreneurs. These results are in accordance with main theoretical backgrounds. Section 5
concludes and summarizes the main findings.
2. The determinants of entry’s mode : theoretical backgrounds and testable
hypotheses
The economic theory has addressed the causes of entrepreneurship. At the micro level,
the theory of occupational choice identifies the driving forces behind becoming entrepreneurs.
This academic literature stresses the role of human capital (Cressy, 1996, Bates, 1997,
Greene, Rosen, 1994), social capital (Anderson, Miller, 2002, Anderson and Jack, 2002) and
financial capital (Evans, Jovanovic, 1989, Holtz-Eakin, Joulfaian, Rosen, 2000). Studies at the
meso level focus on market-specific factors such as profit opportunities and opportunities for
entry and exit (Carree, Thurik, 1996).
In this article, we derive from these studies what could drive the individual’s choice of a
specific mode of entry. More precisely, we identify factors that may explain why an
entrepreneur chooses to buy an existing firm rather than start a new venture. We derive from
this analysis testable hypotheses on determinants of entry’s mode by entrepreneurs.
2.1. Human and social capital
Human capital is defined as an investment in skills and knowledge that boots earning
power (Becker, 1964). Human capital refers in general to owners’ characteristics, in particular
education, workplace experience and family ties (Bates, 1997, Green, 2000). A high human
capital corresponds to a high degree level or/and a past experience close to the activity the
individual wants to develop. It is generally well admitted that human capital matters in
entrepreneurship. For example, Montgomery, Johnson and Faisal (2005) show that human
capital increases the probability of pursuing self-employment. Human capital matters above
all because of past experience. Research on education’s impact on entrepreneurship has been
indeed rather inconclusive. Some studies have found that better educated people are the most
likely to become entrepreneurs (Bates, 1997). However, in some others, education was
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negatively related to self-employment (Bulter, Herring, 1991). The effect of work experience
or entrepreneurial capital (Aldrich, Renzilli, Langton, 1998) is less controversial.
Entrepreneurial capital refers to experience and skills associated with business ownership and
managerial experience. Kim, Aldrich and Keister (2003) decompose the concept of work
experience into four components: general full-time work experience, managerial experience,
previous start-up, and current self-employment. Researchers suggest that all kinds of work
experience increase the likelihood of becoming entrepreneurs.
Human capital can influence the choice of entry’s mode by an individual too. For
Ucbasaram, Westead and Wright (2007), identifying new business opportunities requires
different abilities than managing business opportunities. According to these authors, a high
level of human capital permits to identify new markets or markets with high opportunities of
profit. The required abilities are high formal education and prior experiences which ensure
that entrepreneurs own “cognitive abilities” and better know where they must look for. As
business transfers concern most often and more probably than starts-up existing markets and
traditional activities, we expect to verify the following hypothesis.
H1: Entrepreneurs with high level of human capital (education and general full-time
work experience) are more inclined to start a new firm than to buy an existing one.
Buying an existing firm requires other form of human capital than formal education and
general full-time work experience. By nature, an existing firm is on average larger than a start
up. The number of salaries, shareholders, customers and providers are higher so that
entrepreneurs’ abilities in management can be expected to be a significant comparative
advantage when entrepreneurs choose to take over an existing firm. Furthermore,
entrepreneurial decision depends on two types of knowledge: market conditions and
managerial skills. Before entry, an individual does not know perfectly his own managerial
skills. In order to manage uncertainty, he can choose to penetrate the market with a smaller
size less than optimal. After entry, if he learns that market conditions or/and managerial
abilities are favourable, he can decide to make the firm grow to reach the optimal size
(Jovanovic, 1982).
An ex nihilo creation is on average smaller than an existing firm.
Consequently, when the agent does not know perfectly his managerial abilities, he can prefer
penetrating the market with an ex nihilo creation rather than with a takeover. The discussion
here suggests the following hypothesis.
H2: Entrepreneurs with higher managerial skills are more inclined to buy an existing
firm than to start a new one.
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This hypothesis is verified with data from Netherlands by Parker and Van Praag (2006).
The study of entrepreneurship does not focus only upon the individual. Since the 80s,
the importance of social capital to entrepreneurship has been widely recognized (for a review
of literature see Cope, Jack, Rose, 2007). Social capital can be defined as the entrepreneurial
quality of environment which is based on trust and networks. It involves the building of
networks and the norm of behaviour that underpin them (Putnam, 2000) and the goodwill that
is linked with social relations (Adler, Know, 2002). Social capital is indeed expected to
improve the entrepreneur’s human capital by enhancing the individual’s ability to identify
opportunities and to accede to resources (Green, Brown, 1997). Despite the concept of social
capital is intuitively very important, it is difficult to measure it empirically. However,
concerning the decision to become self-employed, we can notice, for instance, that
individuals’ own wealth is less important in becoming self-employed than parents’ wealth and
that having a self employed parent produce a substantial positive effect on becoming selfemployed (Dunn and Holtz-Eakin, 2000).
