Faculty of Economics and Business
The effect of risk taking propensity
on employee-growth-ambition
Eline Out - 5927579
Abstract
The focus of this paper is on small firms, and their owner’s ambition to hire external labour.
The factors that influence ambition and realization to hire (additional) employees are
separately investigated for entrepreneurs without employees (Own-Account Workers) and
with employees (Employers). Data is collected by means of a survey among entrepreneurs in
a cooperation between Bureau Onderzoek en Statistiek Amsterdam and the Amsterdam
Center for Entrepreneurship. As expected, general risk taking propensity has a positive and
significant influence on achieving and having employee-growth-ambition for both OwnAccount Workers and Employers. General risk taking propensity does not exclusively
influence employee-growth-ambition but also influences other entrepreneurial ambitions. The
perception of barriers to employee-growth-ambition differs between Own-Account Workers
and Employers, and between entrepreneurs with and without employee-growth-ambition.
Master Thesis
09-08-2013
Supervisor: Prof. Dr. C.M. van Praag
Master in Business Economics, specialization Organization Economics
2
Preface
I would like to thank my supervisor prof. dr. Mirjam van Praag for her guidance during my
thesis process. Furthermore I would like to thank Bureau Onderzoek en Statistiek Amsterdam
for providing me the opportunity to cooperate with them. In special I would like to thank
Carine van Oosteren, Sanna de Ruiter and Ilona Vierveijzer for their overall support and for
their help in designing the survey, their constructive comments were very useful.
3
Table of Contents
1
Introduction………………………………………………………………..……….…..5
2
Related literature………………………..………………………………….…………..7
2.1 Heterogeneity………...…………………………………………………………….7
2.2 Factors influencing the decision to hire employees……………...……………...…8
2.2.1 Risk taking propensity…..………………………………………………..9
2.2.2 Owner-manager motivation………………………………….….………11
2.2.3 Financial capital…..……………………………………………….…….11
2.2.4 Human capital…………………………………………………...………12
2.2.5 Miscellaneous factors...…………………………………………………13
2.3 Barriers to (employee-)growth………………………………….………………...14
2.4 Concluding remarks from the literature……………………………….……..…...15
3
Research methodology ……………………...……..…………………………………16
3.1 Data………...……………………………...……………………………………...16
3.2 Measures.…...……………………………...…………………………………...…17
3.2.1 Dependent variables…………………………………………………….17
3.2.2 Main independent variable…………………………...……...………….18
3.2.3 Control variables……………………….…………………………...…..18
3.2.4 Reasons for employee growth and barriers…………….……………….20
3.2.5 Entrepreneurial ambitions………………………………………………21
3.3 Estimation method………………………………………………………………...21
4
Results and analysis……………………...……..…………………………………….22
4.1 Descriptive statistics..……………………...……………………………………...22
4.2 Factors influencing EGA………………...………………………………………..26
4.2.1 Entrepreneurial stage……………………………………………………26
4.2.2 Having employees…………………………………………………...….28
4.2.3 Ambition to become Employer…………………………………………29
4.2.4 Ambition to hire additional employees…………………………………30
4.3 Exclusivity check.………………………………………………………………...32
4.4 Reasons (not) to hire employees………………………………………………….33
4
Table of Contents (continued)
4.4.1 Reasons to hire employees……………………………………………...33
4.4.2 Barriers to hiring employees……………………………………………37
5
Discussion and limitations……………………………………………………………41
6
Conclusion……………………………………………………………………………43
References…………………………………………………………………………….45
Appendix I - Tables………………………………………………………………...…49
Appendix II - Survey……………………………………………………………….…62
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1
Introduction
Given the focus in the economy on growth, it seems almost natural to assume that every
entrepreneur has a desire to let his business grow bigger. Entrepreneurs are considered to be
engines of economic growth and coherently creators of jobs (Minniti, 2008; Carree et al.,
2002; Audretsch and Thurik, 2001). However, although the number of entrepreneurs in the
Netherlands has increased from 661.000 to nearly 1.1 million during 1996-2011, the increase
was mainly caused by a rise of self-employed without employees (CBS, 2012; CBS, 2009). A
CPB study in 2012 forecasts a further increase of self-employed without personnel in the
future (CPB, 2012). The increase of self-employed without employees suggests that not every
entrepreneur has ambition to hire (additional) employees. This leads to the following research
question: What determines the ambition and realization of an entrepreneur to hire
(additional) external labour and does this differ for own-account workers and employers? In
this paper the terms ‘entrepreneur’ and ‘self-employed’ are alternately used. Both terms
capture the same meaning, namely (co-)owner of an enterprise. Self-employed are subdivided
in a similar means as Earle and Sakova (2000): entrepreneurs who currently have no
employees are referred to as ‘Own-Account Workers’ (OAW) and entrepreneurs who
currently have one or more employees are referred to as ‘Employers’.
Firstly, investigating the existence of employee-growth-ambition (EGA) among
entrepreneurs is useful for policy-makers. For example for calculation of employment
predictions. Furthermore it is useful to know whether barriers to employee growth are
consisting of individual preferences or external factors which potentially can be altered.
Secondly, the investigation is also relevant for future entrepreneurship research.
Entrepreneurs are often, for simplicity, defined as ‘individuals who are self-employed’
(Román et al., 2013). Thereby not making a difference between OAW and Employers, nor
making a difference between self-employed who have ambition to grow and those who do not
have such an ambition. Therefore, there exists heterogeneity in the people regarded as
entrepreneurs, possibly causing misleading research results and conclusions (Henrekson and
Sanandaji, 2013). For instance, past studies on personality differences between entrepreneurs
and wage-employees find little and mixed results.1 Perhaps this lack of distinguishable
features is partly caused by heterogeneity in the group of entrepreneurs. Van Es and van
Vuuren (2011, p. 1666) furthermore point out the issue of ‘false’ self-employment caused by
1
See for example: Brown et al. (2011), Beugelsdijk and Noorderhaven (2005), Miner and Raju (2004) and
Blanchflower and Oswald (1998).
6
OAW who behave like employees, in the sense that they carry out work that can also be done
by a ‘normal’ employee. It remains highly speculative, but if only entrepreneurs with growthambitions are considered as ‘true entrepreneurs’, differences might be found between wageworkers and these true entrepreneurs.
In order to investigate the factors that can help explain differences in ambition (and
realization) to hire (additional) external labor, a survey is designed and online distributed
among entrepreneurs by bureau of Onderzoek en Statistiek Amsterdam2 (O+S) together with
The Amsterdam Center for Entrepreneurship (ACE). In total 833 responses are used,
concerning 536 responses from OAW and 297 responses from Employers. The focus of the
paper is on small enterprises, the largest firm in the dataset employed 125 employees, the
average (median) number of employees was 3.3 (0) for all firms and 9.3 (4) for all firms given
they had at least one employee. My contribution to the literature consists of getting an insight
in the relationship between risk attitude and EGA, which is not yet covered in the current
literature. Moreover my research is a valuable contribution to policy-makers by giving them
insights into barriers to employee growth. As expected, the results indicate that general risk
taking propensity has a positive and significant influence on achieving and having EGA for
both OAW and Employers. The results also confirm that self-employed are a heterogeneous
group, since only 16% of the responding OAW has EGA, while 62% of the Employers aims
to hire an additional employee. In addition the marginal influence of risk taking propensity
differs between OAW and Employers, and there are also differences in other factors that are
of influence. However, general risk taking attitude is not exclusively influencing EGA. The
results indicate that for both OAW and Employers, general risk taking propensity has a
positive and significant influence on ambition to increase turnover in the next five years by
hiring other self-employed and/ or cooperate with other self-employed for the fulfilling of
projects. Additionally the results indicate that for Employers general risk taking propensity
has a significant positive influence on ambition to increase profits within the next five years.
There is thus an overall correlation between risk taking propensity and ambition.
The paper is organized as follows. Paragraph 2 discusses relevant insights from the
literature. Thereafter paragraph 3 contains information on the set up of the empirical research.
Paragraph 4 contains the results and analysis of the data. Paragraph 5 contains a discussion of
the results, limitations and suggestions for future research. At last paragraph 6 contains the
conclusion.
2
Translated to English: Bureau Research and Statistics Amsterdam.
7
2
Related literature
This section gives an overview of relevant insights from the literature on entrepreneurship. In
order to understand the importance of distinguishing between different categories of selfemployed, paragraph 2.1 discusses literature on heterogeneity among self-employed. There is
more research on factors influencing realization rather than the ambition of hiring (additional)
employees. Paragraph 2.2 reviews both types in order to determine which variables should be
included in the empirical analysis. Thereafter paragraph 2.3 discusses literature on barriers to
employee-growth. Finally paragraph 2.4 contains concluding remarks.
2.1 Heterogeneity
In empirical research entrepreneurs are for simplicity often defined as self-employed.
However, a large number of scholars recognize that self-employment is a biased proxy for
entrepreneurship, since individuals who are included in the self-employment category differ
widely on all kind of features (Henrekson and Sanandaji, 2013; Román et al., 2013; Levine
and Rubinstein, 2012; Cowling et al., 2004; Gundry and Welsch, 2001; Earle and Sakova,
2000; Stewart et al., 1999; Carland et al., 1984). According to Henrekson and Sanandaji
(2013), using self-employment to proxy for entrepreneurship often leads to misleading
inferences. As a consequence of the recognition that entrepreneurs are a heterogeneous group,
scholars distinguish between different categories of self-employed. Earle and Sakova (2000)
for example distinguish between ‘Employers’, who create jobs for others, and ‘Own-Account
Workers’ (OAW), who work on their own or with the support only of unpaid family helpers
(2000, p. 576). Their empirical research in East-European transition economics shows that
Employers differ from OAW on features such as earnings, family background and schooling.
Empirical results by Román et al. (2013), based on data from the European Community
Household Panel for the EU-15, confirm the notion that self-employed should be considered
as a heterogeneous group. Cowling et al. (2004) use different terminology to make a similar
distinction as Earle and Sakova (2000). Cowling et al. (2004) label self-employment with
employees as ‘job-creating self-employed’ and those without employees as ‘individual selfemployment’. Cowling et al. (2004) argue that in entrepreneurship research, both groups
should be treated as related, but distinct groups.
Distinctions are also made regarding differences in growth-orientations of
entrepreneurs, or in different terms, ambition levels. Carland et al. (1984) for example
8
distinguish between ‘small business owners’ and ‘entrepreneurs’. Small business owners can
be interpreted as establishers and managers of a business for the principal purpose of
furthering personal goals. While entrepreneurs are driven by innovation, profit and growth
(Carland et al., 1984). Stewart et al. (1999) empirically find that self-employed who are
labeled as entrepreneurs score significantly higher in achievement motivation, risk taking
propensity and preference for innovation compared to both corporate managers and small
business owners. While entrepreneurs were found to differ significantly from corporate
managers, the only different feature between small business owners and corporate managers
appeared to be a higher propensity to take risks among small business owners (Stewart et al.,
1999). Gundry and Welsch (2001) investigate differences between high-growth-oriented
(‘ambitious’) and low-growth-oriented (‘status quo’) entrepreneurs. Their empirical findings
indicate that ambitious entrepreneurs indeed differed from status quo entrepreneurs among
several dimensions. Overall, ambitious entrepreneurs were found to have a more structured
approach to organizing their business (Gundry and Welsch, 2001). Stam et al. (2007) found
that entrepreneurs who are ambitious regarding expected firm growth, contribute more
strongly to macro-economic growth than entrepreneurial activity in general.
Alternatively Levine and Rubinstein (2012) distinguish between incorporated and
unincorporated self-employed. The results of their empirical study in the USA indicate that
incorporated self-employed have distinct (non)cognitive traits. Incorporated self-employed are
more educated, score higher on learning aptitude tests, exhibit greater self-esteem and engage
in more aggressive, illicit and risk-taking activities.
In summary, empirical evidence shows that self-employed are a heterogeneous group
and should be treated as such in empirical research.
2.2 Factors influencing the decision to hire employees
The decision process regarding hiring an (additional) employee is complex and influenced by
multiple factors. First the factor of most interest for this study is discussed, the personality
trait ‘risk taking propensity’. Thereafter the influence of owner-manager motivation, financial
capital, human capital and miscellaneous factors are discussed.
9
2.2.1 Risk taking propensity
Many scholars attempted to identify personality traits of successful entrepreneurs, however in
their literature review Driessen and Zwart (2007) come to the conclusion that results are
mixed. Nevertheless they conclude that it seems that on average successful entrepreneurs,
compared to individuals who do not own a (successful) business, have a higher score on the
following traits: need for achievement, risk taking propensity, internal locus of control, need
for autonomy, need for power, tolerance of ambiguity, need for affiliation and endurance. In
my empirical research only the trait ‘risk taking propensity’ is taken into account, since most
other traits require a more extensive measurement process.3 Zhao et al. (2010) conclude in a
meta-analysis that risk propensity is the best predictor of entrepreneurial intentions among the
traits that they investigated.
Risk taking propensity can be defined as ‘the perceived probability of receiving the
rewards associated with the success of a proposed situation’ (Brockhaus, 1980, p. 513). The
view of entrepreneurs as risk takers dates back to Cantillon (1755). He distinguished between
‘undertakers’ (entrepreneurs) who took the risk of demand and price uncertainty, and ‘hired
people’, who earned a fixed wage. Knight (1921) evolved the risk taking theory by separating
uncertainty from risk. Risk concerns ‘recurring events whose relatively frequency can be
known from past experience’, while uncertainty is caused by ‘unique events which can only
be subjectively estimated’ (Wu and Knott, 2006, p. 1316). Wu and Knott (2006) explain that
the role of entrepreneur as bearer of risk has led to the inference in most theoretical literature4
that entrepreneurs have greater risk tolerance than wage earners. Yet empirical research on the
difference in risk tolerance between entrepreneurs and wage-workers has generated mixed
results.5 Hsieh et al. (2011) propose an opposing theory, by arguing that risk aversion
encourages individuals to invest in balanced skill profiles, which makes them more likely to
become entrepreneurs. Empirical data on Dutch university graduates provides evidence for
this theory.
To date little research has been conducted to differences in risk attitude within the
group of entrepreneurs. Block et al. (2009) performed an empirical study on the risk attitude
of different types of entrepreneurs in Germany. Firstly Block et al. (2009) investigate whether
3
An example of a valid measurement method is the Entrepreneur Scan developed by Driessen and Zwart, which
measures necessary traits and capabilities for entrepreneurship (Driessen and Zwart, 2007).
4
Such as: Kanbur (1979), Kihlstrom and Laffont (1979), Lucas (1978) and McClelland (1961).
5
See for example: Brown et al. (2011), Caliendo et al. (2009), Miner and Raju (2004), Stewart et al. (1999) and
Brockhaus (1980).
10
the risk attitude of necessity and opportunity entrepreneurs differs. Secondly the authors
investigate the relationship between work motivation and risk attitude. Work motivation is
measured by three dimensions: motivation by creativity, motivation by independence and
motivation by income. The results indicate that necessity entrepreneurs have a lower risk
tolerance than opportunity entrepreneurs. Additionally entrepreneurs motivated by a high
level of creativity are found to be less risk averse than other types of entrepreneurs. The
measurement of risk taking propensity in my study is similar to the method used by Block et
al. (2009), yet Block et al. (2009) also use two other measures. Another difference is that my
study uses another categorization of entrepreneurs. Block et al. (2009) do not take into
account the number of employees an entrepreneur has.
As mentioned earlier, Stewart et al. (1999) incorporated a measure for risk taking
propensity in their study on differences between different types of self-employed6 and
corporate managers. Their findings show that ‘small business owners’ are significantly less
risk oriented as compared to ‘entrepreneurs’. In addition, scholars have argued that successful
entrepreneurs have a higher risk tolerance than less successful entrepreneurs (Driessen and
Zwart, 2007). However, Rauch and Frese (2007) conclude from their meta-analysis that the
effect of risk taking propensity on entrepreneurial success is rather small. Zhao et al. (2010)
even argue that risk taking propensity is not significantly related to entrepreneurial
performance. In contrast, the results by Caliendo et al. (2010) indicate that the relationship
between risk attitude and entrepreneurial survival is inversely U-shaped. Entrepreneurs with
particularly low or particularly high risk attitudes have a lower survival rate than
entrepreneurs with a medium-level risk attitude.
Whether an individual’s risk taking propensity is fixed or can convert over time
remains subject to debate. For example Stewart et al. (1999, p. 194) argue that risk taking is
‘predispositional and not simply a situational variable’, based upon studies by Jackson et al.
(1972) and Plax and Rosenfeld (1976). Contrarily, McCarthy (2000, p. 567) claims that risk
attitude is ‘not just a static personality trait forged by nature or nurture, but seems to reflect
learning in a business context’. His view is supported by earlier findings that risk taking
propensity varies during different stages of business development (Brockhaus, 1987;
O'Farrell, 1986). Sitkin and Weingart (1995) argue that risk taking propensity is a stable trait
but can change over time as a result of experience. The authors stress that an individual’s risk
perception is partly determined by ‘outcome history’, which they define as ‘the degree to
6
Steward et al. (1999) divide self-employed into entrepreneurs and small business owners based on the theory of
Carland et al. (1984).
11
which the decision maker believes that previous risk-related decisions have resulted in
successful or unsuccessful outcomes’ (Sitkin and Weingart, 1995, p. 1576). Baron (2007,
cited in Zhao et al., 2010, p. 389) notes that ‘attention to the types of outcomes associated
with different stages of entrepreneurship may help reconcile conflicting findings regarding the
role of risk propensity in entrepreneurship’.
In summary, psychological factors in the form of personality traits might influence the
decision to hire (additional) employees. Little is known with certainty about the influence of
risk propensity on job creation ambition and realization.
