Determinants of Self-employment With and Without Employees: Empirical Findings from Europe

Abstract

Although the determinants of self-employment are widely studied, the distinction between self-employed workers with and without employees is not often applied. We study determinants of self-employment in Europe by utilising three waves of the European Survey on Working Conditions (2005, 2010 and 2015) and estimating the individual odds of being self-employed with and without employees. We show that there are considerable differences concerning variables such as age, education, and household situation, where we found different patterns for solo self-employed workers and employer entrepreneurs. Among other findings, we show that jobs are created by middle-aged individuals who on average work more hours, have more experience in their own firm and who attained higher levels of education (bachelor, master and doctoral level). Our study contributes to replication of earlier research in the relatively underexplored area of the determinants of self-employment with and without employees.

Full text

International Review of Entrepreneurship, Article #1587, 16(3): pp. 405-426.
© 2018, Senate Hall Academic Publishing.
Determinants of Self-Employment with
and Without Employees: Empirical
Findings from Europe
Ond ej Dvouletý1
Department of Entrepreneurship, University of Economics, Prague, Czech Republic
Abstract. Although the determinants of self-employment are widely studied, the distinction
between self-employed workers with and without employees is not often applied. We study
determinants of self-employment in Europe by utilising three waves of the European Survey on
Working Conditions (2005, 2010 and 2015) and estimating the individual odds of being self-
employed with and without employees. We show that there are considerable differences concerning
variables such as age, education, and household situation, where we found different patterns for solo
self-employed workers and employer entrepreneurs. Among other findings, we show that jobs are
created by middle-aged individuals who on average work more hours, have more experience in their
own firm and who attained higher levels of education (bachelor, master and doctoral level). Our
study contributes to replication of earlier research in the relatively underexplored area of the
determinants of self-employment with and without employees.
Keywords: determinants of self-employment, self-employed with employees, self-employed
without employees, solo-self-employed, job creators, employer entrepreneurs, human capital,
European Survey on Working Conditions (EWCS), determinants of entrepreneurship.
JEL codes: L26; M51; J21
Acknowledgement and Funding: An earlier version of the paper was presented at the Global
Research Workshop on Freelancing and Self-Employment, held in London in April, 2018. The
author thanks André van Stel and José María Millán for their helpful and encouraging comments
and suggestions. This work was supported by Internal Grant Agency of Faculty of Business
Administration, University of Economics in Prague, under no. IP300040.
1. Introduction
Entrepreneurship as a field is still growing, and that means there are still many
research challenges that are important for both policymakers and researchers
(Parker, 2009; Dale, 2015). One of the important topics is the research on
determinants of entrepreneurship and self-employment at country, regional and
individual levels. Recent empirical studies show that it is important to distinguish
between various forms of entrepreneurial activity (Stam and Van Stel, 2011; Van
1. Correspondence: Ond ej Dvouletý, Department of Entrepreneurship, Faculty of Business
Administration, University of Economics, Prague, W. Churchill Sq. 4, 130 67 Prague 3, Czech
Republic. Phone: +420 224 09 8753. Email: ondrej.dvoulety@vse.cz
ř
ř
© 2018, Senate Hall Academic Publishing. All Rights Reserved

406 Are the Solo-Self-Employed Different Individuals from Job Creators?
Stel et al., 2014; Cieslik, 2015; Sevä et al., 2016; Jansen, 2017; Dilli et al., 2018;
Szaban and Skrzek-Lubasiska, 2018; Hessels et al., 2018), because they may
differently affect regional economic development. In the economy, there are self-
employed/entrepreneurs2 with and without employees who might have different
motivations and goals. We may observe high-growth entrepreneurs, own-account
workers and freelancers, necessity entrepreneurs or even social entrepreneurs
(e.g. Audretsch et al., 2015; Fritsch and Storey, 2017; Dvouletý, 2017; 2018).
Moreover, an ongoing research agenda on freelancing and self-employment finds
that not all entrepreneurs and self-employed want to hire employees (Stanworth
and Stanworth, 1995; Burke, 2015a; 2015b; Bögenhold and Klinglmair, 2016)
and thus, it is very relevant to study factors that affect the decision of own-account
workers to hire employees (Cowling et al., 2004; Millán et al., 2014a; 2014b;
Kraaij and Elbers, 2016; Petrescu, 2016; Caliendo et al., 2017; Fairlie and
Miranda, 2017).
In this study, we aim for diving deeper into individual characteristics
determining the choice of being an employer. Previously obtained empirical
evidence is rather scarce. Besides the one-country based studies (e.g. Cowling et
al., 2004 for the UK), research was driven mainly by the data from two large
European surveys, the European Community Household Panel (ECHP) and the
European Social Survey (ESS). Millán et al. (2014a) utilised in their work data
from the ECHP, and they focused mainly on the demographic and individual-
related characteristics of the self-employed with and without employees. Petrescu
(2016) concentrated in her work mainly on psychological factors. For this
purpose, she utilised data from the ESS. The present study aims to contribute to
the current state of knowledge from the angle of the third large European survey
- the European Survey on Working Conditions (EWCS). We utilise three waves
of EWCS (2005, 2010 and 2015) and we aim to identify the differences between
those self-employed having employees and those without them. We analyse the
established determinants of entrepreneurship (Simoes et al., 2016) with a focus on
available demographic characteristics and personal attributes, such as age,
gender, education, experience, migration status and family/household situation.
Nevertheless, we also contribute to the literature by providing an insight into
occupational differences between both groups. Finally, our research contributes
to replication of earlier found determinants of self-employment with and without
employees by using more recent data for a broader range of countries (Davidsson,
2015).
The structure of the paper is conventional. In the following section, we review
the previous literature dedicated to the individual determinants of self-
employment and differences concerning having employees or staying solo. Then
we describe the collected dataset and present several initial comparisons. The
following part is focused on the empirical analysis, where we estimate the
2.