Social capital can influence the choice of entry’s mode by an individual too. To succeed
in taking over a good firm, an individual need information on opportunities. When he belongs
to entrepreneurial networks, his likelihood to access to this information, and consequently to
buy a “good” existing firm, is higher. This suggests the following hypothesis.
H3: Social capital increases the likelihood that an individual takes over an existing firm
rather than create an ex nihilo firm from the scratch..
2.2. Financial capital
Research on the impact of financial capital on new firm formation has generated mixed
results. On the one hand, studies stress the role of financial capital in becoming self-employed
and keeping it going (Bates, 1987, Evans and Jovanovic, 1989). In support of this view,
research has shown that obtaining money from an inheritance, gifts and lottery winning
increase the likelihood of self-employment (Holtz-Eakin, Joulfaian, Rosen, 1994 Lindh,
Ohlsson, 1996, Blanchflower, Oswald, 1998). On the other hand, some studies disagree with
this emphasis on financial resources (for example, Cressy, 1996). Kim, Aldrich and Keister
(2003) give two arguments to explain why financial capital should not be the preponderant
factors to determine entrepreneurship. First, many new businesses do not require large
amounts of financial capital. Second, small businesses can find ways around capital
constraints and build solutions to decrease capital needs in the start-up phase (Freear, Sohl,
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Wetzel, 2002). Kim, Aldrich and Keister (2003) give some examples such as relatives
working below market salary, borrowing from relatives or other firms, withholding the
owner’s salary, and leasing equipment rather than buying it.
The role of financial capital in determining entrepreneurship remains controversial.
However, some expectations can be stressed concerning the modes of entry.
First, takeovers should be less credit rationed, in the sense of Stiglitz and Weiss (1981),
than ex nihilo new firms because their informational system is less opaque. Takeovers can
exhibit track records that cannot be produced in the cases of start up which begin from
nothing. Moreover, takeovers should be less redlined, always in the sense of Stiglitz and
Weiss (1981), they are less risky than ex nihilo new firms. In consequence, access to credit
should be easier for takeovers than for new firms. If the internal wealth of entrepreneurs is
limited, for a fixed level of total finance needed to begin activity, an individual should prefer
choosing takeover than ex nihilo new firm to make financial constraints decrease.
Second, access to banking loans depend on initial capital entrepreneurs can provide in
his venture (Leland, Pyle, 1977). In average, a business takeover is more costly than a start-up
as not only physical assets are transferred but also relationships with all stakeholders, firm’s
reputation and future cash flows expectations too. If the entrepreneur’s wealth is low, his
capacity to access to banking loans should be low too so that the entrepreneur can choose the
least costly project which is an ex nihilo creation rather than a buyout.
Consequently, the discussion here suggests the following hypothesis.
H4: Constraints on the credit market make the likelihood of buyout increase and
banking loans are more often associated with buyouts than with ex nihilo new firms at a given
level of capital needed to begin activity.
2.3. Market-specific factors
Entrepreneurial decisions, and in particular the choice of entry’s mode, is not only made
at the individual micro level. These decisions depend also from factors at the meso level that
influence both the supply and the demand of entrepreneurships: “The supply and demand side
create conditions for the entrepreneurial decision made at the individual level. The demand
side creates entrepreneurial opportunities though the market demand for goods and services,
whereas the supply side provides potential entrepreneurs that can act upon the opportunities.”
(Verheul, Audrestch, Thurik, et alii, 2001, p. 19). Business environment drives the creation of
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new firms. In this article, we distinguish the effect of four factors: economic development,
characteristics of demand, characteristics of sector, and innovation by firms.
At the most aggregated level, economic development influences entrepreneurship but
the nature of this impact is ambiguous. On the one hand, economic development is
accompanied by a decrease in the rate of unemployment that may lower the supply of new
entrepreneurs but also favours new opportunities. On the other hand, a lower level of
prosperity makes the rate of unemployment increase. In this situation, the opportunity costs of
entrepreneurship decreases and the supply of new entrepreneurs tends to increase. However,
there are less new opportunities on demand side.