2.2.2 Owner-manager motivation
Another psychological factor of influence is owner-manager motivation, which is related to
but distinguishable from personality traits. An owner-manager’s aspiration to expand is
regarded as an important factor in the explanation the growth of a business venture (Hansen
and Hamilton, 2011; Delmar and Wiklund, 2008; Wiklund and Shepherd, 2003; Smallbone et
al., 1995). Empirical findings by Davidson (1989) in Sweden indicate that once firms reach a
size of about 5-9 employees, willingness for additional growth by the owner-manager
decreases. On the contrary, Delmar and Wiklund (2008), also using Swedish data, argue that
achieving growth reinforces future growth motivation. Willingness to expand is positively
influenced by the personality trait need for achievement (Davidson, 1989). Ambition to
expand can arguably be negatively influenced by perceived barriers to growth, however
Doern (2011) claims that the relationship between barriers and intentions has not been
examined explicitly. Paragraph 2.3 Barriers to (employee-) growth explores the concept of
(perceived) barriers in more detail.
In my research the motivation of the entrepreneur to expand is measured by several
statements that reflect entrepreneurial ambitions.
2.2.3 Financial capital
Obviously one of the requirements for hiring (additional) external labour is availability of
sufficient financial means in order to pay the worker’s salary and to potentially make
necessary investments so that the newly hired worker can be productive. Although according
12
to financial theory business owners would have no trouble financing investments if expected
cash flows are positive, this is not always the case for small business owners.7
Empirical research on the influence of financial capital on job creation is limited but it
seems that financial capital has a positive influence on job creation. For example Henley
(2005) finds that housing has a positive influence on the ability of self-employed to create
jobs, as housing is an important source of collateral in acquiring external finance. Likewise
Wiklund and Shepherd (2003) find that access to financial capital has a positive direct effect
on growth. Congregado et al. (2010) find that incomes of OAW in prior periods has a positive
effect on becoming an Employer.
However, research on the influence of financial capital suffers from lack of reliable
and representative data. Therefore, this factor is not included in my empirical research. Yet,
the amount of financial capital of an entrepreneur is found to be positively influenced by the
amount of his human capital (Bates, 1990), for which indicators are included.
2.2.4 Human capital
High levels of informal and formal human capital are necessary for OAW to become
Employers (Cowling et al., 2004). Human capital can be measured by indicators such as
achieved educational level, age and (business) experience. Sluis et al. (2008) conclude from
their meta-analysis that the relationship between human capital and entrepreneurial success is
higher for knowledge/skills which are outcomes of human capital investments, compared to
experience/schooling which are direct human capital investments.
Meta-analysis studies have shown that schooling has a positive influence on
entrepreneurial performance (Unger et al., 2011; Sluis et al., 2008). Additionally Henley
(2005) and Congregado et al. (2010) find, by using data for the UK and EU-15 respectively,
that level of formal education has a positive influence on the likelihood of OAW to become
an Employer. Yet both Cowling et al. (2004) and Burke et al. (2002), using British data, find
that the positive influence of education only applies to men. Wiklund and Shepherd (2003)
furthermore find in Sweden that level of education itself has no effect on growth, but
education does positively influence the relationship between aspiration and growth.
7
Cagetti and de Nardi (2006) show that tightness of borrowing constraints is one of the main forces in
determining the number of entrepreneurs in a society; Van Praag and van Ophem (1995) have shown that
availability of capital is typically the most constraining factor in the decision to start a business. See furthermore
Fairlie and Krashinsky (2012) for a discussion on liquidity constraints and problems with measuring methods.
13
According to findings by Henley (2005) and Cowling et al. (2004) age has a positive
effect on becoming a job creator. Contrarily, the findings by Wiklund and Shepherd (2003)
suggest that owner’s age has no impact on growth. Likewise Carroll et al. (2000) find that age
itself does not increase the probability of becoming a job creator, but age does increase the
number of jobs created among self-employed with personnel.
A positive influence of business experience on hiring employees is found by
Congregado et al. (2010), Cowling et al. (2004) and Burke et al. (2000). In contrast, Wiklund
and Shepherd (2003) find no direct relationship between experience and growth, yet they do
find that experience affects growth when accompanied by owner’s growth aspirations.
To sum up, even though results are somewhat mixed, human capital seems to have a
positive impact on the likelihood to hire (additional) employees, although the strength of the
influence may differ for males and females and for Employers and OAW.
2.2.5 Miscellaneous factors
There are a couple more factors of interest on the decision to hire (additional) employees that
are included in my empirical research. Firstly, number of weekly working hours by the
entrepreneur is of importance. Working part-time decreases the likelihood of becoming an
Employer (Congregado, 2010) and is characterized by having significantly fewer employees
(Burke et al, 2000). Differences in employment creation are also related to gender, as several
studies find that female entrepreneurs have lower job creation rates (Henley, 2005; Burke et
al., 2002; Cooper et al., 1994). Furthermore for both Employers and OAW, entering selfemployment from unemployment has a negative influence on the probability of survival
(Millán et al., 2012; Millán et al., 2011; van Praag, 2003) and thus perhaps also on job
creation. Another control factor that is often taken into account in empirical research is
industry.8However empirical results are too inconsistent to make predictions about differences
in sectors (Millán et al., 2012).
Additionally there are a number of control factors that are sometimes taken into
account but given the scope of my empirical research are not included. These control factors
are for example macro economical factors such as unemployment rate, business failure rate
and GDP growth9, and institutional factors such as tax rates10. Again empirical results for
these factors are mixed (Millán et al., 2012).
8
9
For example industry variables are used by: Millán et al. (2012) and van Praag (2003).
For example macro economical variables are used by: Millán et al. (2012) and van Praag (2003).
14
2.3 Barriers to (employee-)growth
An entrepreneur can perceive several barriers to employee growth which may prevent him to
have EGA in the first place. Even whenever the entrepreneur has no perception of barriers or
does not let the perceived barriers stop him to pursue hiring (additional) employees, he might
not be able to accomplish his ambition because of the real existence of those barriers.
According to Robson and Obeng (2008), the literature on constraints to SME (employee)
growth is less extensive than research on explanations of firm’ success. Some scholars focus
on investigating the existence of a specific barrier, whereas other scholars investigate multiple
barriers with the purpose of defining the most constraining barriers. The most investigated
barrier is lack of finance.11 There are however more barriers, take for example employment
protection legislation, which is found to be negatively related to the decision of OAW to
become an Employer depending on its strictness (Millán et al., 2013). Another governmental
barrier is regulatory pressure, yet research indicates that perception of regulatory pressure
varies widely among entrepreneurs (Avans, 2010). De Jong and van Witteloostuijn (2011)
investigate (perceived) administrative burdens in northern provinces of the Netherlands. They
find that administrative burdens are negatively perceived and have indeed a significant
negative real influence on business performance. This negative effect is especially strong for
enterprises with 6-10 employees and companies within the construction and industry sector
(De Jong and van Witteloostuijn, 2011).
Empirical research taking multiple barriers into account are more inclined to focus on
non-Western countries.12 Nevertheless some of the research on (perceived) barriers is also
focused on Western countries. For example, findings by Davidson (1989) indicate that among
small business owner-managers the most important growth deterrents are employee wellbeing and loss of supervisory control. Vroonhof et al. (2007) investigate why Dutch OAW are
not willing to hire employees. According to their findings the most important reason concerns
‘business is not large enough’ and the second most indicated reason concerns ‘wanting to
remain total control’. According to another Dutch research, the most important reasons
concern ‘limited distribution market’ and ‘state of the economy’ (De Vries and Span, 2013).
The current economic crisis can be a barrier to growth. The negative impact of the crisis is
10
For example Carroll et al. (2000) include entrepreneurs’ personal taxes and Millán et al. (2012) include
Statutory tax rates on dividend income.
11
See paragraph 2.2.3 Financial capital
12
For example: Aidis (2005) investigates barriers to SME operations in East-European transition countries;
Robson and Obeng (2008) investigate barriers to growth in Ghana; Coad and Tamvada (2012) investigate firm
growth and barriers to growth among small firms in India.
15
more severe for SME than larger enterprises, in 2012 turnover decreased by 1.75% for SME
while it decreased by 0.5% for larger enterprises in the Netherlands. As a result employment
in SME decreased with approximately 20.000 jobs in 2012 and for 2013 employment in SME
was expected to decrease with approximately 35.000 jobs (Bangma and Snel, 2012).
According to a survey by the Chamber of Commerce, in the 1st quarter of 2012 58% of the
entrepreneurs said to be hindered by the economic climate. This number increased to 65% in
2013. Yet according to the same survey entrepreneurs are in 2013 less negative about
profitability, 19% of the entrepreneurs are negative about profitability compared to 25% in the
4th quarter of 2012 (COEN, 2013).
In my study the investigated barriers are chosen such that they capture lack of business
opportunities, financial factors, macro-economic factors, regulatory factors, employee
availability and personal preferences.
2.4 Concluding remarks from the literature
In conclusion, from this literature review it can be argued that self-employed are a
heterogonous group, differing in having employees or not and having future growth intentions
or not. The literature on the decision to hire (additional) employees is focused on achieved
outcomes rather than future ambitions. An entrepreneur’s decision to hire (additional)
employees is influenced by multiple factors. Stam et al. (2007, p. 3) neatly summarize this
complex process: ‘In order to grow a new business, growth intentions, resources and
opportunities are necessary conditions’. Arguably these conditions still apply when
enterprises exist for a longer period of time. In addition, the employee-hiring decision can be
restrained by several (perceived) barriers.
My empirical research focuses on ambition to hire (additional) employees in order to
contribute to the relative lack in the literature on this topic. More specifically, one of the aims
of the empirical research is to gain a better insight in the relationship between risk-attitude
and entrepreneurial ambition. Moreover clear distinctions are made between OAW and
Employers. Previously discussed factors on the decision to hire (additional) employees which
are included in my analysis are: risk taking propensity, owner-manager motivation, human
capital, weekly working hours, gender, previous employment status and industry.
Additionally the analysis also takes into account perceived barriers to employee-growth.
Previously discussed factors that are not (directly) taken into account in my analysis are:
16
financial capital13, personality traits other than risk taking propensity, macro-economic
factors14 and institutional factors15.With the main explanatory variable being risk taking
propensity, the main hypothesis is the following: There is a positive relationship between risk
taking attitude and ambition (realization) to hire (additional) employees.
3
Research methodology
This paragraph provides information about the set-up of the empirical research. In order to
determine the factors that can help explain differences in ambition (and realization) to hire
(additional) external labor, a survey is designed (see Appendix) and online distributed among
entrepreneurs by bureau of Onderzoek and Statistiek Amsterdam (O+S). First paragraph 3.1
discusses the data. Thereafter paragraph 3.2 explains the dependent, independent and control
variables and other measures. Finally paragraph 3.3 describes the estimation method.
3.1 Data
A survey was sent out online to a panel of 2409 entrepreneurs by O+S in May 2013. O+S is a
non-profit research agency of the municipality of Amsterdam which regularly sends out
questionnaires to their panel of entrepreneurs. 905 entrepreneurs have completed the survey,
resulting in an overall response rate of 37.6%. Participants are not necessarily (co-)owners of
the company for which they fill in the questionnaire. It is only meaningful to investigate the
relationship between risk taking attitude and entrepreneurial ambitions for those people who
are in control of realizing these ambitions. Therefore, 54 respondents who indicated not to be
a (co-)owner are excluded from analysis. Additionally 18 responses are excluded because
these were considered to be inadequate16. From the remaining 833 responses, 536 (64%) are
from OAW and 297 (36%) are from Employers. An OAW is defined as having zero
employees. Entrepreneurs who employ one or more people are defined as Employers. All
13
However financial capital is indirectly taken into account as possibly perceived barriers to employee growth.
Also because financial capital is related to human capital (Bates, 1990), financial capital is indirectly taken into
account by including human capital factors.
14
However macro-economical factors are indirectly taken into account by the possibly perceived barrier ‘no
opportunity to increase turnover’ which is related to the state of the economy.
15
However institutional factors are indirectly taken into account by possibly perceived barriers regarding
employee protection legislation and administrative burdens.
16
14 respondents did not submit their number of employees, 1 respondent’s age of 99 seemed questionable and
3 respondents indicated to have respectively 300, 310 and 7000 employees. According to European Union
guidelines a company can only be defined as an SME if it employes less than 250 employees (EC, 2013).
17
companies are located in Amsterdam. Finally it is worth mentioning that not all survey
questions are relevant for my research, but these are nevertheless asked because of the interest
for O+S.
3.2 Measures
3.2.1 Dependent variables
There are four different regressions models conducted to investigate EGA. Each model has a
different dependent variable. These four dependent variables are: entrepreneurial stage,
having employees, having ambition to become Employer and having ambition to hire
additional employees.
The first dependent variable, entrepreneurial stage, can take four different values,
representing the different stages of (achieved) EGA an enterprise can go through. The
respondents are divided among the stages based upon their number of employees and EGA.
EGA is measured by asking the entrepreneur whether it is his ambition to hire one or more
(additional) employee(s) within the next five years. The answering possibilities were: ‘yes’,
‘no’ and ‘I do not know/ no answer’. Respondents who answered ‘I do not know/ no answer’
are excluded from this regression analysis, which resulted in a loss of 102 responses. The first
stage, coded 1, consists of entrepreneurs who have currently no employees and have no
ambition to change this within the next five years. The second stage, coded 2, consists of
entrepreneurs who have currently no employees, but ambition to hire one or more
employee(s) within the next five years has emerged. The third stage, coded 3, consists of
entrepreneurs who have achieved their ambition to hire one or more employee(s) by becoming
an Employer and are satisfied in the sense that they have no ambition to hire (an) additional
employee(s) within the next five years. The fourth stage, coded 4, consists of entrepreneurs
who have achieved their ambition to hire one or more employee(s), but still have a remaining
ambition to hire (an) additional employee(s) within the next five years. Of main interest in my
research are the determinants for progressing to the next stage, which is ultimately an endless
process. Due to lack of longitudinal data actual changes are not observed, yet observing the
current stage of an enterprise is also informative.
The second dependent variable, having employees, can take two values, either the
respondent is an OAW and thus has no employees, or the respondent is an Employer and thus
has at least one employee. Having no employees is coded 0 and having at least one employee
is coded 1. Having employees essentially means having achieved a prior EGA.
18
The third dependent variable, having ambition to become Employer, can take two
values and is only applicable to OAW. Having no ambition to become Employer by hiring
one or more employee(s) within the next five years is coded 0 and having this ambition is
coded 1. Respondents whose EGA is unknown are excluded, which resulted in a loss of 74
responses.
The fourth dependent variable, having ambition to hire additional employees, can take
two values and is only applicable to Employers. Having no ambition to hire one or more
additional employee(s) within the next five years is coded 0 and having such an ambition is
coded 1. Again, respondents whose EGA is unknown are excluded from the regression, which
resulted in a loss of 28 responses.
3.2.2 Main independent variable
The main independent variable is general risk taking propensity, measured by a question
originated from the German Socio-Economic Panel (SOEP) which directly asks respondents
to give a global assessment of their willingness to take risks. The wording (translated) is as
follows: “How do you see yourself: are you generally a person who is fully prepared to take
risks or do you try to avoid taking risks? Please indicate your answer on the scale below,
where the value of 0 means ‘not at all willing to take risks’ and the value of 10 means ‘very
willing to take risks’.”(Dohmen et al. 2011, p. 525). The behavioral validity of this survey
measure is verified by Dohmen et al. (2011) and the same measure is for example used by
Caliendo et al. (2009) and Block et al. (2009). The risk taking attitude variable is divided into
eleven levels. The lowest risk taking propensity is coded 0 (corresponding with an answer of
0) and the highest risk taking propensity is coded 10 (corresponding with an answer of 10).
3.2.3 Control variables
Several other independent variables are included in the regressions as control variables. These
variables are chosen based upon a possible influence on the dependent variable according to
the literature review in paragraph 2.2. All information on the control variables are measured
by the survey or are gathered by a previous survey. The first control variable is gender, a
dummy variable which equals 1 for females. Another control factor is human capital, which is
captured by the variables age of the entrepreneur, education and additionally for OAW only,
previous experience as Employer. Age of the entrepreneur is measured in number of years.
19
Based upon suggestion by Carroll (2000, p. 336) a quadratic term in age is also added in the
regression. Education concerns the highest obtained diploma of the entrepreneur, divided into
seven categories. The lowest form of education, primary school, is coded 1 and the highest,
university (dr), is coded 7. Previous experience as Employer by OAW is a dummy variable
which equals 1 if an OAW answered ‘yes’ to the question whether he used to have employees
in his current or in a previous venture. Another independent variable is prior occupation.
Respondents were asked what their most important occupation was directly prior to their
current occupation. ‘Prior wage employee’ is a dummy variable which equals 1 for
individuals whose main occupation prior to their current occupation was being a wage
employee. The other dummy variables for prior occupation are: ‘Prior self-employed’, ‘Prior
family business’, ‘Prior unemployed’, ‘Prior student’, ‘Prior housemaker’, ‘Prior retired’,
‘Prior volunteer’ and ‘Prior other’. Another control variable is weekly working hours,
measured by the weekly average number of working hours as indicated by the respondent.
Finally another type of control factors are venture characteristics, consisting of venture age,
number of employees, sector and in case of Employers, phase of hiring first employee.