International Review of Entrepreneurship, Article #1587, 16(3) 407
individual likelihood of being self-employed with and without employees, and in
the final section of the paper, we discuss the obtained evidence and present
recommendations for future research.
2. Literature Review
As Cowling et al. (2004), Millán et al. (2014b) and Petrescu (2016) point out, the
empirical evidence on the differences between self-employed with and without
employees is still scarce, and therefore we might get some inspiration on the
potential variables of interest in the classical entrepreneurship literature first.
Then we report the findings of scholars studying the differences between job
creators and solo-self-employed.
2.1. Individual Determinants of Self-employment
Numerous studies aiming to explore the various patterns behind the decision of
becoming self-employed have been written in the past years (e.g. Brockhaus,
1980; Evans and Leighton, 1989; Blanchflower and Meyer, 1994; Le, 1999;
Dunn and Holtz-Eakin, 2000; Brandstätter, 2011; Bosma et al. 2012; Lukeš and
Zouhar, 2013; 2016; Wu, 2015; Szarucki et al., 2016; Holienka et al., 2016;
Santos et al., 2017; Bernat et al., 2017; Criaco et al., 2017; Zhang and Acs, 2018;
Woronkowicz and Noonan, 2018; Dvouletý et al., 2018). Simoes et al. (2016)
have reviewed the obtained empirical evidence and categorised individual
determinants of self-employment into several categories. These include basic
individual characteristics, family background, personality characteristics, human
capital, health condition, nationality, ethnicity and access to financial resources.
As Simoes et al.’s (2016) review covers the most recent empirical evidence and
findings of scholars systematically, we utilise it as a baseline for our study.
2.1.1. Basic Individual Characteristics
Simoes et al. (2016) include in the category of basic individual characteristics
variables representing age, gender, marital status and children. They conclude
that women have lower propensity to enter self-employment than men, which
may be explained by higher risk aversion of females, different sectoral
preferences or the theory of discrimination. When it comes to age, Simoes et al.
(2016, p. 786) support the existing evidence for the inverse U-shaped relationship
between age and self-employment, with a threshold dependent on the country and
year of analysis (which often ranges between 35 and 44). This decreasing
relationship for age above the threshold is often explained by the lower physical

408 Are the Solo-Self-Employed Different Individuals from Job Creators?
and mental availability associated with ageing. According to Simoes et al. (2016,
p. 787) the propensity of becoming self-employed is higher for married
individuals and for those having children, because a husband/wife/partner might
serve as a source of financial, material and emotional support and having children
is associated with higher family costs, requiring higher earnings.
2.1.2. Family Background
Simoes et al. (2016, pp. 788-789) further discuss the role of intergenerational
transmission. According to their summary of empirical studies, having at least
one parent with self-employment experience is positively associated with a higher
chance to become self-employed, because the children tend to follow the similar
career pathways as their parents did. Moreover, the authors indicate that this
positive association might also hold for a partner/spouse because individuals tend
to match with others with similar characteristics and labour market pathways.
2.1.3. Personality Characteristics
Simoes et al. (2016, pp. 789-790) then discuss the role of personality traits and
characteristics, where they highlight the individual (entrepreneurial)
characteristics and traits which are positively associated with the self-
employment engagement. These include higher willingness to take risks,
overconfidence, overoptimism, need for achievement and autonomy, self-
efficacy and internal locus of control.
2.1.4. Human Capital
When it comes to the role of human capital, previous empirical evidence relies
mostly on the variables representing formal education and obtained experience
(Simoes et al., 2016, pp. 790-792). Previously published studies cannot agree on
the role of formal education, which appears to be in empirical studies both
positive and negative, because of multiple effects acting against each other. In
particular, formal education not only increases skills that are useful in
entrepreneurship but also skills that are useful in wage-employment (Parker,
2009). On the other hand, self-employment is clearly positively linked with
higher years of experience (self-employment, managerial and industry-specific)
and with the diversity of obtained experience reflecting the accumulation of
human capital over time.