Economic development can influence the entrepreneur’s mode of entry. We can expect
that in periods of high economic growth, new opportunities of entry arise for firms and
individuals and the rates of entrepreneurs’ and firms’ exits decrease. Finally, in this
environment, “bad” entrepreneurs to remain active and the supply of takeovers can be
supposed to be rather rare whereas ex nihilo new firms are favoured. In periods of low
economic growth, “bad” firms should exit and “good” firms managed by “bad” entrepreneurs
too. In this situation, the supply of takeovers should increase. This discussion suggests the
following hypothesis.
H5: Prosperity favors rather ex nihilo creations whereas low growth rates favor
takeovers.
The characteristics of demand influence entrepreneurship too. In particular, since the
seventies, the demand of customers for proximity and more flexible productions allows
smaller firms to survive and can explain the structural increase in the rate of self-employment
(Piore, Sabel, 1984). This need of proximity, linked with some activities, can influence the
mode of entry chosen by entrepreneurs. When a business focuses on proximity, reputation,
clientele and localisation are strategic assets. These specific assets are difficult to build from
nothing. When an agent acquires an existing firm, he acquires them, or at least a part of them,
immediately. This suggests the following hypothesis:
H6: Local activity focussed upon proximity increases the probability of business
takeover.
The impact of sector on entrepreneurship is widely recognized. Manufacturing oriented
economies are accompanied with high scale production and weak self-employment rates
whereas services oriented economies are characterized by smaller firms, creating more
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opportunities for self-employment. The sector specialization can influence the mode of entry
too. The first argument is the entry barriers which imply a strong constraint on the choice of
entry’s mode and lead more probably to business takeovers. In sectors with rather high MES
(Minimum Efficiency Scale), it is more difficult to create an independent ex nihilo new firm
than in sectors with low MES and we can expect a higher rate of takeovers. On the opposite
side, in sectors where MES are low, for example services, the likelihoods of an individual to
create ex nihilo new firms and to succeed in this venture can be expected to be higher. The
industrial structure should certainly impact the entrepreneur’s mode of entry. However, the
limitations of the database we use do not permit to study this hypothesis.
Innovation is a determinant factor in explaining firms’ size. New flexible technologies
can indeed justify the decreasing minimum efficiency scale in many sectors (Carlsson, 1989)
and can justify the increase of self-employment in many countries. Since Williamson (1985)
and the development of the transactions cost approach, we know innovation needs flexibility
i.e. firms’ ability to change. By definition, an existing firm is less flexible than a new one. If
the entrepreneur wants to innovate, in particular in a new technological process, adjustment
costs should be very heavy to adapt in an existing firm, in particular in industrial sectors. As a
consequence, in order to innovate, an entrepreneur can prefer create a new firm than to take
over an old one. Furthermore, for innovative projects, the specificity of assets and uncertainty
are strong so that entrepreneurs can be incited to entry with a small size. This situation is
easier to reach when entrepreneurs create new firms than when they buy existing ones. For
these two reasons, we expect to verify the following hypothesis.
H7: Entrepreneurs with innovative projects are more inclined to start a new firm than to
buy an existing firm and the effect is stronger in industrial sector.
3. Methodology
In this section, we describe data, variables and methods we use.
3.1. Data
In this article, we use the SINE database produced by the French National Institute of
Statistical and Economic Studies (INSEE) in the framework of the New firms Information
System1 (SINE). In this survey, new firms are identified on the basis of their registration in
the “Système d'Informations et de Répertoire des Entreprises et des Etablissements"
1
For a complete presentation of the database, see Charpin (2004).
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(SIRENE). SIRENE gives information on their identity, localisation, economic activity and
size. The SINE database completes this information on the population of new firms. Data are
generated by surveys which have been driven on a representative population of new firms
every five years since 1994. These data give us qualitative information2 on the entrepreneur’s
profile, the project he develops, the conditions of their setting up, in particular the
characteristics of goods and services, the market and the financing, and the difficulties he
must cope with at the beginning of the venture. Firms in each cohort are followed during five
years. During this period, two surveys are carried out: the first three years after setting up and
the second five years after.
In this article, we study the cohort of firms which set up in 1998 and which had survived
at least for one month. This compulsory survey is carried out among the population of 52 000
new firms. The sample is representative of the total population of new firms which concern
2 666 447 firms. A weight variable is used for the sample to fit the entire population. Firms
belong to 9 sectors of activity; financial and agricultural activities and units established
abroad are not taken into account in this database.
In this study, we focus upon pure creations which correspond to new means of
production and takeovers when a firm purchase the whole or part of another firm's activity
and means of production. Consequently, the sample only focuses on pure creation and
excludes creation of new establishments or takeovers by existing firms (subsidiaries).
Transfers concerning the internal market of buyout are excluded too. We do not indeed take
into account firms taken over by their own employees, in particular managers, by family
members of firm’s owners (legacy or sales). All these situations do not really result from a
choice between ex nihilo new creation and takeover. Finally, our sample is composed from
20 344 units which represent 62 176 enterprises set up or taken over in 1998.