Venture age is the age of the business and is measured by the reported number of years since
the venture is located in Amsterdam. Data on actual venture age is unknown, yet it is likely to
assume that in most cases the venture is originally established in Amsterdam. In previous
research venture age is sometimes regarded as an indicator of human capital (Cowling et al.,
2004). However, in this case venture age is an inaccurate indicator of human capital because it
might be possible that a responding entrepreneur was not the founder of his company. Sector
concerns the industry in which the main activity of the business takes place. ‘AFF sector’ is a
dummy variable which equals 1 for individuals whose main activity of the venture concerns
agriculture, forestry and fishing. In addition the other dummy variables for sector are:
‘Industrial sector’, ‘Construction sector’, ‘Trade sector’, ‘Distribution sector’, ‘Hospitality
sector’, ‘Communication sector’, ‘Financial institutions sector’, ‘Real estate sector’, ‘Business
services sector’, ‘CSR Sector’ (=Culture, Sports and Recreation), ‘Other services sector’ and
‘Other sectors’. Finally, phase of hiring first employee is divided into three categories. The
first category, coded 1, is a dummy variable for ventures which hired their first employee
within a year after the company was founded. The second category, coded 2, is a dummy
variable for ventures which hired their first employee between one and two years after the
company was founded. The third category, coded 3, is a dummy variable for ventures which
hired their first employee more than two years after the company was founded. Table A1 (in
the appendix) gives an overview of all variables.
20
3.2.4 Reasons for employee growth and barriers
Besides previously discussed variables, the questionnaire investigates two other measures that
are of interest to this paper, namely reasons for aspiring employee growth and barriers to
employee growth. Respondents who indicated to have EGA, are asked what their main
reasons are for having EGA. This is an open question, so that the mind of the entrepreneur
cannot be influenced by provided answer categories. The disadvantage of an open question
however is that not every respondent is likely to answer and answers might be difficult to
categorize.
As discussed in paragraph 2.3, entrepreneurs can experience several barriers to
employee growth. In order to get a better insight into the barriers to employee growth that are
perceived in Amsterdam, questions regarding these barriers are included in the questionnaire.
All respondents were presented possible barriers and were asked to express whether each of
these barriers were applicable to them by means of answering ‘yes’, ‘no’ or ‘I do not know/
no answer’. The advantage of phrasing the questions with provided answer possibilities is
convenience in comparability of responses between different subgroups. The presented
barriers capture both individual and external factors. Most barriers are arguably not
distinctively internally or externally influenced. The barrier ‘Having sufficient access to
external financial means’ depends for example on internal factors such as having collateral
(Henley, 2005) and human capital (Bates, 1990), and on external factors such as tightness of
borrowing constraints (Cagetti and de Nardi, 2006).
The presented barriers differed between OAW and Employers, and within this groups
depending on indicated ambition. The reasons for presenting them different barriers is that not
every barrier is applicable for every subgroup. For both OAW and Employers with EGA, the
presented barriers are: ‘foreseeing insufficient additional turnover’, ‘having insufficient
private financial means’, ‘having insufficient access to external financial means’, ‘legislation
concerning protection against dismissal’, ‘legislation concerning continued payment in case of
employee’s sickness and disabilities’, ‘additional administrative burdens’ and ‘not being able
to find (a) suitable employee(s)’. In addition to these barriers, entrepreneurs without EGA and
with unknown EGA were also presented the following reason not to hire employees:
‘satisfaction with current situation’. Furthermore both OAW without EGA and OAW with
unknown EGA were also presented the following reasons not to hire employees: ‘wanting to
maintain complete control over the enterprise’ and ‘not willing to take responsibility over
21
employees’. Finally all respondents were also given the opportunity to report another barrier
themselves. Thereby the disadvantage of missing out on important barriers is reduced.
3.2.5 Entrepreneurial ambitions
An entrepreneur can have several ambitions, ambition to hire (additional) employees is only
one element. It could be possible that risk taking propensity does not have a specific influence
on employee-growth-ambition, but that there is merely a general correlation between risk
attitude and ambition. In the survey four other entrepreneurial ambitions are also measured.
With these entrepreneurial ambitions it can be tested whether risk taking propensity
specifically influences EGA, or whether risk taking propensity has an influence on
entrepreneurial ambitions in general. The four entrepreneurial ambitions which are measured
are: ‘ambition to remain active with the enterprise in the next five years’ (RAA), ‘ambition to
increase turnover within the next five years by hiring other self-employed and/ or cooperate
with other self-employed for the fulfilling of projects’ (SECA), ‘ambition to contribute
significantly to society within the next five years’ (SCA) and ‘ambition to increase profits
within the next five years’ (PGA). Again respondents could answer ‘yes’, ‘no’ or ‘I do not
know/ no answer’. The remaining-active ambition indicates the entrepreneur’s long-term
perspective. The self-employed-cooperation-ambition is relevant to ask because hiring other
self-employed on a project basis is an alternative to hiring employees. It is also possible for
entrepreneurs to enter a more equal (and possibly less official) cooperation with other selfemployed. By measuring this ambition it can be analyzed whether respondents who want to
achieve profit growth without hiring (additional) employees, want to achieve profit growth by
hiring other self-employed and/ or cooperating with other self-employed. The societycontribution-ambition is included because not every entrepreneur is (solely) profit-driven.
Finally the profit-growth-ambition indicates whether the respondent is satisfied with his
current profit level or wants to increase his profit level.
3.3 Estimation method
Different estimation methods are used for the different dependent variables. The first
dependent variable, entrepreneurial stage, can considered to be ordered response data and
therefore an ordered probit regression model is used for analysis. Ordered response data arise
when mutually exclusive qualitative categories have a natural ordering, but do not have
22
natural numerical values which makes OLS inappropriate (Stock and Watson, 2011, p. 460).
The values that the variable entrepreneurial stage can take are four mutually exclusive levels,
which have a natural ordering. A higher stage indicates a higher entrepreneurial level.
The three other dependent variables, having employees, having ambition to become
Employer and having ambition to hire additional employees, can considered to be binary
variables. Probit regression models are used for analysis of these three dependent variables
since these models are specifically designed for binary dependent variables (Stock and
Watson, 2011, p. 429).
4
Results and analysis
In this section results of the empirical study are analysed. First paragraph 4.1 discusses the
descriptive statistics. Thereafter paragraph 4.2 analyses the results of the four different
regressions that are executed to investigate which factors influence ambition and realization
of an entrepreneur to hire (additional) external labor. Thereafter paragraph 4.3 compares the
regression outcomes on EGA with regressions on other entrepreneurial ambitions in order to
check for exclusivity. Finally paragraph 4.4 discusses reasons (not) to hire employees.
4.1 Descriptive statistics
As discussed in paragraph 3, in total 833 responses to the survey were found adequate for
analysis. The results confirm that entrepreneurs are a heteregenous group, since features differ
per subgroup. Table 1 summarizes the measured entrepreneurial ambitions. 84 (15.7%) OAW
indicated to have ambition to become Employer, 378 (70.5%) OAW indicated to have no
ambition to become Employer and 74 OAW (13.8%) indicated to not know whether they have
ambition to become Employer or did not want answer. Employers are significantly more
likely than OAW to have EGA (p < .01).17 185 (62.3%) Employers indicated to have the
ambition to hire additional employees, 84 (28.3%) Employers indicated to have no EGA and
28 (9.4%) Employers indicated to have an unknown EGA. Among OAW the most prevalent
ambition is to remain active (83.4%) and the least prevalent ambition is EGA (15.7%). This
suggests that a large majority of OAW are satisfied with the entrepreneurial stage they are in.
Among Employers the most prevalent ambitions are growth of profit (84.5%) and to remain
active (83.8%), and the least prevalent is to cooperate with other self-employed (52.5%).
17
In this paper all statistical tests on differences concern two-sided t-tests, unless otherwise specified.
23
Table 1: Descriptive statistics entrepreneurial ambitions
(1)
Employers
(N=297)
(2)
Percentage
Percentage
0.1567
0.7052
0.1381
OAW (N=536)
Employee-growth-ambition
‘yes’
‘no’
‘I do not know/ no answer’
Remaining-active-ambition
‘yes’
‘no’
‘I do not know/ no answer’
Self-employed- cooperationambition
‘yes’
‘no’
‘I do not know / no answer’
Society-contribution-ambition
‘yes’
‘no’
‘I do not know / no answer’
Profit-growth-ambition
‘yes’
‘no’
‘I do not know/ no answer’
(3)
difference in
proportions
(4)
(SE)
0.6229
0.2828
0.0943
-0.4662***
(0.03221)
0.8340
0.0784
0.0877
0.8384
0.0808
0.0808
-0.0044
(0.02673)
0.4440
0.4030
0.1530
0.5253
0.3636
0.1111
-0.0813**
(0.03606)
0.5970
0.1381
0.2649
0.6296
0.1380
0.2323
-0.0326
(0.03513)
0.6735
0.1847
0.1418
0.8451
0.0774
0.0774
-0.1716***
(0.02917)
notes: difference = prop (OAW) - prop (Employers); H0: diff = 0, H1: diff ≠ 0
* significant at 10% level; ** significant at 5% level; *** significant at 1% level
Table A2 (in the Appendix) provides an overview of the descriptive statistics of the
independent variables for both OAW and Employers. The average risk taking propensity is
significantly higher among Employers as compared to OAW (p < .01). The average general
risk taking propensity is 6.21 (S.D. 2.08) among the responding OAW and 6.91 among the
responding Employers (S.D. 1.89). The share of women is significantly larger among OAW
(36%)18 than among Employers (19%)19 (p < .01). The age of OAW ranges from 26 to 79
years old, whereas the age of the Employers ranges from 25 to 80 years old. The average age
of OAW and Employers is significantly different (p < .01). The average age of OAW is 50.66
years old20 and the average age of Employers is 48.61 years old.21 The education mean of
OAW is 5.09, indicating college level. However the most prevalent highest obtained diploma
18
In 4 responses gender was unreported. These missing values are adjusted to male.
In 2 responses gender was unreported gender. These missing values are adjusted to male.
20
In 13 responses age was unspecified. These missing values are adjusted to the OAW average.
21
In 9 responses age was unspecified. These missing values are adjusted to the Employer average.
19
24
is university (drs, master) (41%).22 Similarly, Employers have an education mean of 4.91,
indicating college level. However the most prevalent highest obtained diploma is university
(drs, master) (39%).23 In total 107 (20%) OAW have previous experience as Employer. Most
of the OAW (62%) were wage employee directly prior to becoming an OAW.24 Also
Employers were most often (58%) wage employee.25 OAW work on average significantly less
hours per week (42) than Employers (53) (p < .01).26 The average venture age among
Employers (19.9 years) is significantly older than among OAW (12.7 years) (p < .01).27 The
venture age ranges from 1 to 94 years among OAW and from 2 to 123 years among
Employers. The number of employees at Employers’ ventures ranges from 1 at the smallest to
125 at the largest enterprise. The mean (median) number of employees that Employers have is
9.3 (4) employees. Almost all sectors are represented by both OAW and Employers, except
for Real Estate which is only represented by Employers.28 For OAW the most prevalent sector
is Business services sector (25%) and for Employers the most prevalent sector is
Communication sector (17%). Finally the first employee was most often hired within a year
after the company was founded (50%), followed by more than two years after the company
was founded (27%) and between one and two years after the company was founded (15%). In
23 cases (8%) the phase in which the first employee was hired remained unanswered.
Table 2 shows the correlation coefficients between the investigated entrepreneurial
ambitions. Panel A depicts the correlations for OAW. For OAW, the highest correlations are
found between profit-growth-ambition & self-employed-cooperation-ambition (0.426) and
between self-employed-cooperation-ambition & employee-growth-ambition (0.423). A high
correlation between profit-growth-ambition & self-employed-cooperation-ambition was
expected since the measurement for self-employed-cooperation-ambition explicitly included
‘ambition to increase turnover’. The correlation between self-employed-cooperation-ambition
& employee- growth-ambition however is remarkable since hiring other self-employed for
22
In 2 responses highest obtained degree was answered with ‘I do not know’. These missing values are adjusted
to MBO (coded 4). In 5 responses highest obtained degree was answered with ‘no answer’. These missing values
are adjusted to university (drs, master).
23
In 4 responses highest obtained degree was answered with ‘no answer’. These missing values are adjusted to
university (drs, master).
24
Prior occupation was answered with ‘I do not know’ in 2 responses and with ‘no answer’ in 6 responses. These
missing values are adjusted to ‘prior other’.
25
Prior occupation was answered with ‘I do not know’ in 1 response and with ‘no answer’ in 3 responses. These
missing values are adjusted to ‘prior other’.
26
Working hours was unspecified in 13 responses from OAW and in 6 responses from Employers. These
missing values are adjusted to their respective average number of working hours.
27
Venture age was unspecified in 8 responses from OAW and 1 response from Employers. These missing values
are adjusted to their respective average venture age.
28
Sector was unspecified in 3 responses from OAW and in 1 from Employers. These missing values are adjusted
to ‘other sectors’.
25
Table 2: Correlations between entrepreneurial ambitions
Correlation Coefficients
1
2
3
4
5
1.000
0.154
0.423
0.161
0.322
1.000
0.231
0.186
0.321
1.000
0.302
0.426
1.000
0.264
1.000
1.000
0.267
0.325
0.242
0.371
1.000
0.201
0.232
0.310
1.000
0.382
0.346
1.000
0.225
1.000
Panel A: OAW (N=536)
1. Employee-growth-ambition
2. Remaining active-ambition
3. Self-employed-hiring/cooperation-ambition
4. Society-contribution-ambition
5. Profit-growth-ambition
Panel B: Employers (N=297)
1. Employee-growth-ambition
2. Remaining active-ambition
3. Self-employed-hiring/cooperation-ambition
4. Society-contribution-ambition
5. Profit-growth-ambition
projects and cooperating with other self-employed can be thought of as an alternative for
hiring employees. It is also interesting to note that the correlation between remaning-activeambition & employee-growth-ambition (0.154) is rather low. Apparently there is not a strong
relationship between ambition to remain active as a self-employed and ambition to hire
external labor.
Panel B depicts the correlation coefficients for Employers, these are somewhat
different than those for OAW but broadly show the same positive trends. For Employers, the
highest correlations are found between profit-growth-ambition & employee-growth-ambition
(0.371) and profit-growth-ambition & self-employed-cooperation-ambition (0.346). The high
correlation between profit-growth-ambition & employee-growth-ambition is expected as
growth of employees can help facilitate growth of profit. Similar to OAW, the high
correlation between profit-growth-ambition & self-employed-cooperation-ambition also fits
with expectations. For Employers the lowest correlation exists between self-employedcooperation-ambition & remaining-active-ambition (0.201). The largest difference in
correlations between OAW and Employers is found in remaining-active-ambition &
employee-growth-ambition (0.113). Which indicates that Employers who want to remain
active are more inclined to hire additional employees than OAW who want to remain active.
This makes sense as Employers are much more likely to have EGA than OAW.
26
4.2 Factors influencing EGA
4.2.1 Entrepreneurial stage
The dependent variable of the first regression is entrepreneurial stage. For this regression 731
observations are used. Table A3 (in the Appendix) contains the descriptive statistics. These
statistics reveal that the self-employed in this sample are very heterogeneous. The average
risk taking propensity of entrepreneurs in each stage differs significantly (p < .05).29 On
average the lowest risk taking propensity is found in stage 1 (OAW without EGA, 5.99),
while entrepreneurs in stage 4 (Employers with EGA) have on average the highest risk taking
propensity (7.30). A two-sample t test confirms that the difference in the mean of risk taking
propensity between these two groups indeed is significant (p < .01). Interestingly there is no
significant difference in risk taking propensity between OAW with EGA (stage 2) and
Employers with EGA (stage 4; p = 0.13). Similarly there is also no significant difference
between OAW without EGA (stage 1) and Employers without EGA (stage 3; p = 0.79). This
suggests that EGA is related to risk taking propensity. In addition the four groups differ
significantly in gender (p < .01), education (p < .10), prior occupation (multiple) and venture
age (p < .01).
Columns (1) and (2) in table 3 depict the results of the ordered probit regression. The
estimated coefficient of 0.097 for risk taking propensity is difficult to interpret. The sign and
the p-value however can be easily interpreted. Concluded can be that general risk taking
propensity has a positive and significant influence on entrepreneurial stage (p < .01). This
result is thus in accordance with the main hypothesis. It means that when comparing two
identical entrepreneurs who only differ in their general risk taking propensity, the person with
highest risk taking propensity is more likely to be in a higher entrepreneurial stage. The other
dependent variables that have a significant influence on entrepreneurial stage are: female, age,
working hours and venture age. The coefficient of female is negative (p < .01), indicating that
when comparing a male and female entrepreneur who are similar in all other aspects, the male
is likely to be in a higher stage than the female. The coefficient of age is negative (p < .10),
indicating that older entrepreneurs are less likely to be in a higher stage as compared to
younger entrepreneurs who have identical features. The coefficient of working hours is
positive (p < .01). Thus an increase of working hours by one hour, keeping all other factors
constant, has a positive influence on entrepreneurial stage. Finally venture age also has a
29
Here the statistical tests on differences concern Bartlett's test for equal variances, which is a one-way ANOVA
model. Significance implies that the variances are unequal for at least two groups.