International Review of Entrepreneurship, Article #1587, 16(3) 409
2.1.5. Health Condition
Inconclusive is also the question of health condition and particularly, the role of
poor health, illness or disability (Simoes et al., 2016, pp. 792-793). Some scholars
find a positive impact of good health status on self-employment and argue that
self-employment is linked with higher levels of stress and working hours and
thus, a good health condition is needed. Others support an argument that
individuals having poor health might find in self-employment flexibility in
working hours/volume of work and an opportunity to escape potentially from
employer discrimination.
2.1.6. Nationality and Ethnicity
A positive relationship between self-employment and migration is usually found
by previous scholars, explained by the enclave hypothesis or theories of
discrimination and human capital, allowing individuals to actively integrate
themselves in the economy (Simoes et al., 2016, pp. 793-794).
2.1.7. Access to Financial Resources
Finally, previous empirical evidence clearly shows that individuals (and
households) with more wealth and those with lower financial constraints tend to
become more likely self-employed (Simoes et al., 2016, pp. 794-795).
2.2. Previous Evidence on the Differences between Solo-Self-employed and Job
Creators
A pioneering work distinguishing between factors determining the choice of self-
employed workers to take on employees or to stay solo has been written by
Cowling et al. (2004), who assume that these two categories of entrepreneurs
distinguish from each other in the way “they are managed, the constraints they
face at start-up and the way public policy should accommodate them.” (p. 602).
Cowling et al. (2004) have based their analysis on the data from the British
Household Panel Survey (BHPS) in 1999, and they have estimated probit
regressions with the dependent variables relating to being a job creator (versus
solo self-employed) and being self-employed (versus wage-employment).
Models were estimated separately for males and females, and some gender
differences were observed in the results. The conducted empirical analysis
covered a range of individual and household characteristics. For both categories
of self-employment, the authors find the inverse U-shaped relationship for age

410 Are the Solo-Self-Employed Different Individuals from Job Creators?
(with turning points of 59 years for solo-self-employed and 41 for job creators).
This finding was however confirmed only for males. In female models, no U-
shaped relationship was proved. Mixed and insignificant results were found for
the role of family (partners) and children. Regarding children, Cowling et al.
(2004) only found a positive impact of having the youngest child of age below
five in models for females determining the decision of being self-employed. The
authors find a positive impact of having self-employed parents only in the models
determining the choice of being self-employed (versus wage-employment), but
not for being a job creator (versus solo self-employment). The empirical findings
for the role of formal education were also quite mixed. Use of initiative was found
to be positively related with the choice of being self-employed, however
surprisingly not with the decision to become a job creator. When it comes to the
role of human capital, experience in current occupation was positively related
with being a job creator. However, mixed results have been obtained for the role
of formal education. Cowling et al. (2004) found that men with a university
education are less likely to be self-employed (and hence more likely wage-
employed), compared to those with no education. A non-significant result was
obtained for females. On the contrary, a positive relationship between higher
education and job creation was found for males, however, again, non-significant
results were obtained for females. The authors do not find any impact of work-
related health limits on the decision to become self-employed with or without
employees. Interestingly, being a female born outside of UK was associated
positively with self-employment. No significant results were found for males.
Finally, wealth measures were not found to be important determinants in Cowling
et al.’s (2004) models, and only some evidence was obtained for variables
representing receiving windfall payments.
Millán et al. (2014b) reviewed several studies related to the choice of
becoming a job creator (also known as employer-entrepreneur) based on the
European Community Household Panel (ECHP) from 1994 to 2001. They
conclude that being self-employed with employees is positively associated with
higher levels of education and years of experience (especially with previous self-
employment experience). They also acknowledge the role of intergenerational
transmission indicating that the presence of relatives working as self-employed
positively influences the likelihood of becoming a job creator. Millán et al.
(2014b) also mention the influence of liquidity constraints, operationalised
through earnings and household income, indicating a positive impact on being
self-employed with employees.
The most recent study was published by Petrescu (2016) who exploited data
from the seventh wave (2016) of the European Social Survey (ESS). She aimed
to observe the differences between self-employed with and without employees
concerning psychological and social values and traits, such as happiness, need for
power and satisfaction. Using MANOVA, Petrescu (2016) found that self-
employed with employees, are happier, have a higher need for power and are