These individual data are completed with data at the sector level published by the
INSEE in the collection “Images Economiques des Entreprises”. More precisely, we introduce
data on the sector growth between 1996 and 1997 which can influence the entrepreneur’s
decision.
2
Entrepreneurs must answer yes or no to some questions or choose one or more options in a given list of
possible answers.
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3.2. Variables3
To test hypotheses, we have created a dummy variable called “CREATION”. This
binary variable is equal to 1 when the new firm has been set up from nothing and 0 when the
firm is a takeover. In our sample, creation represents 87.7 % of the population.
Explanatory variables are created to study the effect of human capital, social capital,
financial capital and market-specific factors.
To study the effects of human capital on the choice of entry's mode, we use several
dummy variables. We can class them in two groups: entrepreneur's private characteristics and
entrepreneur’s managerial skills.
Entrepreneur's characteristics are represented by six dummy variables. The variable
“ALCREATED” is equal to one when entrepreneurs declare that they already set up a firm
and 0 otherwise. According to us, an experience in setting up a firm makes the human capital
of the creator grow. However we must be careful in interpreting the results we can obtain with
this variable as some individual can be “serial” creators who prefer creating ex nihilo new
firms to taking other existing ones. In this specific case, entrepreneurs do not really make a
choice between ex nihilo new firms and takeovers; there is a kind of physiological bias toward
ex nihilo creation. In the sample, we can observe this bias. People that have already set up a
firm represent a higher proportion in the sub-sample of ex nihilo creations (about 23%) than
in the sub-sample of takeovers (19,25 %). The variable “EXPERIENCE” is equal to one when
individuals declare that they have experience in the same sector and 0 otherwise. Ceteris
paribus, such an experience makes individuals’ human capital increase. In our sample, 76.4 %
of ex nihilo creators declare that they have experience in the same sector. This experience is
only mentioned by 66.7% of individuals who choose takeovers as mode of entry. We
introduce three different variables to take into account diploma owned by entrepreneurs. Each
variable is equal to 1 when the entrepreneur has a degree and 0 otherwise. These variables are
called “CAPBEP”, “BAC” and “BACDEUXETPLUS”4 which correspond respectively to
technical undergraduate degrees, the baccalaureate and postgraduate diplomas. In our sample,
18.01 % of creators have not got any qualifications (19.47 % of individuals who choose
takeovers) whereas 30.48 % of them have a postgraduate degree (and only 19.76 % of
individuals who choose takeovers).
3
4
Description of variable are given in appendix 1.
For these three variables, the reference is when entrepreneur has no degree.
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Managerial skills are an important part of human capital. In the survey, entrepreneurs
are questioned about their acquisition of managerial skills during their career. The variable
“MANAGERIAL” is equal to 1 when individuals answer that they acquired managerial skills
in their past activity and 0 otherwise. In our sample, proportion of individuals with managerial
skills is not very different between creators and individuals who decide to take over an
existing firm (respectively 21.96% and 22.75 %). Managerial skills can also depend on
diploma. The likelihood to experience in the past managerial activities is certainly higher for
post graduated individuals than for the others. For example, in our sample, post graduated
individuals (“BACDEUXETPLUS”) represent 29.16 % of the global population but 36.33 %
of entrepreneurs that declared to have acquired managerial skills during their past experience.
Social capital is measured by two dummy variables, “NETWORK” and “RELATION”.
The variable “NETWORK” is equal to 1 when the entrepreneur belongs to an entrepreneurial
network and to 0 otherwise. Individuals inscribed in such a network can access more easily to
information and so be aware of good opportunities of new ventures. In our sample, 70% of
creators feel to belong to an entrepreneurial network (64.25 % of the takeover subsample).
The variable “RELATION” is equal to 1 when starting the business is favoured by
relationships with customers or suppliers. In this case, social capital is generated by
relationships inside professional networks. This variable can also illustrate the favourable
effect of a stable environment on ex nihilo creation. Strong relationships with commercial
partners are mentioned by 47.11 % of ex nihilo creation against 28.25% of individuals who
choose takeovers.
To analyse the impact of financial capital on the choice of entry’s mode, we introduce
two variables. First, we take into account the access of new firms to baking loans for a given
level of financial needs. Seven dummy variables called “LOANSi with i=1,...,7” are created.