27
Table 3: Factors influencing (realized) ambition to hire (additional) employees
Entrepreneurs (N=731)
DV = entrepreneurial
stage
Risk taking propensity
General risk taking propensity
Gender
Female
Human capital
Age entrepreneur in number of years
Age^2 entrepreneur
Education
Previous employer experience
Prior occupation
Prior wage employee
Prior self-employed
Prior unemployed
Prior student
Working hours
Weekly working hours
Venture characteristics
Venture age
Number of employees
Phase of hiring first employee
Entrepreneurs (N=833)
DV = having employees
OAW (N=462)
DV = having ambition to
become Employer
Employers (N=251)
DV = having ambition to hire
additional employees
(1)
Coefficient
(2)
(SE)
(3)
(4)
Marginal effect (SE)
(5)
Marginal effect
(6)
(SE)
(7)
Marginal effect
(8)
(SE)
0.09659***
(0.02382)
0.02515***
(0.0091)
0.01499*
(0.00781)
0.05284***
(0.01572)
-0.47054***
(0.10694)
-0.13783***
(0.0368)
-0.07083**
(0.02873)
-0.04099
(0.08162)
-0.07106*
0.00033
-0.00689
(0.03895)
(0.00039)
(0.03611)
-0.02207
0.00014
-0.00839
(0.01461)
(0.00015)
(0.01364)
-0.01400
0.00003
0.00466
0.12872***
(0.01537)
(0.00016)
(0.0128)
(0.05453)
0.00986
-0.00017
0.02417
(0.02369)
(0.00024)
(0.02092)
0.08173
0.15816
-0.28431
-0.17666
(0.17222)
(0.18624)
(0.34402)
(0.24178)
-0.03353
0.00556
-0.16597
-0.10341
(0.06596)
(0.0708)
(0.09412)
(0.07874)
-0.00303
-0.01074
-0.07455
-0.06993
(0.06324)
(0.06477)
(0.05482)
(0.04635)
-0.03690
0.01023
-0.20661
0.02070
(0.10211)
(0.11117)
(0.29695)
(0.14005)
0.02650***
(0.00322)
0.00876***
(0.00124)
0.00297***
(0.00104)
0.00440**
(0.00200)
0.01643***
(0.00326)
0.00895***
(0.00145)
-0.00526**
(0.00224)
-0.00338**
0.01464***
0.03666
(0.00172)
(0.00388)
(0.03348)
notes: * significant at 10% level; ** significant at 5% level; *** significant at 1% level.
Columns (1) and (2) concern an ordered probit regression; Columns (3) to (8) concern probit regressions.
positive influence on entrepreneurial stage (p < .01). The Wald test is executed to test whether
the parameters of all of the included explanatory variables are simultaneously equal to zero.
The Wald test is performed with 11 degrees of freedom. The results show that the Wald
statistic is significant at 1% level. Which means that the coefficients of all explanatory
variables that are included in the regression on entrepreneurial stage are not equal to zero and
thus should be included in the model.
4.2.2 Having employees
The dependent variable of the second regression is having employees. Columns (3) and (4) in
table 3 depict the results of the probit regression. All 833 adequate responses are used in this
regression, table A2 (in the Appendix) contains the descriptive statistics. Since the results
show marginal effects, interpretation is more easily than interpreting the results from an
ordered probit regression. The results report the marginal effect on the probability that an
entrepreneur has employees from an infinitesimal change in each continuous independent
variable and the discrete change in the probability for dummy variables. The results indicate
that general risk taking propensity has a significant positive influence on having employees
(p < .01). When comparing two entrepreneurs who only differ in their general risk taking
attitude, the entrepreneur with the highest risk taking propensity is 2.5% per degree of
difference more likely to have employees than the other entrepreneur. Thus entrepreneurs
with a higher risk taking propensity are more likely to succeed in achieving EGA. There are
two explanations possible. Firstly entrepreneurs with a higher risk taking propensity dare to
take more risks, leading on average to a higher success rate. Secondly, entrepreneurs with a
higher risk taking propensity are more inclined to have EGA than those with a lower risk
taking propensity.
The other dependent variables that have a significant influence on having employees
are: female, weekly working hours and venture age. These factors are thus almost similar to
the significant factors in the first regression, only here age is no longer of influence. A female
entrepreneur is 13.8% less likely to have employees than a male entrepreneur with identical
features (p < .01). An increase of weekly working hours, keeping all other factors constant,
increases the likelihood of being an Employer with 0.9% per increased hour (p < .01). An
increase of venture age by one additional year, keeping all other factors constant, increases
the likelihood of being an Employer with 0.9% (p < .01). The Wald test is executed with 11
degrees of freedom. The results show that the Wald statistic is significant at 1% level.
29
Which indicates that the coefficients of all explanatory variables that are included in the
regression on having employees are not equal to zero and thus should be included in the
model.
4.2.3 Ambition to become Employer
The dependent variable of the third regression is having ambition to become Employer.
Columns (5) and (6) in table 3 depict the results of the probit regression. In total 462
observations are used in this regression. Table A4 (in the Appendix) contains the descriptive
statistics. OAW with EGA have on average a significantly higher risk taking propensity than
OAW without EGA (6.93 vs. 5.99, p < .01). The regression results indicate that general risk
taking propensity has a positive and significant influence on the probability of OAW to have
ambition to become Employer (p < .10). When comparing two OAW who only differ in their
general risk taking attitude, the person with the highest risk taking propensity is 1.5% per
degree of difference more likely to have EGA. This result thus confirms that OAW with a
higher risk taking propensity are more inclined to have EGA than those with a lower risk
taking propensity. Additionally the other dependent variables that have a significant influence
on having ambition to become an Employer are: female, previous experience as Employer,
weekly working hours and venture age. A female OAW is 7.1% less likely to have EGA than
a male OAW with identical features (p < .05). If an OAW has previous experience as
Employer he is 12.9% more likely to have EGA than an OAW with similar features who has
no previous experience as Employer (p < .01). An increase of working hours, keeping all
other factors constant, increases the likelihood of having EGA with 0.3% per hour (p < .01).
An increase of one year in venture age decreases the likelihood of having EGA with 0.5% (p
< .05). Interestingly this last result is opposite to the effect that venture age has on being an
Employer. The Wald test is executed with 12 degrees of freedom. The results show that the
Wald statistic is significant at 1% level. Indicating that the coefficients of all explanatory
variables that are included in the regression on having ambition to become Employer are not
equal to zero and thus should be included in the model.
In order to control for sector specific influences, an additional regression including
sector dummies is executed. Columns (1) and (2) in table A6 (in the Appendix) depict the
results of this probit regression. The results indicate that none of the sectors has a significant
marginal influence on ambition to become Employer. Yet including the sector dummies
30
results in a small improvement in significance for risk taking propensity (p-value 0.051 vs.
0.042).
4.2.4 Employer’s ambition to hire additional employees
The dependent variable of the fourth regression is having ambition to hire additional
employees. Columns (7) and (8) in table 3 depict the results of the probit regression. In total
251 observations are used in this regression, table A5 (in the Appendix) contains the
descriptive statistics.30 Employers with EGA have on average a significantly higher risk
taking propensity than Employers without EGA (7.35 vs. 6.16, p < .01). The regression results
indicate that general risk taking propensity has a positive and significant influence on the
probability of Employers to have EGA (p < .01). When comparing two Employers who only
differ in their general risk taking attitude, the person with the highest risk taking propensity is
5.3% per degree of difference more likely to have EGA. This result thus confirms that
Employers with a higher risk taking propensity are more inclined to have EGA than those
with a lower risk taking propensity. Additionally the other dependent variables that have a
significant influence on having EGA for Employers are: weekly working hours, venture age
and number of employees. Remarkably in contrast with the other regression models, having
EGA does not differ significantly between identical male and female Employers. Apparently
females are less likely than their male counterparts to become Employer, but once they are an
Employer there is no difference in additional EGA. An increase of working hours, keeping all
other factors constant, increases the probability of having EGA with 0.4% per additional hour
(p < .05). Similar as for OAW, venture age decreases the likelihood of having EGA. An
increase of venture age by one year decreases the likelihood for Employers of having EGA
with 0.4% (p < .05). An Employer who has more employees more than an Employer with
identical features, is per additional employee 1.5% more likely to have EGA (p < .01). The
Wald test is executed with 13 degrees of freedom. The results show that the Wald statistic is
significant at 1% level. Indicating that the coefficients of all explanatory variables that are
included in the regression on having employees are not equal to zero and thus should be
included in the model.
In order to control for sector specific influences, an additional regression including
sector dummies is executed. Columns (3) and (4) in table A6 (in the Appendix) depict the
30
Compared to the regression on entrepreneurial stage, 23 observations are excluded. In these 23 cases the phase
in which the first employee was hired was unanswered.
31
results of this probit regression. The results indicate that none of the sectors has a significant
marginal influence on Employer’s EGA. Including the sector dummies does not result in
changes in significance for other independent variables.
In summary, the results of the previously discussed regressions indicate that general risk
taking propensity has a positive and significant effect on all four dependent variables. This is
in accordance with the main hypothesis. Entrepreneurs with a higher general risk taking
attitude are more likely to have EGA and, as a consequence, to succeed in achieving EGA.
The composition of other independent variables that significantly influence the dependent
variable differs per regression. Consistent with predictions from the literature (Henley, 2005;
Burke et al., 2002; Cooper et al., 1994), female has a significantly negative influence on
entrepreneurial stage and ambition and realization to become Employer. But there is no
significant effect of female on having EGA for Employers. The literature on the influence of
human capital on job creation is mostly positive. Here the influence of age is only confirmed
as a predictor for entrepreneurial stage, however the sign is negative. This correlates with
findings by Wiklund and Shepherd (2003) and Carroll et al. (2000). Education is not
significant in any of the regressions. Previous experience as an Employer does have a
significant and positive influence on OAW’s EGA. Which fits with expectations based on the
literature (Congregado et al., 2010; Cowling et al., 2004; Burke et al., 2000). In none of the
regressions prior occupation is of influence on the dependent variable. Perhaps the number of
respondents in some of the categories, such as ‘prior unemployed’, was too low. The results
of weekly working hours are positive and significant in all regressions, which confirms earlier
findings from the literature (Congregado, 2010; Burke et al., 2000). Venture age is significant
in all regressions, yet its sign differs per regression. This suggests that it takes enterprises a
couple of years to achieve EGA, but once this is achieved there is a certain turning point after
which desire to hire additional employees gradually decreases per year. An underlying reason
for this effect cannot be that willingness to grow decreases once a certain number of
employees is reached, as Davidson (1989) suggests. Number of employees has a positive and
significant effect for Employers on having EGA, which is in line with findings by Delmar and
Wiklund (2008). Phase of hiring first employee is not of significant influence for Employer’s
EGA. The dummy variables for sectors which were included to control for sector specific
effects in the regressions on having EGA for OAW and Employers were not significant. The
reason for this might be that some sectors were underrepresented.
32
Additionally, in order to check for robustness of the results regarding the adjustments that
were made to missing values, additional regressions are executed excluding the observations
with missing values. These results show no difference in significance.
4.3 Exclusivity check
An additional analysis is conducted in order to distinguish between a general correlation
between risk taking propensity and entrepreneurial ambition and a specific effect of risk
attitude on the ambition to grow a company by hiring (additional) employees. Three probit
regressions are executed with the following dependent variables: remaining-active-ambition,
self-employed-cooperation-ambition and profit-growth-ambition. Tables A7 and A8 (in the
Appendix) depict the results of these regressions and descriptive statistics can be found in
tables A9, A10 and A11 (in the Appendix). The results indicate that for OAW general risk
taking propensity only has a significant and positive influence on the self-employedcooperation-ambition. For Employers risk taking propensity has a positive and significant
influence on both self-employed-cooperation-ambition and profit-growth-ambition.31
When comparing two OAW who only differ in their general risk taking attitude, the
person with the highest risk taking propensity is 4.0% per degree of difference more likely to
have self-employed-cooperation-ambition (p < .01). Thus the magnitude of influence that risk
taking propensity for OAW has on self-employed-cooperation-ambition is larger than on EGA
(4.0% vs. 1.5%). Which suggests that cooperating together with and/ or hiring other selfemployed involves more risk than hiring employees. At first glance this may seem
counterintuitive, but perhaps an underlying reason why entrepreneurs seek cooperation is to
share risk. Yet another explanation might be that self-employed-cooperation-ambition
explicitly included the aim to increase turnover while this was not included in EGA. However
this reasoning would infer that risk taking attitude should also have a significant influence on
profit-growth-ambition, which was not the case.
When comparing two Employers who only differ in their general risk taking attitude,
the person with the highest risk taking propensity is 4.3% per degree of difference more likely
to have self-employed-cooperation-ambition (p < .05). This magnitude is similar to the effect
of risk taking attitude on EGA, yet for self-employed-cooperation-ambition the level of
31
However, the results from the regressions for Employers on remaining-active-ambition and profit-growthambition may be questionable due to the small number of Employers without remaining-active-ambition and
profit-growth-ambition (20 and 22 respectively).
33
significance is lower. In addition, the person with the highest risk taking propensity is 1.0%
per degree of difference more likely to have ambition to increase profit (p < .10). Thus the
strength of risk taking propensity for Employers on profit-growth-ambition is lower than on
EGA and self-employed-cooperation-ambition.
From these exclusivity checks can be concluded that general risk taking propensity
does not exclusively influence EGA. It also influences self-employed-cooperation-ambition
for both OAW and Employers, and profit-growth-ambition for Employers. These results are
not very surprising, given the relatively high correlations between self-employed-cooperationambition & EGA for both OAW and Employers. Also the influence of risk taking attitude on
profit-growth-ambition for Employers is not surprising, since a business strategy to increase
profits is likely to involve some risks. Remarkably though is that for OAW risk taking attitude
has no significant influence on ambition to increase profit. Risk taking attitude thus has an
influence on entrepreneurial ambition in general, which makes it difficult to establish the
specific effect of risk attitude on the ambition to hire (addition) employees. Factors that do
exclusively influence EGA and no other entrepreneurial ambitions are gender and current
number of employees.
4.4 Reasons (not) to hire employees
4.4.1 Reasons to hire employees
All respondents with EGA were asked in an open question what their main reasons are to hire
one or more (additional) employee(s). Out of these 269 entrepreneurs who indicated to have
EGA, 251 (93%) submitted an answer. 76 OAW reported one or more reasons (97 reasons in
total) and 175 Employers reported one or more reasons (210 reasons in total). Since the
manner of questioning was an open question, the answers had to be categorized to make
comparison possible. The categories are created based upon interpretation of the content of
the entries. A category is established if at least five mentions could be labelled under the same
header, i.e. capture the same meaning.
Table 4 depicts the results of the responses by OAW. The most often indicated reason
for having EGA among OAW can be broadly categorized as ‘expansion of business activities’
(44.3%). Responses in this category concern both ‘ability to accept more and larger projects’
and ‘increase in business activities’, as there are no clear boundaries between these reasons.
Essentially ‘expansion of business activities’ refers to expansion of capacity. The remaining
categories for having EGA contain less frequently mentioned reasons and are more detailed.
34
Table 4: OAW - Reasons to hire employees
Number of
mentions
Percentage in
total mentions
Expansion of business activities
43
44.3%
Delegation of tasks
13
13.4%
Professionalization
12
12.4%
Improving quality
6
6.2%
Reducing own workload
5
5.2%
Increase in turnover
5
5.2%
13
13.4%
97
100%
Category
Other
Total
Table 5: Employers - Reasons to hire additional employees
Number of
mentions
Percentage in
total mentions
Expansion of business activities
115
54.8%
Increase in turnover
25
11.9%
Reducing (own) workload
18
8.6%
Professionalization
12
5.7%
Improving quality
10
4.8%
Delegation of tasks
7
3.3%
Efficiency benefits
5
2.4%
Knowledge required
5
2.4%
Category
Other
Total
13
6.2%
210
100%
These categories are: ‘delegation of tasks’ (13.4%), ‘professionalization’ (12.4%), ‘improving
quality’ (6.2%), ‘reducing own workload’ (5.2%) and ‘increase in turnover’ (5.2%). Finally
the category ‘other’ comprises mentions which did not fit any of the above categories and
were not similar enough to form a new category. ‘Delegation of tasks’ concerns delegation
and distribution of tasks, which is coherent to ‘reducing own workload’. ‘Professionalization’
entails that the entrepreneurs aims to create a stronger basis by hiring employees, leading to
more stability. ‘Improving quality’ concerns for example improvement of service to satisfy
customers better. Interestingly one respondent mentioned his aim to make his enterprise
marketable so that it can function as retirement funding.
Table 5 depicts the results of the responses by Employers. Also among Employers the
most often indicated reason for having EGA can be categorized as ‘expansion of business
activities’ (54.8%). The second most cited reason is ‘increase in turnover’ (11.9%). Other less
35
frequently mentioned reasons for having EGA are more detailed. These can be categorized as:
‘reducing (own) workload’ (8.6%), ‘Professionalization’ (5.7%), ‘improving quality’ (4.8%),
‘delegations of tasks’ (3.3%), ‘efficiency benefits’ (2.4%), ‘knowledge required’ (2.4%) and
‘other’ (8.7%). It is remarkable that only 5 respondents explicitly mention ‘efficiency
benefits’ as their reason to hire an additional employee, a topic typically coherent to growth in
standard economic theory.
Based upon this short analysis cannot be concluded that the reasons between OAW
and Employers to hire (additional) employees differ much. Especially because the submitted
answers concern to a large extent ‘expansion of business activities’. From the answers it
remains unclear whether entrepreneurs aim to hire (additional) employees due to insufficient
capacity to satisfy existing demand or rather future demand. The latter is inherently more
risky, but it thus remains unanswered whether there exists a difference in inclined risk taking
by OAW or Employers.