International Review of Entrepreneurship, Article #1587, 16(3) 411
more satisfied with income. When she estimated a logistic regression with the
dependent variable probability of being a job creator, she confirmed the results of
MANOVA. Moreover, she found that being self-employed with employees was
positively associated with being a male, age (but she did not test for inversed U-
shaped pattern) and household income. On the other hand, she failed to prove any
impact of formal education. Other psychological variables (need for achievement,
need for self-direction, community well-being and satisfaction with the state of
the economy) included in her regression model remained statistically
insignificant.
While reviewing the previous literature on the individual determinants of
entrepreneurship, we have found that there are still variables (e.g. family,
education, health) whose impact on the choice of being self-employed is not
conclusive. We were even more surprised to see that there are only a few studies
that addressed the differences in these determinants concerning the different
forms of self-employment (having employees vs. solo). Therefore, we aim to
contribute to the debate on these differences from the European perspective in the
following part of the article, and we focus our analysis mainly on the role of basic
individual characteristics, family/household relations and the impact of human
capital.
3. Data and Comparisons
For the purpose of this study, analysis of the differences between self-employed
with and without employees in Europe, we utilize three harmonized waves of the
European Survey on Working Conditions (2005, 2010 and 2015), that is being in
operation since 1991 by the European Foundation for the Improvement of Living
and Working Conditions (2018) in 35 European countries (for questionnaires,
details, sampling and data collection procedures, see Eurofound, 2017). The
selection of the waves was driven by the availability of the occupational
distinction between self-employed with and without employees (included in the
survey series from 2005 onwards). This section aims to provide readers with an
initial empirical comparison between both groups and with summary statistics for
the obtained sample.
According to survey data, the rates of self-employed are in Europe at around
17% of the economically active population (consisting of 12% solo-
entrepreneurs/own-account workers and 5% entrepreneurs having employees).
These numbers roughly correspond with the recent empirical reports by Masso
(2015), Sheehan and McNamara (2015) and Dvouletý and Lukeš (2017), who
range self-employment engagement in Europe between 14-15% depending on the
data availability and survey used.
First, we observe the differences between the groups of self-employed with
and without employees concerning the motivation for business start-up and then

412 Are the Solo-Self-Employed Different Individuals from Job Creators?
we explore the occupational differences. Given the data availability of the EWCS,
we observe the differences in the most frequently reported motive to establish an
enterprise – enjoyment of being one’s own boss (e.g. Brandstätter, 2011; Lukeš
and Zouhar, 2013; Masso, 2015). Table 1 shows that those having employees
more likely enjoy being their own bosses, compared to those without employees,
however, this relationship is relatively weak (Chi-Square’s p-value < 0.000;
Cramer’s V = 0.08).
Table 1: Motivation for becoming Self-employed for Self-employed with and without Employees
(relative frequency in %; N=13,272)
Note: Post-stratification weights applied.3
We also explore the distribution of self-employed concerning the industry in
Table 2. We find several statistically significant differences (Chi-Square’s p-
value < 0.000; Cramer’s V = 0.25). For instance, the most of the self-employed
without employees are involved in agriculture, hunting and forestry (26.8%),
whereas the self-employed with employees are most frequently represented in
wholesale and retail trade (23.8%).
Cross Tab Self-employed with and without Employees
I Enjoy Being my Own Boss With employees Without Employees
No 8.57 13.38
Yes 91.43 86.62
Test of association, Chi-Square = 58.43, p-value < 0.000, Cramer’s V = 0.08
3. Results are weighted across the relative size of the workforce in each of the countries.

International Review of Entrepreneurship, Article #1587, 16(3) 413
Table 2: Industry Distribution for Self-employed with and without Employees (relative frequency
in %; N=17,978)
Note: Post-stratification weights applied
More interesting insights can be found in the occupational structure of self-
employed according to ISCO-1 classification that is reported in Figure 1. Several
statistically significant differences are observed (Chi-Square’s p-value < 0.000;
Cramer’s V = 0.30). It is not surprising that most of the self-employed with
employees describe themselves as managers (41%) contrary to only 16% of self-
employed without employees. It is worth mentioning, that solo-self-employed are
represented more in the following three occupational groups: Technicians and
Associate Professionals; Skilled Agricultural Forestry and Fishery Workers and
Elementary Occupations.
We have also found (t-test’s p-value < 0.000) that self-employed with
employees work on average more hours weekly (mean 48.8, median 50),
compared to self-employed without employees (mean 43.2, median 42).
Cross Tab Self-employed with and without Employees
Industry Classification - NACE Codes
(Weighted %)
Self-employed without
Employees
Self-employed with
Employees
Agriculture, hunting and forestry 26.79 10.66
Fishing 0.25 0.15
Mining and quarrying 0.19 0.21
Manufacturing 8.02 11.53
Electricity, gas, and water supply 0.35 0.64
Construction 8.13 10.89
Wholesale and retail trade 19.20 23.82
Hotels and restaurants 3.41 10.29
Transport, storage and communication 4.37 3.99
Financial intermediation 1.50 1.53
Real estate activities 9.38 11.20
Public administration and defence 0.44 0.66
Education 1.65 1.30
Health and social work 4.12 4.72
Other service activities 9.73 7.97
Activities of households 2.41 0.41
Activities of extraterritorial organiza-
tions 0.04 0.02
Test of association, Chi-Square = 48.83, p-value < 0.000, Cramer’s V = 0.25.