These variables are equal to 1 when entrepreneurs receive a banking loan and when their
financial needs belongs to one of seven bracket (the first is less than 1 500 euros and the last
is more than 76 000 euros) and 0 otherwise. We cross these two variables because what we
want to take into account banking constraint at the start up of the firm. Firms that need a high
level of financial resources and that are not financed by banks are probably more constrained
than firms with low financial needs that are financed by banks. Second, we introduce the
variable “PB_TRES” to take into account financial difficulties the entrepreneurs expect to
suffer from at the beginning of the venture.
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Market-specific factors correspond to the growth of the sector, the innovation of the
firm and its localisation. The growth rate of each sector’s turnover between 1996 and 1997 is
reckoned. We use a subdivision of the economy in about 700 sectors. We only retain the
period 1996-1997 because of the lack of available data. The variable is called “GROWTH”.
To take into account innovation, we introduce the variable INNOVATION that is equal to 1
when the entrepreneur declares that he sells a new product or he uses a new production
process and 0 otherwise. It is an imperfect proxy because of the subjectivity of the answer
given by entrepreneurs. We catch only the perception of innovation by individuals. In our
sample, 16.6 % of entrepreneurs who choose ex nihilo new firms creators and 6.29 % of
individuals who choose to take over declare to be innovative. To take into account the
characteristics of firm's market, we introduce two variables. The variable “LOCALE” is equal
to 1 when customers are geographically close to new firms (not regional neither national nor
international). The variable “SUBCONTRACTING” is equal to 1 when the enterprise has
declared that subcontracting is a significant part of its turnover.
To finish, variables of control are introduced.
They reflect some entrepreneur's and market’s characteristics. First, the nationality of
entrepreneurs is taken into account because it can contribute to create a network or because
some nationalities are more frequent in some sectors where ex nihilo creations or takeovers
are more frequent. We so create the variable “FRENCH” which is equal to 1 when the
entrepreneur is French and 0 otherwise. Second, we take into account the entrepreneur’s
gender because men or women can be more present in some sectors. The variable “MALE” is
equal to 1 when the entrepreneur is a man and 0 otherwise.
We introduce dummy variables that take the value 1 according to the activity sector of
the firm. We expect indeed that sector's structure can favour a specific kind of entry (set up or
take over). For example, in some sectors, the large number of small firms facilitates the
takeovers of existing enterprises. Some sectors are also more innovative sectors than others
and set up are used to be more frequent in these sectors. This variable doesn't affect directly
the choice between set up or take over. It influences the probability to create or take over in
particular according to the occupational qualification of the entrepreneur which can be
sectors-dependent.
These
sector
variables
are
“AGRIBUSINESS”,
“INDUSTRY”,
“BUILDING”, “TRANSPORTATION”, “ESTATE” (real estate), “PRIVATE” (private
persons services), “ENTERPRISES” (enterprises services) and “EDUCATION” (the
reference variable is called “TRADE” and corresponds to trade sector).
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3.3 Methods
Given the nature of dependent variable, logit and probit models are used to test
hypotheses. In order to control the influence of sectors on interest variables, we conduct
estimation for each of them. When the predictors have not the same significant effect (sign) in
each sector, we introduce interactions variables (sector*interest dummy variables) in the
global regression. The comparisons of the estimates yielded by logit and probit models show
that the signs of coefficients are the same across models and that the same variables are
statically significant. Consequently, we only present the logit model results of the likelihood
of choosing an ex nihilo creation (Appendix 2).
4. Empirical results
The appendix 2 presents our results and allows us to test directly hypotheses on
successively human and social capital, financial capital, and market-specific factors. To
finish, we comment results on control variables.
4.1. Human and social capital
Human capital appears to influence the mode of entry. However results appear to be
sector-dependent for variables which represent formal education (“BAC”, “BACDEUXET
PLUS” and “CAP/BEP”), prior experiences in the activity or a close activity
(“EXPERIENCE”) and business ownership experience (“ALCREATED”). In the base sector
(the trade sector), high formal education and experience increase the probability to create a
new firm5. This result is consistent with the first assumption. Nevertheless, if the interaction
variables are taken into account, the impact of “EXPERIENCE” on the probability of an ex
nihilo creation is reduced in agribusiness, real-estate, private persons and enterprises services,
and education and health sectors (the base category is the trade sector). It is reinforced in the
other sectors. The consideration of the simultaneous presence of prior business ownership
experience (“ALCREATED”) and sector attenuates, for all sectors, the individual effect of
prior ownership comparatively to the base category (trade sector). The effect of education is
different across sectors. The probability of ex nihilo creation for postgraduates tends to be
higher than for the base category with no diploma6 in trade sector. In this sector, there is no
difference between technical diploma and no diploma. As specialized degrees are compulsory
The coefficient of “ALCREATED”, “EXPERIENCE”, “BAC and BACDEUXETPLUS” are positives and
statically significant.