Figure 1: Barriers to hiring (additional) employees
0%
20%
40%
60%
80%
100%
Foreseeing insufficient additional turnover
Having insufficient private financial means
Having insufficient access to external financial means
Legislation concerning protection against dismissal
Legislation concerning continued payment in case of
employee’s sickness and disabilities
Additional administrative burdens
Not being able to find (a) suitable employee(s)
Satisfaction with current situation
Wanting to maintain complete control over the enterprise
Not willing to take responsibility over employees
OAW with EGA
OAW without EGA
OAW unknown EGA
Employers with EGA
Employers without EGA
Employers unknown EGA
36
Table 6: Barriers to hiring (additional) employees
(N=84)
(1)
OAW
Without
EGA
(N=378)
(2)
Unknown
EGA
(N=74)
(3)
0.4762
0.4048
0.119
0.619
0.2725
0.1085
0.5135
0.3649
0.1216
0.3135
0.6324
0.0541
0.5595
0.3571
0.0833
0.5357
0.2857
0.1786
0.6071
0.3095
0.0833
0.5132
0.3492
0.1376
0.6081
0.2568
0.1351
0.4757
0.4865
0.0378
0.5
0.4286
0.0714
0.4643
0.3214
0.2143
0.5357
0.3095
0.1548
0.4048
0.3836
0.2116
0.473
0.3108
0.2162
0.3838
0.5514
0.0649
0.4762
0.4286
0.0952
0.3929
0.3214
0.2857
0.4048
0.4286
0.1667
0.4497
0.3942
0.1561
0.4595
0.3649
0.1757
0.6
0.3351
0.0649
0.5238
0.4167
0.0595
0.6071
0.2857
0.1071
0.5238
0.3452
0.131
0.5212
0.3333
0.1455
0.5946
0.2162
0.1892
0.5838
0.3568
0.0595
0.5
0.4405
0.0595
0.6429
0.25
0.1071
0.3571
0.5833
0.0595
0.5265
0.3757
0.0979
0.4189
0.4865
0.0946
0.2432
0.7297
0.027
0.3452
0.631
0.0238
0.2143
0.7143
0.0714
0.0833
0.7976
0.119
0.0926
0.6614
0.246
0.1622
0.6216
0.2162
0.3892
0.5568
0.0541
0.1429
0.7619
0.0952
0.0357
0.7857
0.1786
n.a.
n.a.
n.a.
0.828
0.1243
0.0476
0.6216
0.2973
0.0811
n.a.
n.a.
n.a.
0.6548
0.2738
0.0714
0.6071
0.3214
0.0714
n.a.
n.a.
n.a.
0.6693
0.2354
0.0952
0.4459
0.3784
0.1757
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
0.6561
0.2487
0.0952
0.5405
0.3514
0.1081
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
With EGA
Foreseeing insufficient additional turnover
Fraction ‘yes’
Fraction ‘no’
Fraction ‘I do not know/ no answer’
Having insufficient private financial means
Fraction ‘yes’
Fraction ‘no’
Fraction ‘I do not know/ no answer’
Having insufficient access to external
financial means
Fraction ‘yes’
Fraction ‘no’
Fraction ‘I do not know/ no answer’
Legislation concerning protection against
dismissal
Fraction ‘yes’
Fraction ‘no’
Fraction ‘I do not know/ no answer’
Legislation concerning continued payment
in case of employee’s sickness and
disabilities
Fraction ‘yes’
Fraction ‘no’
Fraction ‘I do not know/ no answer’
Additional administrative burdens
Fraction ‘yes’
Fraction ‘no’
Fraction ‘I do not know/ no answer’
Not being able to find (a) suitable
employee(s)
Fraction ‘yes’
Fraction ‘no’
Fraction ‘I do not know/ no answer’
Satisfaction with current situation
Fraction ‘yes’
Fraction ‘no’
Fraction ‘I do not know/ no answer’
Wanting to maintain complete control over
the enterprise
Fraction ‘yes’
Fraction ‘no’
Fraction ‘I do not know/ no answer’
Not willing to take responsibility over
employees
Fraction ‘yes’
Fraction ‘no’
Fraction ‘I do not know/ no answer’
Employers
Without Unknown
With EGA
EGA
EGA
(N=185)
(N=84)
(N=28)
(4)
(5)
(6)
notes: ‘With EGA’ represents ‘having ambition to hire (additional) employees’, ‘Without EGA’ represents ‘having no
ambition to hire (additional) employees’ and ‘Unknown EGA’ represents ‘ambition to hire (additional) employees is
unknown’.
37
4.4.2 Barriers to hiring employees
All entrepreneurs32 were presented possible barriers to hiring (additional) employees and
were asked to express whether each of these barriers were applicable to them. Figure 1 and
table 6 give an overview of the responses. The most collectively agreed upon perceived
barrier by OAW with EGA is ‘having insufficient private financial means’ (60.7%), by
Employers with EGA it is ‘legislation concerning protection against dismissal’ (60%) and by
Employers with unknown EGA it is ‘legislation concerning continued payment in case of
employee’s sickness and disabilities’ (58.4%). By both OAW and Employers without EGA
and OAW with unknown EGA, the most collectively agreed upon reason not to hire
employees is ‘satisfaction with current situation’ (respectively 82.8%, 65.5% and 62.2%).
Comparing the answers from the different subgroups leads to interesting insights.
Firstly, the results indicate that foreseeing sufficient additional turnover is an important driver
for having EGA. OAW without EGA are namely more inclined to perceive ‘foreseeing
insufficient additional turnover’ as a barrier compared to OAW with EGA (61.9% vs. 47.6%,
p < .05). Moreover, between Employers with and without EGA the difference in perceiving
‘foreseeing insufficient additional turnover’ as a constraint is even larger (31.4% vs. 56.0%,
p < .01). ‘Foreseeing insufficient additional turnover’ is likely to be related to the current
economic crisis. The results from this study are coherent to the results from earlier studies
which found that SME enterprises are hindered by the crisis (COEN, 2013; Bangma and Snel,
2012).
The results furthermore suggest that perception of administrative burdens could play a
role for OAW in determining their EGA. OAW without EGA namely perceive ‘additional
administrative burdens’ as more constraining than OAW with EGA (52.7% vs. 35.7%, p <
.01). Contrarily, between Employers with and without EGA there is no significant difference
in the perception of ‘additional administrative burdens’ as a barrier (24.3% vs. 34.5%). In
addition a large share of OAW without EGA indicate that ‘wanting to maintain complete
control over the enterprise’ (66.9%) and ‘not willing to take responsibility over employees’
(65.6%) are withholding them to have EGA.
The results furthermore indicate that having insufficient financial means is a larger
problem among OAW than Employers with EGA. OAW with EGA are namely more likely
than Employers with EGA to perceive ‘having insufficient private financial means’ and
32
Including entrepreneurs with unknown EGA.
38
‘having insufficient access to external financial means’ as constraints in achieving EGA
(respectively 60.7% vs. 47.6% and 53.6% vs. 38.4%, both p < .05). There is also an
interesting difference in the perception of ‘legislation concerning protection against dismissal’
as a barrier between Employers with EGA and OAW with EGA (60.0% vs. 40.1%, p < .01).
In addition there is also a large difference in perception of ‘not being able to find suitable
employee(s)’ as a barrier between OAW and Employers with EGA (8.3% vs. 38.9%, p < .01).
As discussed in paragraph 2.2.1, several scholars argue that risk perception can be influenced
by learning from experiences (Baron, 2007; McCarthy, 2000; Sitkin and Weingart, 1995;
Brockhaus, 1987; O'Farrell, 1986). An explanation for these difference in perception between
Employers and OAW might thus be that negative experiences in the past have influenced the
risk perception of Employers. In total 73.8% of the OAW have no previous experience as
Employer.
Also remarkable is the large difference in perception of ‘not being able to find (a)
suitable employee(s)’ as a barrier between Employers with and without EGA (38.9% vs.
14.3%, p < .01). Apparently not being able to find (a) suitable employee(s) is not an important
factor that withholds self-employed to have EGA, yet a considerable share of Employers
perceive it as barrier in achieving EGA.
In summary, the results indicate that out of the presented barriers the following barriers are
significantly more negatively perceived by OAW without EGA as compared to OAW with
EGA: foreseeing insufficient additional turnover and additional administrative burdens. In
addition important reasons why OAW do not want to become Employers are: satisfaction with
current situation, wanting to maintain complete control over the enterprise and not willing to
take responsibility over employees. For Employers the only factor that is perceived as
significantly more negative by those without EGA is: foreseeing insufficient additional
turnover. In addition, satisfaction with current situation is an important reason why Employers
do not want to hire additional employees. These results imply that for all entrepreneur
‘foreseeing sufficient additional turnover’ is an important driver for EGA. For OAW another
positive influence on EGA is ‘not letting oneself be put off by additional administrative
burdens’.
39
At last the possibility to submit another barrier besides the predetermined barriers resulted in
115 different mentions from OAW and 79 different mentions from Employers. Yet a large
number of the total responses concern issues which can be categorized among previously
presented barriers and thus contain no new information.33 In table 7 the results for OAW are
displayed. By not taking the responses into account which are already discussed, 82 responses
remain. These remaining mentions can be categorized as ‘no need for’ (22.0%), ‘loss of
freedom’ (14.6%), ‘not fitting well in profession’ (12.2%), ‘too expensive’ (9.8%), ‘lack of
workspace’ (8.5%) and ‘aim to quit’ (6.1%). Finally 22 mentions (26.8%) which could not be
categorized in any of the above categories remain.
Table 8 displays the results of the option to submit an additional barrier for
Employers. By not taking the responses into account which are already discussed, 47
mentions remain. These mentions can be categorized as ‘regulation’ (29.8%), ‘lack of
workspace’ (27.7%) and ‘expenses’ (12.8%). Finally 14 mentions (29.8%) which could not be
categorized remain. Regulation concerns for example that the collective labor restrictions are
too complicated.
Table 7: OAW - Self-reported constraints
Category
No need for
Loss of freedom
Not fitting well in profession
Too expensive
Lack of workspace
Aim to quit
Other
Total
Number of
mentions
18
12
10
8
7
5
22
82
Percentage in
total mentions
22.0%
14.6%
12.2%
9.8%
8.5%
6.1%
26.8%
100%
Table 8: Employers - Self-reported constraints
Number of
mentions
Percentage in
total mentions
Regulation
14
29.8%
Lack of workspace
13
27.7%
Too expensive
6
12.8%
14
47
29.8%
100%
Category
Other
Total
33
From OAW 28.7% of the total mentions can be categorized as either ‘not being able to find (a) suitable
employee(s)’ or ‘insecurity about sufficient turnover’ (due to current economic climate). From Employers 40.5%
of the total mentions can be categorized among those previously presented barriers.
40
Finally an additional analysis is conducted in order to establish whether the ambition to
cooperate with other self-employed is driven by perceived barriers in the legislation regarding
the hiring of employees. Table 9 and figure 2 give depict the responses to the barriers
concerning legislation, divided by subgroups representing differences in the self-employedcooperation-ambition. There is only a small significant difference in the perception of
‘legislation concerning protection against dismissal’ between OAW with and without selfemployed-cooperation-ambition (49.2% vs. 40.1%, p < .10). Thus from these results it cannot
convincingly be argued that entrepreneurs who have self-employed-cooperation-ambition are
driven by legislation issues which constraint them to hire (additional) employees.
Figure 2: Barriers to hiring (additional) employees, divided per ‘Self-employed-cooperation-ambition’
0%
20%
40%
60%
80%
100%
Legislation concerning protection against dismissal
Legislation concerning continued payment in case of
employee’s sickness and disabilities
OAW with SECA
OAW without SECA
OAW unknown SECA
Emp with SECA
Emp without SECA
Emp unknown SECA
Table 9: Barriers to hiring (additional) employees, divided by self-employed-cooperation-ambition
OAW
Employers
With
Without
Unknown
With
Without
SECA
SECA
SECA
SECA
SECA
(N=238)
(N=216)
(N=82x)
(N=156)
(N=108)
(1)
(2)
(3)
(4)
(5)
Legislation concerning protection against
dismissal
0.4916
0.4074
0.4024
0.5705
0.5463
Fraction ‘yes’
0.3824
0.4213
0.3659
0.3590
0.3981
Fraction ‘no’
0.1261
0.1713
0.2317
0.0705
0.0556
Fraction ‘I do not know’
Legislation concerning continued payment
in case of employee’s sickness and
disabilities
0.5630
0.4954
0.5366
0.5449
0.5741
Fraction ‘yes’
0.3151
0.3426
0.2683
0.3910
0.3611
Fraction ‘no’
0.1218
0.1620
0.1620
0.0641
0.0648
Fraction ‘I do not know’A
Unknown
SECA
(N=33)
(6)
0.7273
0.1818
0.0909
0.6364
0.3030
0.0606
notes: ‘With SECA’ represents ‘having self-employed-cooperation-ambition’, ‘Without SECA’ represents ‘having no
self-employed-cooperation-ambition’ and ‘Unknown SECA’ represents ‘having self-employed-cooperation-ambition is
unknown’.
41
5
Discussion and limitations
This section provides an additional discussion of the results and limitations of the empirical
research. Based upon these limitations recommendations for future research are provided. The
analysis of the empirical results in paragraph 4 made clear that the main hypothesis is proven.
An entrepreneur’s general risk attitude has indeed a positive influence on ambition and
realization to hire (additional) employees. This implies that self-employed regard hiring
(additional) employees as risk-involving. Ambition to hire (additional) external labour arises
more strongly for those entrepreneurs who are willing to bear involved risks. However, the
results of the exclusivity checks indicate that risk taking attitude is also of importance in
having a self-employed-cooperation-ambition for both OAW and Employers, and in having a
profit-growth-ambition for Employers. This implies that risk attitude and ambition are related
in general. In fact, for OAW general risk taking propensity is relatively a more important
influence on having self-employed-cooperation-ambition than on having EGA. Which
suggests that OAW perceive striving to succeed self-employed-cooperation-ambition as a
more risk-involving business strategy than striving to succeed EGA. This is a remarkable
finding, as it contradicts the belief that working together with other self-employed is more
flexible and therefore less risky than hiring employees. Perhaps cooperation between selfemployed is driven by risk sharing. Additional research is necessary to establish what the
perception among entrepreneurs is of risk in hiring other/ cooperating with other selfemployed and how much risk-involving it actually is.
The results furthermore indicate that a high general risk taking propensity increases
the probability of achieving EGA. However, an underlying reason for this effect is that OAW
with EGA are more inclined to take risks to begin with. Policy-makers can adopt several
measures to stimulate self-employed to hire employees. Firstly, policy-makers can stimulate
entrepreneurs to take risks, for example by rewarding innovative businesses. Secondly,
policy-makers can lower barriers to decrease the required risk-taking. According to the results
in paragraph 4.3, the (local) government could lower administrative burdens and can help
facilitate workspace. In addition, OAW struggle more with attaining sufficient private and
external financial means than Employers. Policy-makers might help facilitate financial means
for OAW. Furthermore, foreseeing insufficient turnover is an important explanation for not
having EGA. If policy-makers find a way to stimulate turnover, this would likely stimulate
self-employed to have EGA. The most prevalent indicated reason for having EGA is business
expansion. Policy-makers can for example help stimulate business expansion by organising
42
networking event. Finally policy-makers have to take into account that a large share of selfemployed, especially among OAW, have simply no intentions to hire (additional) employees
because they are satisfied with their current situation. There is probably little that the (local)
government can do to stimulate these self-employed to hire (additional) employees.
There are however several limitations to the empirical findings. First of all the external
validity might be threatened since all participating enterprises are located in Amsterdam.
Entrepreneurs in Amsterdam and their kind of enterprises might differ from entrepreneurs in
other Dutch regions, especially rural areas. Which implicates that the results cannot be
generalized for the entire Dutch entrepreneurial population. In order to establish whether the
findings are applicable to the entire Dutch entrepreneurial population, the survey should be
executed in other regions.
Another threat to external validity is a typical limitation concerning survey data.
Respondents volunteered to participate in the Panel of Entrepreneurs and in this particular
survey. Which means that the sample is not random. These volunteering entries might not
give an accurate representation of the entire entrepreneurial population of Amsterdam. The
concern for this limitation is not very high though given the large number of responses.
Moreover the sample covered entrepreneurs with a wide range of different characteristics.
Another limitation is that a large share of variation in EGA remains unexplained by
the regression models. There are several explanations for the lack of explanatory power.
Firstly, as always with survey data measurement error is likely to be present. Each respondent
interprets the questions asked in his own way and has a different response style (Davidson,
1989). Secondly, it is likely that there are omitted variables which are of importance in
explaining EGA. As concluded in the literature review, an entrepreneur’s decision is to hire
(additional) employees is a complex process. Multiple factors are of influence. Due to lack of
reliable data, several factors are not included in the analysis. These factors are for example
financial means, personality traits other than risk taking propensity, macro-economic and
institutional factors. The explanatory power of the regressions will likely improve when
reliable information on these omitted variables will be included.
Another limitation is the cross-sectional nature of the data. Longitudinal data would
have been preferable. Several studies indicate that growth intentions are related to actual
growth outcomes (Hansen and Hamilton, 2011; Delmar and Wiklund, 2008; Wiklund and
Shepherd, 2003; Smallbone et al., 1995). Yet in this particular research the strength of the
relationship between EGA and actually hiring employees within five years remains unknown.
43
The best way to establish the actual strength of this relationship is to investigate the achieved
results among the same set of respondents in 2018.
Finally another concern is whether the relationship between general risk taking
preference and EGA is merely a correlation or causal. Multiple factors are of influence on
EGA, it is difficult to determine the precise extent of influence of general risk taking on EGA.
Additionally there might even be reversed causality. It is debatable whether someone’s risk
taking preference is fixed or can be altered by experiences. Delmar and Wiklund (2008) argue
that having achieved growth reinforces future growth motivation. Having achieved (profit)
growth can indirectly influence EGA positively in two ways. Firstly the entrepreneur has
financial resources, which is a driver of EGA. Secondly the entrepreneur has positive
feedback from previous risk-involving decisions, which might increase the entrepreneur’s
general risk taking preferences, which in turn is a driver of EGA. The question is thus whether
successful entrepreneurs are successful because they are risk taking or whether having
experienced success leads to increased risk taking preferences. Future research is necessary to
inquire whether risk taking preferences are fixed over time or can be altered by experiences.
6
Conclusion
The aim of this paper was to investigate the factors that determine ambition and realization of
an entrepreneur to hire (additional) labour and whether these factors differ for Own-Account
Workers (OAW) and Employers. In particular the factor risk taking propensity is investigated.