414 Are the Solo-Self-Employed Different Individuals from Job Creators?
Figure 1: Occupational Structure of Self-employed with and without Employees according to
ISCO-1 Classification (relative frequency in % N=18,032)
Notes: Test of association, Chi-Square = 135.28, p-value < 0.000, Cramer's V = 0.30. Post-
stratification weights applied.
Given these differences, we may support our assumptions and findings of
other scholars in the field (e.g. Cowling et al., 2004; Millán et al., 2014b; Petrescu,
2016) that there are significant differences between both groups and thus, we
employ other available personal characteristics to dive deeper into these. Due to
the limited data availability of EWCS (in terms of characteristics of the self-
employed), we focus our empirical analysis mainly on the demographic
characteristics and personal attributes, such as age, gender, education, migration
status and household situation.
We keep in our sample only individuals having as main occupation self-
employment or wage-employment activity who are younger than 65 years. That
allows us to conduct a relatively straightforward empirical analysis without the
presence of other confounding effects as it is discussed by several scholars in the
field (e.g. Caliendo et al., 2014; Simoes et al., 2016; Georgellis and Yusuf, 2016).
The description of all variables is presented in Table 3 below, while Table 4
provides summary statistics for these variables as they enter the regression
analysis.

International Review of Entrepreneurship, Article #1587, 16(3) 415
Table 3: List of Variables
Var i a b l e D e f i n i t i on
Employment status
Employment status as one of three categories: Self-employed with employ-
ees (having at least one employee excluding the owner of the business), self-
employed without employees or in paid employment.
Age Respondent’s age.
Female Dummy variable which equals 1 if the respondent is female.
Education Set of dummy variables according to ISCED (International Standard Classi-
fication of Education, 1997) 1997 classification.
Years of Experience Respondent’s years of experience in the current company or organisation.
Worked Hours Respondent’s working hours per week.
Migrated Dummy variable which equals 1 if the respondent was not born in the coun-
try of the survey.
Subject of Discrimination
(Race, Ethnic)
Dummy variable which equals 1 if the respondent has been personally sub-
ject of discrimination linked to race, ethnic background or colour over the
last 12 months.
Subject of Discrimination
(Disability)
Dummy variable which equals 1 if the respondent has been personally sub-
ject of discrimination because of his/her disability over the last 12 months.
Number of People in House-
hold Number of people living in a respondent’s household.
Living with a Partner/
Spouse
Dummy variable which equals 1 if the respondent lives together with his/her
spouse/partner.
Partner/Spouse Works Full-
time/Part-time
A set of dummy variables which equal 1 if the respondent’s spouse/partner
works full-time/part-time (is wage-employed or self-employed).
Having One/Two/Three and
more Children under 15
A set of dummy variables which equal 1 if the respondent has one/two/three
and more children under 15 years old in his/her household.
Year of Survey Year when the survey was conducted.
Country Respondent’s country of residence.
Industry (NACE Codes) Respondent’s work industry classification according to NACE codes.
Occupation (ISCO-1 Codes) Respondent’s occupation according to ISCO-1 classification.

416 Are the Solo-Self-Employed Different Individuals from Job Creators?
Table 4: Sample Descriptive Statistics
Note: Self-employed and wage-employed only. Post-stratification weights applied.
4. Empirical Approach and Results
The objective of the paper is to study self-employed with and without employees
in Europe. Methodologically, we estimate several multivariate logistic
regressions with the dependent variable being self-employed at the time of the
survey. To observe the differences between both groups, we combine two
empirical approaches. In the first two econometric models (Models 1 and 2), we
separately compare Self-employed without Employees and Self-employed with
Employees with those being wage-employed and then, in the third model (Model
3), we just work with a sample of self-employed only, and we estimate the
Va ri ab le Frequency (%) N
Self-employed with Employees (=1) 5.0 103,496
103,496
Self-employed without Employees (=1) 11.7
Female (=1) 45.5 103,488
101,309
Pre-Primary education (=1) 1.0
Primary education or first stage of basic education (=1) 5.3 101,309
101,309
Lower secondary or second stage of basic education (=1) 14.5
(Upper) secondary education (=1) 42.5 101,309
101,309
Post-secondary non-tertiary education (=1) 7.1
First stage of tertiary education (BSc./MSc./MBA) (=1) 28.7 101,309
101,309
Second stage of tertiary education (Ph.D.) (=1) 1.2
Migrated (=1) 9.0 100,152
103,496
Subject of Discrimination (Race, Ethnic) (=1) 1.4
Subject of Discrimination (Disability) (=1) 1.0 103,496
103,496
Living with a Partner/Spouse (=1) 74.4
Partner/Spouse Works Full-time (=1) 21.0 103,319
103,319
Partner/Spouse Works Part-time (=1) 2.8
Having One Child under 15 (=1) 19.7 103,496
103,496
Having Two Children under 15 (=1) 13.1
Having Three and More Children under 15 (=1) 3.7 103,496
Vari a b l e Mean SD Min Max N
Age 41.5 11.6 15 65 103,147
Working Hours per Week 39.3 12.7 1 120 100,698
Years of Experience in Current Company 10.1 9.7 1 50 101,503
Number of People in Household 3.0 1.3 1 6 103,319