6
The coefficient of “BAC and BACDEUXETPLUS” are positives and statically significant.
5
Bastié/Cieply/Cussy
Page 14
or at least advisable necessary, for many occupations, the impact of education is not the same
across sectors. For example, in comparison with base category (no diploma), technical
diplomas have a positive and significant impact in agribusiness, industry and transportation.
This impact is positive in private person services and trade sector and no significant
otherwise.
Finally, the impact of prior experience in business or close activity and formal education
is different according to the sectors. As a consequence, a high level of human capital
(education and general full-time work experience) does not increase the probability to start a
new firm. The hypothesis 1 is not confirmed.
Contrary to other human capital variables, the influence of managerial skills appears
identical for all activity sectors. Managerial skills make the probability to create a new firm
from nothing decrease. The hypothesis 2 is confirmed. The result is either the same or no
significant among sectors. The effect of managerial skills on mode of entry is unambiguously
what is consistent with Parker and Van Praag (2006).
Social capital appears to affect the mode of entry. The two considered variables,
“NETWORK” and “RELATION” have significant positive effect on the likelihood of ex
nihilo creation. An entrepreneur with family business has a greater probability to create a new
firm than the others. Strong relationships with customers and/or suppliers have the same
effect. This result is stable according to the sector except for the variable RELATION which
has a significant negative sign in the sector of services for enterprise. Social capital does not
increase the probability to buy an existing firm. These results are inconsistent with
hypothesis 3. Two kinds of reasons can justify this result: the limits of data to measure the
social capital and a “false” hypothesis. First, the difficulties to define social capital (Anderson
and Miller, 2002), to measure it and the lack of available data lead to use bad proxies. The
social network is deeper than family network and strong relationship with customers and
suppliers. Second, social capital improves the search of information. Entrepreneurs with a
high social capital have a better knowledge on the market of business transfers (argument for
hypothesis 3), but perhaps a better knowledge on opportunities to start an ex nihilo new firms
too. Ucbasaram, Westehead and Wright (2007) introduce this last effect but their empirical
study does not confirm it. These two effects, better knowledge on business transfers and
better identification of new opportunities, have an opposed impact on the probability to create
an ex nihilo new firms.
Bastié/Cieply/Cussy
Page 15
4.2. Financial capital
Banking loans appear to affect negatively the probability of entrepreneurs to create new
firms from nothing whatever may be both the level of initial capital and the sector. Banking
loans are indeed more often associated with buyouts than ex nihilo creation. The hypothesis 4
is confirmed.
This result can be justified by at least two different explanations. First, the lack of initial
wealth leads the entrepreneur to gather external capital such as, in particular, banking loans.
Because, business takeovers, ceteris paribus, are less risky and opaque than ex nihilo
creations, business takeovers are less credit constrained than ex nihilo new firms. Second, the
entrepreneur must finance his venture with personal equity for a significant part. In this case,
banking loans signal an initial personal wealth and business takeovers are less financial
constrained than ex nihilo creator. This second explanation is confirmed by the estimation.
The ex nihilo creators anticipate more often financial problems (we consider that this
anticipation is a proxy of a lack of initial wealth).
4.3. Market-specific factors
The probability of ex nihilo creation increases with the average growth rate of turnover
of the sector between 1996 and 1997. This result is consistent with hypothesis 5 which
declares that good economic environment favors ex nihilo creation. Nevertheless, this
indicator is not very reliable variable because first, the nomenclature used (NAF 700) is not
sufficiently detailed and second, pertinent growth rate must take into account the regional
place of the creation. In fact, the family situation and other factors make the creator not
perfectly mobile.
The proximity activity is favorable to business takeover for all sectors. This result can
be explained by an important reputation effect in this type of activity which is beneficial to
new entrepreneur (at least at beginning of activity) in the case of takeovers. Local activity
affects positively the probability of business takeover. Consequently, hypothesis 6 is
confirmed.
The innovation of product and process increases in all sectors the probability of ex
nihilo creation. This result is consistent with hypothesis 7. We can notice the particular case
of real estate sector. In this sector, innovations (39 observations upon 773) predict perfectly
the ex nihilo creation. We are careful of the link between formal capital and innovation. In a
Bastié/Cieply/Cussy
Page 16
first approach we have realized estimation for each level of diploma. The sign of innovations’
impact and his significance are the same for all diplomas7.
Finally, all sectors appear to affect significantly the mode of entry comparatively to
the base sector. However this result is difficult to be interpreted at his level of details.
4.4. Control variables
We find also interesting effects for some variables of control.