A survey is designed and online distributed among entrepreneurs by O+S and ACE. The
findings support the notion that entrepreneurs are very heterogeneous. Only 16% of the
responding OAW has the ambition to hire at least one employee compared to 62% of the
Employers. The regression models that were executed confirm the hypothesis: there is a
positive and significant relationship between general risk taking propensity and having EGA.
In addition, entrepreneurs with a higher general risk taking attitude are, partly as a
consequence, also more likely to succeed in achieving EGA. Yet the magnitude of the
influence of risk attitude on EGA differs between OAW and Employers. However, the
exclusivity checks indicate that general risk taking propensity does not exclusively influence
EGA but also other entrepreneurial ambitions. This implies that risk attitude and
entrepreneurial ambition are related in general, which makes it difficult to establish the
specific effect of risk taking propensity on the ambition to hire (additional) employees. Two
factors were found to exclusively influence EGA, namely gender (OAW only) and current
44
number of employees (Employers only). Additional research on the (perceived) riskiness of
self-employed-cooperation-ambition is desirable.
An explanation for the influence of risk taking attitude on EGA would be that hiring
(additional) employees is perceived as risk involving. If policy-makers want to stimulate
entrepreneurs to hire employees, they thus either have to stimulate risk taking or lower the
risks involved.
The findings confirmed that not every entrepreneur has a desire to let his venture grow
bigger. The next step will be to reexamine the same set of entrepreneurs in 2018 and see
whether the ambitious entrepreneurs were able to put their ambitions into reality.
45
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49
Appendix I
Tables
Table A1: Description of variables
Variable
Dependent variable
Entrepreneurial stage
Description
Coded 1: individuals who have no employees and have no ambition to hire
employees (OAW-NEGA)
Coded 2: individuals who have no employees and have ambition to hire
employees (OAW-WEGA)
Coded 3: individuals who have one or more employee(s) and have no ambition
to hire additional employees (E-NEGA)
Coded 4: individuals who have one or more employee(s) and have ambition to
hire additional employees (E-WEGA)
Having employees
Coded 1: individuals who have no employees
Coded 2: individuals who have one ore more employee(s)
Having ambition to become Employer
Coded 1: individuals who have no employees and have no ambition to hire
employees (OAW-NEGA)
Coded 2: individuals who have no employees and have ambition to hire
employees (OAW-WEGA)
Having ambition to hire additional
employees
Coded 1: individuals who have one or more employee(s) and have no ambition
to hire additional employees (E-NEGA)
Coded 2: individuals who have one or more employee(s) and have ambition to
hire additional employees (E-WEGA)
General risk taking propensity
General risk taking propensity
Gender
Female
Respondents' self-assessed general willingness to take risks. Coded 0: ‘not at all
willing to take risks’, to 10: 'very willing to take risks’
Dummy equals 1 for females
Human capital
Age entrepreneur
Age of entrepreneur in number of years
Age² entrepreneur
Age of entrepreneur in number of years squared
Education
Coded 1: individuals with primary school as highest education level achieved
Coded 2: individuals with VMBO/Mavo/VBO school as highest education level
achieved
Coded 3: individuals with Havo/VWO as highest education level achieved
Coded 4: individuals with MBO as highest education level achieved
Coded 5: individuals with HBO as highest education level achieved
Coded 6: individuals with university (drs/Master) as highest education level
achieved
Coded 7: individuals with university (dr) as highest education level achieved
Previous Employer exerience
Prior occupation
Prior wage employee
Dummy equals 1 for OAW who have previous experience as Employer
Dummy equals 1 for individuals whose main occupation prior to their current
occupation was being a wage employee
Prior self-employed
Dummy equals 1 for individuals whose main occupation prior to their current
occupation was being self-employed
Prior family business
Dummy equals 1 for individuals whose main occupation prior to their current
occupation was assisting in a family business
50
Prior unemployed
Dummy equals 1 for individuals whose main occupation prior to their current
occupation was being unemployed
Prior student
Dummy equals 1 for individuals whose main occupation prior to their current
occupation was being a student
Prior housemaker
Dummy equals 1 for individuals whose main occupation prior to their current
occupation was being house wife or husband
Prior retired
Dummy equals 1 for individuals whose main occupation prior to their current
occupation was being retired
Prior volunteer
Dummy equals 1 for individuals whose main occupation prior to their current
occupation was doing voluntary work
Prior other
Dummy equals 1 for individuals whose occupation prior to their current
occupation does not fit any of the previous categories
Working hours
Working hours
Number of average working hours per week
Venture characteristics
Venture age
Number of years since the venture is located in Amsterdam
Number of employees
Current number of employees; zero employees represents OAW
Phase of hiring first employee
Coded 1: First employee hired within a year after venture was founded
Coded 2: First employee hired between 1 and 2 years after founding venture
Coded 3: First employee hired more than 2 years after founding venture
AFF sector
Dummy equals 1 for individuals whose main activity of the venture concerns
agriculture, forestry and fishing
Industrial sector
Dummy equals 1 for individuals whose main activity of the venture concerns,
manufacturing, wastemanagement, electricity, gas, steam and air conditioning.
Construction sector
Dummy equals 1 for individuals whose main activity of the venture concerns
construction
Trade sector
Dummy equals 1 for individuals whose main activity of the venture concerns
wholesale and retail trade, and repair of motor vehicles and motorcycles
Distribution sector
Dummy equals 1 for individuals whose main activity of the venture concerns
transport and storage
Hospitality sector
Dummy equals 1 for individuals whose main activity of the venture concerns
provision of accomodation, food and beverages
Communication sector
Dummy equals 1 for individuals whose main activity of the venture concerns
information and communication
Financial institutions sector
Dummy equals 1 for individuals whose main activity of the venture concerns
financial institutions
Real estate sector
Dummy equals 1 for individuals whose main activity of the venture concerns
renting, buying and selling of real estate
Business services sector
Dummy equals 1 for individuals whose main activity of the venture concerns
consultancy, research, other specialised business services, renting and leasing of
tangible goods and other business support services
CSR sector
Dummy equals 1 for individuals whose main activity of the venture concerns
culture, sport and recreation
Other services sector
Dummy equals 1 for individuals whose main activity of the venture concerns
other service activities
Other sectors
Dummy equals 1 for individuals whose main activity of the venture is not
elsewhere classified
51
Entrepreneurial ambitions
Employee-growth-ambition (EGA)
Ambition to hire one or more (additional) employee(s) within the next five years
Remaining active-ambition (RAA)
Ambition to remain active with the enterprise in the next five years
Self-employed-hiring/cooperationambition (SECA)
Ambition to increase turnover within the next five years by hiring other selfemployed and/or cooperate with other self-employed for the fulfilling of projects
Society-contribution-ambition (SCA)
Ambition to contribute significantly to society within the next five years
Profit-growth-ambition (PGA)
Ambition to increase profits within the next five years
52
Table A2: Descriptive statistics independent variables
OAW (N=536)
Employers (N=297)
(1)
Mean
(2)
S.D.
(3) (4)
Min Max
General risk taking propensity
General risk taking propensity
6.21
(2.08)
0
Gender
Female
0.36
(0.48)
Human capital
Age entrepreneur in number of years
Education
Previous employer experience
(9)
Difference
(5)
Mean
(6)
S.D.
(7)
Min
(8)
Max
10
6.91
(1.89)
0
10
0.71***
0
1
0.19
(0.39)
0
1
-0.17***
50.66 (10.18)
5.09 (1.23)
0.20 (0.40)
26
1
0
79
7
1
48.61
4.91
n.a.
(10.2)
(1.38)
n.a.
25
1
n.a.
80
7
n.a.
-2.05***
-0.18*
n.a.
Prior occupation
Prior wage employee
Prior self-employed
Prior family business
Prior unemployed
Prior student
Prior housemaker
Prior pensioner
Prior volunteer
Prior other
0.62
0.20
0.01
0.04
0.06
0.004
0.002
0.002
0.06
(0.49)
(0.40)
(0.10)
(0.20)
(0.24)
(0.06)
(0.04)
(0.04)
(0.23)
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
0.58
0.23
0.04
0.01
0.07
0
0.003
0.01
0.06
(0.50)
(0.42)
(0.20)
(0.12)
(0.26)
(0.00)
(0.06)
(0.08)
(0.24)
0
0
0
0
0
0
0
0
0
1
1
1
1
1
0
1
1
1
-0.05
0.03
0.03***
-0.03**
0.01
-0.004
0.002
0.005
0.005
Working hours
Weekly working hours
41.96 (15.08)
0
100
52.75 (15.15)
5
100
10.79***
Venture characteristics
Venture age
Number of employees
Phase of hiring first employee
AFF sector
Industrial sector
Construction sector
Trade sector
Distribution sector
Hospitality sector
Communication sector
Financial institutions sector
Real estate sector
Business services sector
CSR sector
Other services sector
Other sectors
12.36 (11.23) 1
0
(0.00)
0
n.a.
n.a.
n.a.
0.004 (0.06)
0
0.004 (0.06)
0
0.04 (0.21)
0
0.02 (0.15)
0
0.01 (0.11)
0
0.02 (0.13)
0
0.15 (0.36)
0
0.02 (0.15)
0
0
(0.00)
0
0.25 (0.43)
0
0.15 (0.35)
0
0.19 (0.39)
0
0.13 (0.34)
0
94
0
n.a.
1
1
1
1
1
1
1
1
0
1
1
1
1
19.9 (20.45)
9.26 (15.83)
1.92 (1.04)
0.003 (0.06)
0.02 (0.15)
0.05 (0.21)
0.10 (0.30)
0.02 (0.13)
0.10 (0.31)
0.17 (0.38)
0.01 (0.12)
0.01 (0.12)
0.16 (0.37)
0.06 (0.24)
0.13 (0.34)
0.15 (0.36)
2
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
123
125
3
1
1
1
1
1
1
1
1
1
1
1
1
1
7.51***
n.a.
n.a.
-0.0004
0.02**
0.002
0.07***
0.01
0.09***
0.02
-0.01
0.01**
-0.09***
-0.08***
-0.05**
0.02
notes: n.a. means not applicable.
difference = mean (OAW) - mean (Employers); H0: diff = 0, H1: diff ≠ 0
* significant at 10% level; ** significant at 5% level; *** significant at 1% level
53
Table A3: Descriptive statistics independent variables entrepreneurial stage
(1)
Mean
General risk taking propensity
General risk taking propensity
Gender
Female
Human capital
Age entrepreneur in number of years
Education
Prior occupation
Prior wage employee
Prior self-employed
Prior family business
Prior unemployed
Prior student
Prior housemaker
Prior pensioner
Prior volunteer
Prior other
Working hours
Weekly working hours
Venture characteristics
Venture age
Stage 1 (N=378 )
(2)
(3)
S.D.
Min
(4)
Max
(5)
Mean
Stage 2 (N=84)
(6)
(7) (8)
S.D. Min Max
(9)
Mean
Stage 3 (N=84)
(10)
(11) (12)
S.D.
Min Max
(13)
Mean
Stage 4 (N=185)
(14)
(15)
(16)
S.D.
Min
Max
(17)
Test
5.992
(2.08)
0
10
6.93
(1.93)
2
10
6.06
(2.05)
0
9
7.30
(1.72)
1
10
**
0.38
(0.49)
0
1
0.26
(0.44)
0
1
0.25
(0.44)
0
1
0.14
(0.35)
0
1
***
53.01
5.10
(9.66)
(1.23)
28
1
79
7
43.38
5.24
(8.31)
(1.15)
28
2
66
7
52.09
4.71
(10.37)
(1.34)
25
1
79
7
46.47
5.03
(9.43)
(1.41)
27
1
72
7
*
0.60
0.21
0.01
0.05
0.06
0.005
0.003
0.003
0.05
(0.49)
(0.41)
(0.11)
(0.21)
(0.24)
(0.07)
(0.05)
(0.05)
(0.22)
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
0.68
0.17
0.00
0.01
0.06
0.00
0.00
0.00
0.08
(0.47)
(0.37)
(0)
(0.11)
(0.24)
(0)
(0)
(0)
(0.28)
0
0
0
0
0
0
0
0
0
1
1
0
1
1
0
0
0
1
0.56
0.21
0.07
0.02
0.05
0.00
0.00
0.01
0.07
(0.5)
(0.41)
(0.26)
(0.15)
(0.21)
(0)
(0)
(0.11)
(0.26)
0
0
0
0
0
0
0
0
0
1
1
1
1
1
0
0
1
1
0.60
0.24
0.03
0.01
0.08
0
0.005
0.01
0.03
(0.49)
(0.43)
(0.18)
(0.1)
(0.27)
(0)
(0.07)
(0.07)
(0.18)
0
0
0
0
0
0
0
0
0
1
1
1
1
1
0
1
1
1
***
***
*
***
***
***
***
39.92 (14.87)
0
100
47.92
(12.6)
15
90
48.96
(14.66)
8
90
55.00 (14.91)
5
100
13.89
1
94
7.45
(6.43)
1
33
23.14
(21.33)
2
113
17.87 (19.51)
2
123
(11.91)
notes: Test refers to Bartlett's test for equal variances.
H0: in all stages variances are equal, H1: at least two are different.
* significant at 10% level; ** significant at 5% level; *** significant at 1% level
***
54
Table A4: Descriptive statistics independent variables having ambition to become
Employer
General risk taking propensity
General risk taking propensity
Gender
Female
Human capital
Age entrepreneur in number of years
Education
Previous employer experience
Prior occupation
Prior wage employee
Prior self-employed
Prior family business
Prior unemployed
Prior student
Prior housemaker
Prior pensioner
Prior volunteer
Prior other
Working hours
Weekly working hours
Venture characteristics
Venture age
AFF sector
Industrial sector
Construction sector
Trade sector
Distribution sector
Hospitality sector
Communication sector
Financial institutions sector
Real estate sector
Business services sector
CSR sector
Other services sector
Other sectors
OAW without EGA (N=378)
(1)
(2)
(3)
(4)
Mean
S.D.
Min Max
OAW with EGA (N=84)
(5)
(6)
(7)
(8)
Mean S.D.
Min
Max
(9)
Difference
5.992
(2.08)
0
10
6.93
(1.93)
2
10
-0.94***
0.38
(0.49)
0
1
0.26
(0.44)
0
1
0.12**
53.01
5.10
0.18
(9.66)
(1.23)
(0.38)
28
1
0
79
7
1
43.38
5.24
0.26
(8.31)
(1.15)
(0.44)
28
2
0
66
7
1
9.63***
-0.13
-0.08
0.60
0.21
0.01
0.05
0.06
0.005
0.003
0.003
0.05
(0.49)
(0.41)
(0.11)
(0.21)
(0.24)
(0.07)
(0.05)
(0.05)
(0.22)
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
0.68
0.17
0.00
0.01
0.06
0.00
0.00
0.00
0.08
(0.47)
(0.37)
(0)
(0.11)
(0.24)
(0)
(0)
(0)
(0.28)
0
0
0
0
0
0
0
0
0
1
1
0
1
1
0
0
0
1
-0.08
0.05
0.01**
0.03**
0.004
0.01
0.003
0.003
-0.03
39.92 (14.87)
0
100
47.92
(12.6)
15
90
-7.99***
13.89
0.003
0.003
0.05
0.03
0.01
0.01
0.14
0.02
0
0.27
0.15
0.20
0.12
1
0
0
0
0
0
0
0
0
0
0
0
0
0
94
1
1
1
1
1
1
1
1
0
1
1
1
1
7.45
0.01
0.01
0.04
0.00
0.01
0.04
0.18
0.02
0.00
0.26
0.12
0.14
0.17
(6.43)
(0.11)
(0.11)
(0.19)
(0.00)
(0.11)
(0.19)
(0.39)
(0.15)
(0.00)
(0.44)
(0.33)
(0.35)
(0.37)
1
0
0
0
0
0
0
0
0
0
0
0
0
0
33
1
1
1
0
1
1
1
1
0
1
1
1
1
6.44***
-0.01
-0.01
0.01
0.03
0.001
-0.03
-0.04
0
0
0.003
0.03
0.06
-0.05
(11.91)
(0.05)
(0.05)
(0.21)
(0.17)
(0.11)
(0.10)
(0.35)
(0.15)
(0.00)
(0.44)
(0.35)
(0.4)
(0.32)
notes: difference = mean (OAW without EGA) - mean (OAW with EGA); H0: diff = 0, H1: diff ≠ 0
* significant at 10% level; ** significant at 5% level; *** significant at 1% level
55
Table A5: Descriptive statistics independent variables having ambition to hire
additional employees
Employers without EGA (N=74)
(1)
(2)
(3)
(4)
Mean
S.D.
Min Max
General risk taking propensity
General risk taking propensity
Gender
Female
Human capital
Age entrepreneur in number of
years
Education
Prior occupation
Prior wage employee
Prior self-employed
Prior family business
Prior unemployed
Prior student
Prior housemaker
Prior pensioner
Prior volunteer
Prior other
Working hours
Weekly working hours
Venture characteristics
Venture age
Number of employees
Phase of hiring first employee
AFF sector
Industrial sector
Construction sector
Trade sector
Distribution sector
Hospitality sector
Communication sector
Financial institutions sector
Real estate sector
Business services sector
CSR sector
Other services sector
Other sectors
Employers with EGA (N=177)
(5)
(6)
(7)
(8)
Mean
S.D.