International Review of Entrepreneurship, Article #1587, 16(3) 417
individual likelihood of being Self-employed with Employees (cf. Cowling et al.,
2004).
We estimate our logistic regressions with robust standard errors. We have
also inspected the level of collinearity among the estimated parameters with the
help of correlation matrices and Variance Inflation Factors (VIF) test, and we
conclude, that no multicollinearity is present in our estimates (Wooldridge,
2002). We also control for country and year effects by a set of dummy variables,
and we also use dummies to adjust for industry specifics (NACE codes) and
occupational variation (ISCO-1 codes). Moreover, the models are estimated with
post-stratification weights, adjusting estimates for the relative size of the
workforce in each of the countries, to ensure balanced results in the pooled
sample. As a robustness check, we tried to estimate our models on the sample of
the EU-12 countries4 only and compare these results with the later entrants to the
European Union. The results for most of the variables were roughly similar, and
thus, we report the final estimates for the whole pooled sample. Presented models
were found to be statistically significant and can be found in Table 5.
The obtained estimates support an assumption of differences concerning
demographic characteristics and personal attributes of both self-employment
groups. It is not surprising that both groups are different from employees as it is
shown in the first two sets of econometric models (Models 1 and 2). However, as
suggested by previous research, job creators differ from solo-self-employed as
well (Model 3).
We begin the interpretation of the findings based on the results obtained from
the first two econometric models (Models 1 and 2). The results show that both
categories of self-employed are less likely represented among females (Female).
The first difference occurs for the variable age (Age, Age Squared). For the group
of self-employed with employees, the age variable indicated the traditional
inverted U-shape with the likelihood of being self-employed (with a turning point
at the age of 56), however for the group of those without employees, we were
unable to find a non-linear pattern.5 The impact of family and household
characteristics was tested by several variables (Number of People in Household,
Living with a Partner/Spouse, Partner/Spouse Works Full-time/Part-time,
Having One/Two/Three and more Children under 15). The number of people
living in a common household and living with a partner/spouse were positively
associated only with the probability of being self-employed with employees.
Similarly, having a partner working part-time or full-time positively
distinguished job creators from employees. On the contrary, living in a common
household with children aged under fifteen was positively associated with both
forms of self-employment. Furthermore, the models show a positive relationship
4. Belgium, Denmark, Germany, Greece, Spain, France, Ireland, Italy, Luxembourg,
Netherlands, Portugal, United Kingdom.
5. When running a model which also included a squared age term (not reported in Table 5), the
p-value for the quadratic term was 0.224 and the sign of the coefficient was positive.

418 Are the Solo-Self-Employed Different Individuals from Job Creators?
of self-employment with the variables measuring years of experience in the
current company (Years of Experience in Current Company) and the number of
working hours per week (Working Hours per Week).
Different patterns were observed for the role of formal education. For the self-
employed without employees, the results indicated a negative relationship with
the increased attained level of formal education, statistically significant especially
for (Upper) secondary education and First stage of tertiary education (BSc./MSc./
MBA). However, the opposite pattern was observed for the self-employed with
employees. The likelihood of being a job creator was increasing with the attained
level of formal education, and the effect was the highest for university graduates
completing First stage of tertiary education (BSc./MSc./MBA) and Second stage
of tertiary education (Ph.D.). We were unable to empirically observe any
significant impact of the disability in both models. Finally, we see differences
concerning discrimination due to race or ethnicity (Subject of Discrimination
(Race/Ethnic)) and migration (Migrated). The signs for these two variables do not
differ in both models, but their significance does. We find a negative statistically
significant impact of migration on being an employer-entrepreneur and a positive
influence of suffering from discrimination due to race or ethnicity for being solo-
self-employed.
Looking at the third model (Model 3) estimated on the sample of self-
employed only, we may even more clearly see the differences mentioned above.
Our model indicates that having employees is more the domain of males,
compared to females. The results also support an argument of an inverted U-shape
pattern of age (with a turning point at the age of 40). Family background is
important for job creators. Compared with solo-self-employed, they seem to live
with a partner/spouse more likely. They are also more likely to live with a partner
engaged on the labour market, working part-time or full-time. Working more
hours per week and having more years of experience in their own firm is also
positively associated with being self-employed with employees. Employer-
entrepreneurs are also more likely people with a higher level of education, where
the highest likelihood of being self-employed with employees was observed for
the individuals, having a doctoral degree (Ph.D.). This is an interesting
observation. According to Appendix 1, 10.4% of those who have attained a
doctoral degree are self-employed with employees and 11.4% are self-employed
without employees. Job creators with a doctoral degree are on average elder, work
more hours a week, and have more years of experience (compared to solo self-
employed workers and wage-workers with a doctoral degree).