First, the subcontracting activity is positively linked with ex nihilo creation. This
results can be explained by some strategies of spin-off or/an externalization some existing
firms can drive. In these cases, ex nihilo new firms are created by an employee of the existing
firm and new firms benefits form preferential relationships with the existing firms.
Second, the entrepreneur that creates alone choices more probably than others ex
nihilo creation. Perhaps, single creators are less risk averse than married ones and we know
that ex nihilo creations have a greater probability to fail. Two alternative reasons can justify
this result. On the one hand, each member of couples can be committed in other activities so
that the risk of the couple is diversified. On the other hand, when the creator is single, his
family expenses are lower. This primitive interpretation requires more empirical support.
Third, the entrepreneur’s nationality influences his mode of entry. The French
nationality increases the probability to create a new firm. An explanation can be the higher
propensity of French to discover new business opportunities. We can indeed assume that
French natives have a better knowledge on French markets than foreigners.
5- Conclusion
In this paper, we focus on the determinants of entry’s mode. We consider two
categories of determinants: individuals characteristic and market specific factors. Our
estimates of entry’s mode appear to be broadly consistent with our hypothesis concerning
managerial skills, financial capital and market specific factors. According to Parker and Van
Praag (2006), the managerial skills appear to improve the probability to takeover a firm. On
the contrary, innovation increases the likelihood of an ex nihilo creation. In other words, the
best managers and the less innovative ones take over existing firms. This result is consistent
to Holmes and Schmitz (1990). The skills necessary to manage efficiently a firm are not
identical to those necessary to identify new opportunities. Another interesting result is that
7
We drive a bivariate probit model on innovation too where the independent variables are the level of education
and the sectors. The results for mode of entry are qualitatively the same than for the univariate model.
Bastié/Cieply/Cussy
Page 17
takeovers have always a greater probability to be financed by banking loans than ex nihilo
creators whatever may be the level of required capital to start the business.
The model developed by Parker and Van Praag (2006) predicts than more educated
people are more likelihood to start a new firm than others. Their empirical results provide a
support for this proposition. Nevertheless, in our empirical framework, the effects of
education are not obvious. It depends on sectors. Globally, the characteristics of market (rate
of growth, characteristics on demand) affect the mode of entry.
According to Parker and Van Praag (2006), the studies upon entrepreneurship should
take into account the mode of entry. An individual doesn’t choice only to become self
employed but also the entry’s mode. This last choice depends on different personal and
environmental characteristics. In this situation, the conflation of the two modes can lead to
overvalue or undervalue the impact of human capital or others variables. The differentiation
of entry’s modes can prevail against this misinterpretation and can improve the efficiency of
public program.
Finally, future research should analyze the pure impact of entry’s mode on survival
and performance. An important question is to understand why business takeovers survive
longer than ex nihilo creations. This fact can be explained by a lower level of risk and a lower
degree of uncertainty for takeovers rather than for ex nihilo creations. Other explanations can
be advanced too. In particular, we can expect that the best managers more probably choose
business transfers. Survival and modes of entry can indeed depend on the same unobserved
variables and the treatment of this endogeneity should finally improve the knowledge of new
firm development and performance.
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Page 21
Appendix 1: description of variables
Size of population: 62 176 new firms (20 344 without any weighting) and among them 54 519 ex nihilo new
firms (17 103 without weighting)
Variable
Créations
Reprises
Ensemble
ALCREATED
22,99
19,25
22,53
EXPERIENCE
76,4
66,72
75,21
NODIPLOMA
18,01
19,47
18,19
CAPBEP
33,53
41,99
34,57
BAC
17,98
18,78
18,08
BAC_DEUXETPLUS
30,48
19,76
29,16
MANAGERIAL
21,96
22,75
22,06
CIRCLE
70,00
64,25
66,29
RELATION
47,11
28,25
44,79
LOANS1
0,80
2,52
1,01
LOANS2
1,75
5,34
2,19
LOANS3
2,62
5,67
2,99
LOANS4
6,25
11,69
6,92
LOANS5
5,63
14,39
6,71
LOANS6
2,76
11,24
3,80
LOANS7
2,63
15,46
4,21
INNOVATION
16,6
6,29
15,33
GROWTH (mean)
0,0216
0,0067
0,0184
FRENCH
89,44
91,25
89,66
AGE (mean)
36,88
37,26
36,92
MALE
72,99
61,8
71,61
CASH
15,4
11,06
14,87
CUSTOMER
36,85
6,27
33,08
LOCALE
46,65
78,8
50,61
SUBCONTRACTING
32,44
9,14
29,57
ALONE
70,29
60,98
69,14
AGRIBUSINESS
1,10
7,00
1,82
INDUSTRY
7,54
2,93
6,97
BUILDING
17,36
2,32
15,51
TRADE
30,05
32,4
30,34
TRANSPORTATION
4,15
4,00
4,14
ESTATE
3,35
0,73
3,03
PRIVATE PERSON SERVICES
11,13
40,28
14,72
ENTERPRISES SERVICES
20,91
3,46
18,76
EDUCATION
4,41
6,88
Unless otherwise specified, perrcentage of population for which variable is equal to 1.