Min Max
(9)
Difference
6.16
(2.08)
0
9
7.35
(1.69)
1
10
-1.19***
0.22
(0.41)
0
1
0.14
(0.35)
0
1
0.07
52.63
4.68
(10.37)
(1.37)
25
1
79
7
46.60
5.02
(9.48)
(1.43)
27
1
72
7
0.54
0.24
0.03
0.05
0.00
0.05
0
0
0.07
(0.5)
(0.43)
(0.16)
(0.23)
(0.00)
(0.23)
(0.00)
(0.00)
(0.25)
0
0
0
0
0
0
0
0
0
1
1
1
1
0
1
0
0
1
0.59
0.24
0.03
0.01
0.09
0
0.006
0.006
0.03
(0.49)
(0.43)
(0.17)
(0.11)
(0.28)
(0.00)
(0.08)
(0.08)
(0.18)
0
0
0
0
0
0
0
0
0
1
1
1
1
1
0
1
1
1
6.03***
-0.35*
0.00
-0.05
0.01
0.04
0.02
-0.03
0
-0.01
-0.01
0.03
49.24
(14.71)
8
90
54.83
(14.89)
5
100
-5.58***
2
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
113
35
3
0
1
1
1
1
1
1
1
1
1
1
1
1
16.50
11.77
1.73
0.01
0.02
0.03
0.09
0.01
0.10
0.24
0.02
0.01
0.16
0.06
0.13
0.14
(16.77)
(17.4)
(0.88)
(0.08)
(0.15)
(0.18)
(0.28)
(0.11)
(0.3)
(0.43)
(0.13)
(0.08)
(0.37)
(0.23)
(0.34)
(0.34)
2
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
98
100
3
1
1
1
1
1
1
1
1
1
1
1
1
1
7.92***
-7.46***
0.04
-0.01
-0.01
0.03
0.01
0.00
0.04
-0.16***
0.00
0.01
0.04
-0.02
0.01
0.05
24.417
4.31
1.77
0
0.01
0.07
0.10
0.01
0.14
0.08
0.01
0.01
0.20
0.04
0.14
0.19
(22.16)
(6.06)
(0.88)
(0.00)
(0.12)
(0.25)
(0.29)
(0.12)
(0.34)
(0.27)
(0.12)
(0.12)
(0.4)
(0.2)
(0.34)
(0.39)
notes: difference = mean (OAW) - mean (Employers); H0: diff = 0, H1: diff ≠ 0
* significant at 10% level; ** significant at 5% level; *** significant at 1% level
56
Table A6: Factors influencing (realized) ambition to hire (additional) employees
including sector dummies
Risk taking propensity
General risk taking propensity
Gender
Female
Human capital
Age entrepreneur in number of years
Age^2 entrepreneur
Education
Previous employer experience
Prior occupation
Prior wage employee
Prior self-employed
Prior family business
Prior unemployed
Prior student
Working hours
Weekly working hours
Venture characteristics
Venture age
Number of employees
Phase of hiring first employee
AFF sector
Industrial sector
Construction sector
Trade sector
Distribution sector
Hospitality sector
Communication sector
Financial institutions sector
Real estate sector
Business services sector
CSR sector
Other services sector
OAW (N=462)
DV = having ambition to
become Employer
(1)
(2)
Marginal effect (SE)
Employers (N=251)
DV = having ambition to
hire additional employees
(3)
(4)
Marginal effect (SE)
0.01557**
(0.00783)
0.05135***
(0.01644)
-0.07754**
(0.02926)
-0.04597
(0.08234)
-0.01017
-0.00001
0.00597
0.12303***
(0.01556)
(0.00016)
(0.01303)
(0.0551)
0.01257
-0.00020
0.01676
(0.0242)
(0.00024)
(0.0227)
0.00319
-0.00347
(0.06366)
(0.06783)
-0.04022
0.01109
(0.1038)
(0.11398)
-0.07939
-0.06563
(0.05044)
(0.04833)
-0.27932
0.04629
(0.31961)
(0.13022)
0.00290***
(0.00105)
0.00410**
(0.00202)
-0.00544**
(0.00225)
-0.00358**
0.01585***
0.02107
(0.00183)
(0.00405)
(0.03477)
-0.02575
0.09518
0.04831
(0.18011)
(0.39791)
(0.1054)
0.04639
0.35193
0.00206
-0.05579
(0.19229)
(0.30018)
(0.05448)
(0.062)
-0.02182
-0.01022
-0.00093
(0.04485)
(0.05198)
(0.05311)
0.02267
-0.03082
-0.01335
0.00764
-0.14096
0.13107
-0.19368
0.00478
-0.03918
0.15252
0.03570
(0.24987)
(0.1592)
(0.12266)
(0.22534)
(0.13339)
(0.07911)
(0.35778)
(0.2537)
(0.10865)
(0.07331)
(0.09803)
notes: * significant at 10% level; ** significant at 5% level; *** significant at 1% level
The regressions are probit regressions.
57
Table A7: Factors influencing remaining-active-ambition and self-employed-hiring/cooperation-ambition
Risk taking propensity
General risk taking propensity
Gender
Female
Human capital
Age entrepreneur in number of years
Age^2 entrepreneur
Education
Previous employer experience
Prior occupation
Prior wage employee
Prior self-employed
Prior unemployed
Prior student
Working hours
Weekly working hours
Venture characteristics
Venture age
Number of employees
Phase of hiring first employee
OAW (N=489)
DV = remaining-activeambition
(1)
(2)
Marginal effect (SE)
Employers (N=252)
DV = remaining-activeambition
(3)
(4)
Marginal effect (SE)
OAW (N=454)
DV = self-employedhiring/cooperation-ambition
(5)
(6)
Marginal effect (SE)
Employers (N=245)
DV = self-employedhiring/cooperation-ambition
(7)
(8)
Marginal effect (SE)
0.00436
(0.00499)
0.00806
(0.00721)
0.03957***
(0.01296)
0.04321**
(0.01795)
-0.00173
(0.022)
-0.02796
(0.04988)
-0.01324
(0.05528)
-0.07920
(0.09141)
0.02516***
-0.00030***
0.00132
-0.05241*
(0.00913)
(0.00009)
(0.00807)
(0.03376)
0.02587***
-0.00029***
0.00824
(0.01046)
(0.0001)
(0.00972)
-0.03595
0.00015
0.00433
0.27060***
(0.02624)
(0.00026)
(0.02229)
(0.06143)
-0.00418
0.00004
0.02546
(0.02752)
(0.00027)
(0.02521)
0.00292
0.03733
(0.03068)
(0.02452)
-0.01892
(0.05942)
0.00317
0.02665
-0.35780*
0.02486
(0.04715)
(0.04018)
(0.29313)
(0.04638)
-0.09048
-0.04029
0.04589
-0.08404
(0.11226)
(0.12113)
(0.15877)
(0.14961)
0.07261
0.19414
-0.28501
0.10299
(0.12332)
(0.11428)
(0.27948)
(0.14961)
0.00096
(0.00075)
0.00017
(0.00095)
0.00731***
(0.00186)
0.00589**
(0.00233)
0.00018
(0.00092)
-0.00038
0.00050
0.00745
(0.00076)
(0.0013)
(0.01678)
-0.00924***
(0.00308)
-0.00586***
0.00596
0.04138
(0.00218)
(0.00300)
(0.04014)
notes: * significant at 10% level; ** significant at 5% level; *** significant at 1% level.
The regressions are probit regressions.
58
Table A8: Factors influencing profit-growth-ambition
Risk taking propensity
General risk taking propensity
Gender
Female
Human capital
Age entrepreneur in number of years
Age^2 entrepreneur
Education
Previous employer experience
Prior occupation
Prior wage employee
Prior self-employed
Prior unemployed
Prior student
Prior other
Working hours
Weekly working hours
Venture characteristics
Venture age
Number of employees
Phase of hiring first employee
OAW (N=460)
DV = profit-growth-ambition
(1)
(2)
Marginal effect
(SE)
Employers (N=255)
DV = profit-growth-ambition
(3)
(4)
Marginal effect
(SE)
0.01047
(0.00893)
0.01020*
(0.00625)
0.02267
(0.03877)
0.00644
(0.03071)
0.01521
-0.00028
0.01930
0.11840***
(0.01882)
(0.00018)
(0.01506)
(0.0367)
-0.00544
0.00003
-0.00417
(0.01063)
(0.0001)
(0.00939)
0.05582
0.05850
0.13472
-0.07623
(0.07995)
(0.06996)
(0.05234)
(0.12267)
-0.03623
-0.06753
-0.51526**
-0.12099
(0.04304)
(0.07952)
(0.31425)
(0.14893)
0.00539***
(0.00138)
0.00122
(0.00086)
-0.00334*
(0.00183)
-0.00183***
0.00232
0.00887
(0.00073)
(0.00131)
(0.01458)
notes: * significant at 10% level; ** significant at 5% level; *** significant at 1% level.
The regressions are probit regressions.
59
Table A9: Descriptive statistics independent variables remaining-active-ambition
OAW without RAA (N=42)
(1)
(2)
(3)
(4)
Mean S.D. Min Max
General risk taking propensity
General risk taking propensity
Gender
Female
Human capital
Age entrepreneur in number of years
Education
Previous employer experience
Prior occupation
Prior wage employee
Prior self-employed
Prior family business
Prior unemployed
Prior student
Prior housemaker
Prior pensioner
Prior volunteer
Prior other
Working hours
Weekly working hours
Venture characteristics
Venture age
Number of employees
Phase of hiring first employee
OAW with RAA (N=447)
(5)
(6)
(7)
(8)
Mean S.D. Min Max
Employers without RAA (N=20)
(9)
(10)
(11)
(12)
Mean S.D.
Min
Max
Employers with RAA (N=232)
(13)
(14)
(15)
(16)
Mean
S.D.
Min
Max
5.881
(2.03)
2
9
6.35
(2.02)
0
10
6.35
(2.5)
0
9
7.03
(1.82)
1
10
0.31
(0.47)
0
1
0.36
(0.48)
0
1
0.20
(0.41)
0
1
0.15
(0.36)
0
1
60.07 (10.76)
5.05 (1.23)
0.36 (0.48)
28
1
0
79
7
1
49.29 (9.23)
5.09 (1.24)
0.18 (0.39)
26
1
0
70
7
1
56.35 (12.62)
4.75 (1.59)
n.a.
n.a.
25
1
n.a.
70
7
n.a.
47.81
4.95
n.a.
(9.36)
(1.40)
n.a.
27
1
n.a.
80
7
n.a.
0.62
0.14
0.05
0
0.05
0.02
0.02
0
0.10
(0.49)
(0.35)
(0.22)
(0.00)
(0.22)
(0.15)
(0.15)
(0.00)
(0.3)
0
0
0
0
0
0
0
0
0
1
1
1
0
1
1
1
0
1
0.62
0.21
0.01
0.05
0.07
0
0
0
0.05
(0.49)
(0.4)
(0.08)
(0.22)
(0.25)
(0.00)
(0.00)
(0.05)
(0.22)
0
0
0
0
0
0
0
0
0
1
1
1
1
1
0
0
1
1
0.45
0.20
0.10
0.10
0.05
0
0
0
0.10
(0.51)
(0.41)
(0.31)
(0.31)
(0.22)
(0.00)
(0.00)
(0.00)
(0.31)
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
0.59
0.23
0.04
0.01
0.08
0
0.004
0.004
0.04
(0.49)
(0.42)
(0.19)
(0.09)
(0.27)
(0.00)
(0.07)
(0.07)
(0.19)
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
35.88 (15.22)
0
70
43.27 (14.4)
1
100
48.04 (15.90)
10
70
53.79 (14.46)
5
100
85
0
n.a.
11.59 (9.97) 1
0
(0.00) 0
n.a.
n.a. n.a.
94
0
n.a.
26.65 (27.17)
6.80 (7.59)
1.80 (0.89)
2
1
1
100
25
3
18.37 (17.58)
9.63 (15.42)
1.75 (0.88)
2
1
1
113
100
3
18.10 (18.81) 1
0
(0.00)
0
n.a.
n.a.
n.a.
notes: n.a. means not applicable
RAA refers to ambition to remain active as an entrepreneur
60
Table A10: Descriptive statistics independent variables self-employed-hiring/cooperation-ambition
OAW without SECA (N=216)
(1)
(2)
(3)
(4)
Mean
S.D. Min Max
General risk taking propensity
General risk taking propensity
Gender
Female
Human capital
Age entrepreneur in number of years
Education
Previous employer experience
Prior occupation
Prior wage employee
Prior self-employed
Prior family business
Prior unemployed
Prior student
Prior housemaker
Prior pensioner
Prior volunteer
Prior other
Working hours
Weekly working hours
Venture characteristics
Venture age
Number of employees
Phase of hiring first employee
OAW with SECA (N=238)
(5)
(6)
(7)
(8)
Mean S.D. Min Max
Employers without SECA (N=97)
(9)
(10)
(11)
(12)
Mean
S.D.
Min
Max
Employers with SECA (N=148)
(13)
(14)
(15)
(16)
Mean
S.D.
Min
Max
5.76
(2.09)
0
9
6.64
(2.07)
0
10
6.47
(2.11)
0
10
7.32
(1.7)
2
10
0.37
(0.48)
0
1
0.36
(0.48)
0
1
0.22
(0.41)
0
1
0.14
(0.35)
0
1
54.74
5.08
0.15
(9.48)
(1.19)
(0.36)
33
1
0
78
7
1
46.49
5.13
0.24
(8.96)
(1.25)
(0.43)
26
1
0
79
7
1
50.64
4.75
n.a.
(10.09)
(1.44)
n.a.
25
1
n.a.
70
7
n.a.
47.78
5.07
n.a.
(9.87)
(1.38)
n.a.
29
1
n.a.
80
7
n.a.
0.62
0.20
0.01
0.04
0.06
0.01
0.01
0
0.05
(0.49)
(0.4)
(0.12)
(0.2)
(0.24)
(0.1)
(0.07)
(0.00)
(0.22)
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
0
1
0.62
0.18
0.01
0.05
0.08
0
0
0.004
0.06
(0.49)
(0.39)
(0.09)
(0.22)
(0.27)
(0.00)
(0.00)
(0.06)
(0.24)
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
0.56
0.18
0.07
0.03
0.08
0
0
0.01
0.07
(0.5)
(0.38)
(0.26)
(0.17)
(0.28)
(0.00)
(0.00)
(0.1)
(0.26)
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
0.58
0.27
0.02
0.01
0.08
0
0.007
0
0.03
(0.5)
(0.45)
(0.14)
(0.08)
(0.27)
(0.00)
(0.08)
(0.00)
(0.18)
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
38.23
(15.07)
1
100
46.16 (13.94)
0
90
49.52
(16.57)
5
98
55.57
(13.32)
20
100
15.64
0
n.a.
(13.18) 1
(0.00)
0
n.a.
n.a.
94
0
n.a.
9.70
0
n.a.
1
0
n.a.
43
0
n.a.
24.40
7.09
1.76
(23.38)
(9.57)
(0.89)
2
1
1
113
60
3
15.78
10.55
1.76
(14.01)
(16.69)
(0.89)
2
1
1
83
100
3
(8.39)
(0.00)
n.a.
notes: n.a. means not applicable
SECA refers to ambition to increase turnover by hiring other self-employed and/or cooperate with other self-employed
61
Table A11: Descriptive statistics independent variables profit-growth-ambition
OAW without PGA (N=99)
(1)
(2)
(3)
(4)
Mean S.D. Min Max
General risk taking propensity
General risk taking propensity
Gender
Female
Human capital
Age entrepreneur in number of years
Education
Previous employer experience
Prior occupation
Prior wage employee
Prior self-employed
Prior family business
Prior unemployed
Prior student
Prior housemaker
Prior pensioner
Prior volunteer
Prior other
Working hours
Weekly working hours
Venture characteristics
Venture age
Number of employees
Phase of hiring first employee
OAW with PGA (N=361)
(5)
(6)
(7)
(8)
Mean S.D. Min Max
Employers without PGA (N=22)
(9)
(10)
(11)
(12)
Mean
S.D.
Min
Max
Employers with PGA (N=233)
(13)
(14)
(15)
(16)
Mean
S.D.
Min
Max
5.70
(2.23)
0
9
6.39
(2.04)
0
10
6.09
(2.54)
0
10
7.14
(1.8)
1
10
0.31
(0.47)
0
1
0.36
(0.48)
0
1
0.18
(0.39)
0
1
0.16
(0.37)
0
1
56.86 (10.02)
4.94 (1.27)
0.16 (0.37)
28
2
0
79
7
1
48.05
5.13
0.22
(9.03)
(1.23)
(0.41)
28
1
0
70
7
1
55.83
5.09
n.a.
(9.69)
(1.34)
n.a.
33
2
n.a.
70
7
n.a.
47.35
4.91
n.a.
(9.71)
(1.42)
n.a.
25
1
n.a.
80
7
n.a.
0.60
0.19
0.02
0.02
0.08
0.02
0.01
0
0.06
(0.49)
(0.4)
(0.14)
(0.14)
(0.27)
(0.14)
(0.1)
(0.00)
(0.24)
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
0
1
0.64
0.19
0.01
0.04
0.06
0
0
0.003
0.05
(0.48)
(0.4)
(0.07)
(0.21)
(0.24)
(0.00)
(0.00)
(0.05)
(0.22)
0
0
0
0
0
0
0
0
0
1
1
1
1
1
0
0
1
1
0.46
0.27
0.09
0.09
0.09
0
0
0
0
(0.51)
(0.46)
(0.29)
(0.29)
(0.29)
(0.00)
(0.00)
(0.00)
(0.00)
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
0.59
0.23
0.03
0.01
0.07
0
0.004
0.004
0.05
(0.49)
(0.42)
(0.18)
(0.09)
(0.26)
(0.00)
(0.07)
(0.07)
(0.22)
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
35.26 (12.95)
0
70
44.33 (14.94)
1
100
46.49
(16.65)
8
70
9.85
(15.39)
1
100
80
0
n.a.
10.63 (10.16) 1
0
(0.00)
0
n.a.
n.a.
n.a.