International Review of Entrepreneurship, Article #1587, 16(3) 419
Table 5: Estimation results self-employment regressions
Model Model (1) Model (2) Model (3)
Self-employed with-
out Employees
Self-employed with
Employees
Self-employed with
Employees
Age 0.0268*** 0.118*** 0.0579***
(0.00166) (0.0156) (0.0167)
Age Squared -0.00106*** -0.000722***
(0.000182) (0.000192)
Female -0.155*** -0.456*** -0.381***
(0.0313) (0.0479) (0.0540)
Pre-Primary education (.) (.) (.)
(.) (.) (.)
Primary education or first
stage of basic education
0.000431
(0.165)
0.662+
(0.386)
0.367
(0.317)
Lower secondary or second
stage of basic education
-0.248
(0.162)
0.898*
(0.380)
0.848**
(0.315)
(Upper) secondary education -0.363* 1.004** 1.110***
(0.161) (0.379) (0.313)
Post-secondary non-tertiary
education
-0.267 1.245** 1.296***
(0.169) (0.384) (0.326)
First stage of tertiary educa-
tion (BSc./MSc./MBA)
-0.377*
(0.163)
1.365***
(0.380)
1.505***
(0.316)
Second stage of tertiary educa-
tion (Ph.D.)
-0.0349 2.033*** 2.027***
(0.203) (0.399) (0.349)
Working Hours per Week 0.0282*** 0.0710*** 0.0143***
(0.00144) (0.00236) (0.00147)
Years of Experience in Current
Company
0.0128*** 0.0329*** 0.0222***
(0.00162) (0.00225) (0.00298)
Migrated -0.0733 -0.211** -0.141
(0.0594) (0.0806) (0.0976)
Subject of Discrimination
(Race/Ethnic)
0.220+ 0.132 0.0465
(0.124) (0.168) (0.216)
Subject of Discrimination (Dis-
ability)
0.227 -0.0143 -0.103
(0.171) (0.275) (0.338)
Number of People in House-
hold
0.0133 0.0413+ 0.0259
(0.0147) (0.0227) (0.0250)
Living with a Partner/Spouse -0.00274 0.179** 0.208**
(0.0379) (0.0593) (0.0679)
Partner/Spouse Works Full-
time
-0.0443 0.201** 0.261**
(0.0496) (0.0718) (0.0851)
Partner/Spouse Works Part-
time
0.0729 0.272* 0.364**
(0.0824) (0.114) (0.134)
Having One Child under 15 0.0977* 0.174** 0.0969
(0.0406) (0.0600) (0.0677)
Having Two Children under 15 0.220*** 0.238** 0.0775
(0.0490) (0.0736) (0.0830)
Having Three and More Chil-
dren under 15
0.228** 0.412*** 0.203
(0.0833) (0.122) (0.131)

420 Are the Solo-Self-Employed Different Individuals from Job Creators?
Robust SE Logistic Regression Estimates (Self-employed vs. Wage-employed in Models 1 and 2;
Self-employed with Employees vs. Self-employed without Employees in Model 3).
Standard errors in parentheses. Stat. significance: + p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001.
Turning points for Age, Age Squared: Model 2 56 Years, Model 3 40 Years.
Post-stratification weights applied
5. Discussion and Concluding Remarks
The present study empirically contributes to entrepreneurship literature by
studying differences between self-employed with and without employees in
Europe. We started by summarising the previously obtained empirical evidence
on the determinants of employer-entrepreneurship, and we showed that there
were only a few studies published on this topic so far. Moreover, most of them are
based on elder data sources, and thus, we would like to stimulate the debate, based
on more recent empirical evidence. Methodologically, we have estimated a set of
logistic regressions based on data from the three recent waves (2005, 2010 and
2015) of the European Survey on Working Conditions (EWCS). The obtained
findings show considerable differences between those self-employed having
employees and those who have not. This observation corresponds to the recent
call for more research on freelancing and solo-entrepreneurship raised by Burke
and Cowling (2015) who argue that self-employed without employees are
“different animals.”
Obtained findings challenge some established patterns in the literature on the
determinants of entrepreneurship and self-employment (Simoes et al., 2016). We
show that there are considerable differences concerning variables such as age,
education, household situation, where we found different patterns for solo-self-
employed and self-employed with employees.
Although the classical entrepreneurship literature (Simoes et al., 2016) and
the previous research (Cowling et al., 2004 for males only) assumed a U-shaped
pattern with age for both forms of self-employment, our results were unable to
support this empirically. We find a non-linear pattern for job creators, but not for
the solo-self-employed. If we consider other variables approximating human
capital, this observation makes sense. The results show that self-employed with
employees have on average more years of experience compared to those without
Constant -2.171*** -9.077*** -4.796***
(0.199) (0.521) (0.479)
Yea r Dum mies Ye s Ye s Ye s
Country Dummies Yes Yes Ye s
Industry Dummies (NACE
Codes)
Yes Yes Ye s
Occupational Dummies (ISCO
1 Codes)
Yes Yes Ye s
Observations 88,455 82,756 13,965
Pseudo R20.219 0.216 0.111
AIC 48804.7 26851.6 15737.2
BIC 49433.9 27485.6 16250.2