Bastié/Cieply/Cussy
4,72
Page 22
Appendix 2: logit model results
Table 2.1: Explanatory variables
ALCREATED
0.3770729***
LOANS3
-1.451408***
EXPERIENCE
0.2828798***
LOANS4
-1.23532***
CAPBEP
-0.045873
LOANS5
-1.506529***
BAC
0.2938081***
LOANS6
-1.796834***
BAC_DEUXETPLUS
0.2871977***
LOANS7
-2.097057***
MANAGERIAL
-0.287367***
PB_TRES
0.4775113***
CIRCLE
0.2391295***
INNOVATION
1.060725***
LOCALE
-0.8132939***
GROWTH
0.188679***
RELATION
0.4166225***
SUBCONTRACTING
0.4356146***
LOANS1
-1.582178***
ALONE
0.4445279***
LOANS2
-1.578085***
Table 2.2: Control variables
FRENCH
0.0779664
TRANSPORTATION
0.8031539***
MALE
0.0871771***
ESTATE
4.176163***
AGRIBUSINESS
-0.215925
ENTERPRISES
1.969702***
INDUSTRY
1.732214***
PRIVATE
-0.8915931***
BUILDING
2.65184***
EDUCATION
0.5685019
Table 2.3: Cross variables
ALREADY*sector
CAPBEP*sector
AGRIBUSINESS
-0.1150729
AGRIBUSINESS
-0.6439507***
INDUSTRY
-1.297854***
INDUSTRY
-0.7871254***
BUILDING
-1.546099***
BUILDING
-0.1011799
TRANSPORTATION
-0.5914743***
TRANSPORTATION
-0.7746025***
ESTATE
-0.4784773
ESTATE
-0.9530055
ENTERPRISES
1.152951***
**
ENTERPRISES
-0.3801185
PRIVATE
-0.4137093***
PRIVATE
0.2936796***
EDUCATION
-0.1827055
EDUCATION
-.6855045*
BAC*sector
BACDEUXETPLUS*sector
-0.742403
***
AGRIBUSINESS
-0.4278265
INDUSTRY
-1.172425
***
INDUSTRY
-0.791302**
BUILDING
-.8057382***
BUILDING
-2.077031***
TRANSPORTATION
-1.761666***
TRANPORTATION
-1.809796***
ESTATE
-2.000488*
ESTATE
-2.021203***
ENTERPRISES
-0.2599051
ENTERPRISES
.0624883
PRIVATE
0.1950619*
PRIVATE
0.6476399***
AGRIBUSINESS
Bastié/Cieply/Cussy
Page 23
EDUCATION
-0.4241082
EDUCATION
EXPERIENCE*sector
-0.2863553
NETWORK*sector
AGRIBUSINESS
-0.7877077***
AGRIBUSINESS
.2132434
INDUSTRY
0.071993
INDUSTRY
-0.1466436
BUILDING
0.0538494
BUILDING
-.0968801
TRANSPORTATION
0.3028057**
***
TRANSPORTATION
0.4953058
ESTATE
-1.058038***
ESTATE
0.1126519
ENTERPRISES
-0.626828***
ENTERPRISES
-0.6354715***
PRIVATE
-0.2408641***
PRIVATE
-0.0813618
EDUCATION
0.3631236
EDUCATION
ALREADY*EXPERIENCE
-0.7403914
***
0.2710685
*** significant at 1 %, ** significant at 5 %, * significant at 1 % elsewhere not significatively
different from zero
Quality of regression
Pseudo R2 : 0.3080
LR chi2(80) = 14291.83 (Prob > chi2 = 0.0000)
Classification table
Classified + if predicted Pr(C) >= .5
True C defined as CREATION != 0 (NC elsewhere)
Sensitivity
Pr( + | C )
97.32%
Specificity
Pr( - | NC )
32.69%
Positive predictive value
Pr( C | + )
91.15%
Negative predictive value
Pr(NC | - )
63.16%
False + rate for true NC
Pr( + | NC )
67.31%
False - rate for true C
Pr( - | C )
2.68%
False + rate for classified +
Pr( NC | + )
8.85%
False - rate for classified -
Pr( C | - )
Correctly classified
Bastié/Cieply/Cussy
36.84%
89.36%
Page 24