94
0
n.a.
34.77
5.55
1.86
(31.92)
(12.46)
(0.89)
4
1
1
113
60
3
16.63
9.85
1.75
(15.21)
(15.39)
(0.88)
2
1
1
98
100
3
16.47 (12.36) 1
0
(0.00)
0
n.a.
n.a.
n.a.
notes: n.a. means not applicable
PGA refers to ambition to increase profit
62
Appendix II
Survey (Dutch)
Geachte heer, mevrouw,
Het Amsterdam Center for Enterpreneurship (ACE) en Bureau Onderzoek en Statistiek (O+S)
doen samen onderzoek doen naar ondernemerschap. Dit is de tweede vragenlijst die
gezamenlijk is opgesteld.
Groeiambitie en update achtergrondgegevens ondernemerspanel
Het thema van deze vragenlijst is de groeiambitie van de Amsterdamse ondernemers. De
vragenlijst bevat daarnaast ook enkele vragen naar uw achtergrondgegevens, bijvoorbeeld de
sector waarin u werkzaam bent en de omvang van de onderneming. Deze vragen zijn
toegevoegd omdat wij uw achtergrondgegevens jaarlijks willen updaten.
De resultaten van het onderzoek zijn over enkele maanden beschikbaar.
Graag willen wij u uitnodigen om aan het onderzoek mee te werken. U kunt de vragenlijst
starten door op onderstaande link te klikken. <link>
Resultaten eerste vragenlijst ACE en O+S
Uit de resultaten van het eerste onderzoek, met als thema ‘de start van de onderneming’,
blijkt dat starten in een team succesvoller is dan alleen starten. Een tweede conclusie was dat
mannen succesvoller zijn als ondernemer dan vrouwen. U kunt een uitgebreidere versie van
de resultaten van dit onderzoek nalezen op <link>.
Vragen?
Uw antwoorden worden anoniem verwerkt. Het invullen van de vragenlijst neemt ongeveer 5
minuten in beslag. De invulsessie kan op ieder moment worden afgebroken. Wanneer u
opnieuw inlogt gaat u automatisch verder waar u was gebleven.
Indien u technische vragen heeft over het onderzoek kunt u contact opnemen met O+S
([email protected]).
Alvast hartelijk bedankt voor uw medewerking!
Met vriendelijke groet, het online onderzoeksteam van O+S
Bureau Onderzoek en Statistiek van de gemeente Amsterdam
63
A1.
Hoe beschouwt u uzelf: Bent u in het algemeen iemand die volledig bereid is om
risico’s te nemen of probeert u het nemen van risico’s te vermijden?
Geef uw antwoord aan op onderstaande schaal waarbij waarde 0 de betekenis heeft
“Absoluut geen bereidheid risico’s te nemen” en waarde 10 de betekenis heeft
“Volledige bereidheid risico’s te nemen”.
0
A2.
1
2
3
4
5
6
7
8
9
10
Hoeveel werknemers heeft uw onderneming nu in dienst?
Exclusief u en eventuele compagnon(s). Indien u geen werknemers in dienst heeft,
geeft u dit aan door ‘0’ in te vullen. Als u het aantal niet precies weet, probeer dan
een zo’n goed mogelijke schatting te maken.
[……] persoon / personen
□ geen antwoord
A3.
Bent u (mede-)eigenaar van de onderneming?
□ Ja, ik ben eigenaar
□ Ja, ik ben mede-eigenaar
□ Nee, mijn functie is [……] <Ga naar vraag Z1>
< Op basis van vraag A3 wordt bepaald welke vervolgvragen (mede-)eigenaren krijgen:
In geval van 0 werknemers: ga naar vraag G1
In geval van 1 of meerdere werknemers: ga naar vraag W1 >
G1.
U geeft aan dat er niemand anders in uw bedrijf werkt dan uzelf. O+S definieert
net als het CBS een zzp'er als volgt.
Een zzp'er is iemand die als (hoofd)baan voor eigen rekening of risico werkt,
ofwel in een eigen bedrijf of praktijk, ofwel in een zelfstandig uitgeoefend beroep
en die daarbij geen personeel in dienst heeft. Een zzp'er kan daarnaast ook in
loondienst werkzaam zijn. Een zzp'er werkt voor één of meerdere opdrachtgevers
en alleen na opdrachtverstrekking. Een zzp'er werkt niet in de detailhandel.
Zou u zichzelf omschrijven als ZZP'er (zelfstandige zonder personeel)?
□ 1. ja
□ 2. nee
□ 3. weet ik niet
64
G2.
Er volgt nu een aantal stellingen. Kunt u per stelling aangeven of de stelling op u
van toepassing is?
Mijn ambitie is om…
1
2
3
4
5
Ja
Nee
Weet ik
niet/ geen
antwoord
over 5 jaar nog steeds actief te zijn met mijn onderneming
binnen nu en 5 jaar één of meerdere werknemer(s)
aan te nemen
in de komende 5 jaar een omzetstijging te realiseren door het inlenen van
diensten van andere zelfstandigen en/of door samenwerking met andere
zelfstandigen voor het uitvoeren van projecten
in de komende 5 jaar een significante bijdrage te leveren aan onze
maatschappij
in de komende 5 jaar mijn winst te vergroten
<Het antwoord op G2 – stelling 2 bepaalt welke vervolgvraag de respondent krijgt:
JA: ga naar vraag G3 en aansluitend G4
NEE: ga naar vraag G5
WEET IK NIET: ga naar vraag G6 >
G3.
<Follow-up: Alleen voor respondenten die vraag G2 – stelling 2 met JA
beantwoordden>
Wat zijn voor u de voornaamste redenen om één of meerdere werknemer(s) aan
te willen nemen?
[……]
□ Geen antwoord
G4.
Het kan zijn dat u belemmeringen ondervindt bij het uitvoeren van uw ambitie
om werknemers aan te nemen. Kunt u per onderstaande belemmering aangeven
of dit op u van toepassing is?
Bij het realiseren van mijn ambitie om (een) werknemer(s) aan
te nemen ervaar ik als belemmering …
dat ik onvoldoende extra omzetmogelijkheden zie
dat ik hiervoor over onvoldoende eigen financiële middelen beschik
dat ik hiervoor onvoldoende toegang heb tot externe financiële
middelen
de wetgeving omtrent ontslagbescherming
de wetgeving omtrent loondoorbetaling bij ziekte en
arbeidsongeschiktheid
de bijkomende administratieve lasten
dat ik geen geschikte werknemer(s) kan vinden
Ervaart u nog een andere belemmering?
□ 1. Ja, namelijk [……]
□ 2. Nee
<ga naar vraag G7>
Ja
Nee
Weet ik
niet/ geen
antwoord
65
G5.
<Follow-up: Alleen voor respondenten die vraag G2 – stelling 2 met NEE
beantwoordden>
Er volgt nu een aantal mogelijke belemmeringen bij het aannemen van
werknemers. Kunt u per belemmering aangeven of dit op u van toepassing is?
Ik wil geen werknemer(s) aannemen omdat ik.…
Ja
Nee
Weet ik
niet/ geen
antwoord
tevreden ben met de huidige situatie
totale controle wil houden over mijn onderneming
geen verantwoordelijkheid wil dragen over personeelsleden
onvoldoende extra omzetmogelijkheden zie
hiervoor over onvoldoende eigen financiële middelen beschik
hiervoor onvoldoende toegang heb tot externe financiële middelen
de wetgeving omtrent ontslagbescherming als belemmering zie
de wetgeving omtrent loondoorbetaling bij ziekte en
arbeidsongeschiktheid als belemmering zie
de bijkomende administratieve lasten als belemmering zie
geen geschikte werknemer(s) kan vinden
Ervaart u nog een andere belemmering?
□ 1. Ja, namelijk [……]
□ 2. Nee
<ga naar vraag G7>
G6:
<Follow-up: Alleen voor respondenten die vraag G2 – stelling 2 met Weet ik niet
beantwoordden>
Er volgt nu een aantal mogelijke belemmeringen bij het in aannemen van
werknemers. Kunt u per belemmering aangeven of dit op u van toepassing is?
Ik zou geen werknemer(s) willen aannemen omdat ik.…
tevreden ben met de huidige situatie
totale controle wil houden over mijn onderneming
geen verantwoordelijkheid wil dragen over personeelsleden
onvoldoende extra omzetmogelijkheden zie
hiervoor over onvoldoende eigen financiële middelen beschik
hiervoor onvoldoende toegang heb tot externe financiële middelen
de wetgeving omtrent ontslagbescherming als belemmering zie
de wetgeving omtrent loondoorbetaling bij ziekte en
arbeidsongeschiktheid als belemmering zie
de bijkomende administratieve lasten als belemmering zie
geen geschikte werknemer(s) kan vinden
Ervaart u nog een andere belemmering?
□ 1. Ja, namelijk [……]
□ 2. Nee
Ja
Nee
Weet ik
niet/ geen
antwoord
66
G7.
Heeft u in uw huidige onderneming of in een eerdere onderneming werknemers
in dienst gehad?
□ 1. ja
□ 2. nee
<ga naar vraag Z1>
VRAGENLIJST BIJ 1 OF MEER WERKNEMERS
W1.
In welke fase van de onderneming is de eerste werknemer in dienst genomen?
(Exclusief u en eventuele compagnon(s))
De eerste werknemer is in dienst genomen:
□ 1. binnen een jaar na de oprichting van de onderneming
□ 2. tussen 1 en 2 jaar na de oprichting van de onderneming
□ 3. later dan 2 jaar na oprichting van de onderneming
□ 4. weet ik niet
W2.
Er volgt nu een aantal stellingen. Kunt u per stelling aangeven of de stelling op u
van toepassing is?
Mijn ambitie is om…
Ja
Nee
Weet ik
niet/ geen
antwoord
1 over 5 jaar nog steeds actief te zijn met mijn onderneming
2 binnen nu en 5 jaar één of meerdere extra
werknemer(s) aan te nemen
3 in de komende 5 jaar een omzetstijging te realiseren door het inlenen van
4
5
diensten van andere zelfstandigen en/of door samenwerking met andere
zelfstandigen voor het uitvoeren van projecten
in de komende 5 jaar een significante bijdrage te leveren aan onze
maatschappij
in de komende 5 jaar mijn winst te vergroten
<Het antwoord op W2 – stelling 2 bepaalt welke vervolgvraag de respondent krijgt:
JA: ga naar vraag W3 en aaneensluitend W4
NEE: ga naar vraag W5
WEET IK NIET: ga naar vraag W6 >
W3.
<Follow-up: Alleen voor respondenten die vraag W2 – stelling 2 met JA
beantwoordden>
Wat zijn voor u de voornaamste redenen om één of meerdere extra werknemer(s)
aan te willen nemen?
[……]
□ Geen antwoord
67
W4.
Het kan zijn dat u belemmeringen ondervindt bij het uitvoeren van uw ambitie
om extra werknemers aan te nemen. Kunt u per onderstaande belemmering
aangeven of u dit op u van toepassing is?
Bij het realiseren van mijn ambitie om (een) extra
werknemer(s) in dienst te nemen ervaar ik als belemmering …
Ja
Nee
Weet ik
niet/ geen
antwoord
dat ik onvoldoende extra omzetmogelijkheden zie
dat ik hiervoor over onvoldoende eigen financiële middelen beschik
dat ik hiervoor onvoldoende toegang heb tot externe financiële
middelen
de wetgeving omtrent ontslagbescherming
de wetgeving omtrent loondoorbetaling bij ziekte en
arbeidsongeschiktheid
de bijkomende administratieve lasten
dat ik geen geschikte werknemer(s) kan vinden
Ervaart u nog een andere belemmering?
□ 1. Ja, namelijk [……]
□ 2. Nee
<ga naar vraag Z1>
W5.
<Follow-up: Alleen voor respondenten die vraag W2 – stelling 2 met NEE
beantwoordden>
Er volgt nu een aantal mogelijke belemmeringen bij het aannemen van extra
werknemers. Kunt u per belemmering aangeven of dit voor u van toepassing is?
Ik wil geen extra werknemer(s) aannemen omdat ik.…
tevreden ben met de huidige situatie
onvoldoende extra omzetmogelijkheden zie
hiervoor over onvoldoende eigen financiële middelen beschik
hiervoor onvoldoende toegang heb tot externe financiële middelen
de wetgeving omtrent ontslagbescherming als belemmering zie
de wetgeving omtrent loondoorbetaling bij ziekte en
arbeidsongeschiktheid als belemmering zie
de bijkomende administratieve lasten als belemmering zie
geen geschikte werknemer(s) kan vinden
Ervaart u nog een andere belemmering?
□ 1. Ja, namelijk [……]
□ 2. Nee
<ga naar vraag Z1>
Ja
Nee
Weet ik
niet/ geen
antwoord
68
W6.
<Follow-up: Alleen voor respondenten die vraag W2 – stelling 2 met Weet ik niet
beantwoordden>
Er volgt nu een aantal mogelijke belemmeringen bij het aannemen van extra
werknemers. Kunt u per belemmering aangeven of dit op u van toepassing is?
Ik zou geen extra werknemer(s) willen aannemen omdat ik.…
Ja
Nee
Weet ik
niet/ geen
antwoord
tevreden ben met de huidige situatie
onvoldoende extra omzetmogelijkheden zie
hiervoor over onvoldoende eigen financiële middelen beschik
hiervoor onvoldoende toegang heb tot externe financiële middelen
de wetgeving omtrent ontslagbescherming als een te grote
belemmering zie
de wetgeving omtrent loondoorbetaling bij ziekte en
arbeidsongeschiktheid als een te grote belemmering zie
de bijkomende administratieve lasten als belemmering zie
geen geschikte werknemer(s) kan vinden
Ervaart u nog een andere belemmering?
□ 1. Ja, namelijk [……]
□ 2. Nee
VRAGEN VOOR ALLE RESPONDENTEN
Z1.
Hoeveel uur per week besteedt u gemiddeld aan de onderneming?
Als u het niet precies weet, probeer dan een zo’n goed mogelijke schatting te maken.
[ ……] uur per week
Z2.
Wat WAS uw belangrijkste bezigheid direct voorafgaand aan uw huidige
functie?
□
□
□
□
□
□
□
□
□
□
□
Z3.
1. werkzaam in loondienst, gesalarieerd
2. zelfstandig werkzaam, freelance
3. meewerkend in familiebedrijf
4. werkloos, werkzoekend, wachtgeld
5. onderwijs volgend, studerend
6. huisvrouw, huisman
7. AOW, gepensioneerd, rentenierend, VUT
8. vrijwilligerswerk
9. anders
10. weet niet
11. geen antwoord
Kunt u aangeven in welke branche u werkzaam bent?
□ 1. Landbouw, bosbouw en visserij
□ 2. Winning van delfstoffen
□ 3. Industrie
69
□ 4. Productie en distributie van en handel in elektriciteit, aardgas, stoom en
gekoelde lucht
□ 5. Winning en distributie van water; afval- en afvalwaterbeheer en sanering
□ 6. Bouwnijverheid
□ 7. Groot- en detailhandel; reparatie van auto’s
□ 8. Vervoer en opslag
□ 9. Logies-, maaltijd- en drankverstrekking
□ 10. Informatie en communicatie
□ 11. Financiële instellingen
□ 12. Verhuur van en handel in onroerend goed
□ 13. Advisering, onderzoek en overige specialistische zakelijke dienstverlening
□ 14. Verhuur van roerende goederen en overige zakelijke dienstverlening
□ 15. Cultuur, sport en recreatie
□ 16. Overige dienstverlening
□ 17. Anders
□ 18. Geen antwoord
Z4.
In 2011 is de Economic Development Board Amsterdam (EDBA) opgericht. Dit
samenwerkingsverband van het bedrijfsleven, kennisinstellingen en de overheid
wil de economische ontwikkeling en het innovatief vermogen van de regio
bevorderen.
De EDBA onderscheidt zeven clusters. Behoort uw onderneming tot een van deze
clusters en zo ja tot welke:
□
□
□
□
□
□
□
□
Z5.
1. Creatieve industrie
2. Financiële- en zakelijke dienstverlening
3. ICT/eScience
4. Life Sciences
5. Handel & logistiek
6. Food & flowers
7. Toerisme & congressen
8. Nee
Wat is uw hoogst voltooide opleiding?
(Als u uw opleiding in het buitenland hebt gevolgd, zou u dan de Nederlandse
equivalent hiervan willen aangeven?)
□
□
□
□
□
□
□
□
□
1. lager onderwijs
2. voorbereidend middelbaar beroepsonderwijs (vmbo, voorheen mavo/vbo)
3. hoger algemeen en voorbereidend wetenschappelijk onderwijs (havo/vwo)
4. middelbaar beroepsonderwijs (mbo)
5. hoger beroepsonderwijs (hbo)
6. wetenschappelijk onderwijs (drs, master, bachelor)
7. wetenschappelijk onderwijs (dr)
8. weet niet
9. wil niet zeggen
70
Z6.
Bent u een
□ 1. man
□ 2. vrouw
□ 3. wil ik niet zeggen
Z7.
Wat is uw leeftijd?
[……]
In deze vragenlijst zijn verschillende onderwerpen aan bod gekomen. Wellicht
zijn er onderwerpen die niet in deze vragenlijst aan de orde zijn geweest, maar
waarover u wel graag iets kwijt zou willen.
Ook suggesties voor verbetering zijn welkom. Deze kunt u hieronder beschrijven.
[…….]
Dit waren alle vragen. Hartelijk dank voor uw medewerking.
Door op 'Vorige' te klikken kunt u uw antwoorden bekijken. Door op 'Verzend'
te klikken verzendt u uw antwoorden en sluit u de vragenlijst af. De vragenlijst
kan hierna niet meer worden geopend.