International Review of Entrepreneurship, Article #1587, 16(3) 421
employees. That might indicate the accumulation of human capital over time
(Marvel et al., 2016; Simoes et al., 2016). Finally, once we add to the whole
picture of human capital variables, the findings for formal education, showing
that the likelihood of being a job creator increases with the level of obtained
education (i.e this likelihood is the highest for university education, i.e., bachelor,
master and doctoral level), we may conclude that jobs are created by individuals
who have stronger profiles in terms of human capital as already stressed by
previous scholars in the field (Congregado et al., 2010; Millán et al., 2014b;
Sorgner et al., 2017; Coad et al., 2017).6 This piece of information may, therefore,
answer the ambiguous findings of scholars on the role of formal education in the
general literature on the determinants of self-employment (Simoes et al., 2016)
and it highlights the importance of distinguishing between these two forms of
entrepreneurship.
We also deliver interesting findings on the role of family and household
situation. As Parker (2009) and Simoes et al. (2016) suggest, the partner living in
a common household may serve as a source of emotional support and as a
financial backup helping the self-employed partner to survive any difficulties in
the activity and to act riskier in the exposition of the own business activity. Our
findings suggest that this might be the case mainly for the job creators. Compared
with solo-self-employed workers, self-employed with employees seem to live
with a partner/spouse more often and their partners are more likely engaged on
the labour market, working part-time or full-time. It is logical that having more
employees is associated with an expansion of the business and thus potentially
higher risks, and therefore, the financial support of the partner might be very
helpful, especially in situations when something goes wrong. This observation
expands the findings of Cowling et al. (2004) who were unable to find clear
patterns regarding the role of the family, and the partner’s engagement on the
labour market.
The obtained estimates also show that self-employed with a migration
background are significantly less likely to be employer entrepreneurs (relative to
being wage-employed). One possible explanation is that their motivation for
becoming self-employed is based more on necessity-related factors (also known
in the literature as a “refugee effect”), such as securing a job for themselves and
having an opportunity to earn income for paying their living costs (e. g.
Kloosterman, 2010; Dvouletý and Lukeš, 2016; Laffineur et al., 2017; Mühlböck
et al., 2017). However, this is not supported by the work of Cowling et al. (2004)
as they did not find statistically significant relations between being born outside
of the country (Great Britain) and being a job creator. More research is required
on this relation.
6. However, this was not found in the most recent study by Petrescu (2016). We believe that it
might have been caused by the model specification which reflected only years of formal
education.

422 Are the Solo-Self-Employed Different Individuals from Job Creators?
We have a number of suggestions for future research. The present study
focused on demographic and personal factors affecting the employment status.
We highly recommend future scholars to study also the role of psychological
factors, intergenerational transmissions and economic circumstances. We also
recommend investigating the role of specific entrepreneurship education, training
and previous self-employment experience. We also think that further
investigation of the role of health status, ethnicity and migration background is
needed as we were unable to draw conclusive findings on these. From the family
perspective, it might be worth to study the role of the partner’s/spouse’s
engagement in self-employment and his/her previous self-employment
experience, because we were not able to distinguish if the partners/spouses work
as employees or self-employed. Another direction of future research could be to
explore the differences in entrepreneurial success (measured both objectively and
subjectively, see e.g. Wach et al., 2016) of employers and solo-entrepreneurs and
to compare their job satisfaction.
Finally, if policymakers aim to support high-growth entrepreneurship, they
need to understand the characteristics of current and future employers better. The
present paper shows that jobs are created by individuals who on average work
more hours, have more experience in their own company and who attained higher
levels of education (bachelor, master and doctoral level). Future research may
find additional characteristics of job creators which are highly relevant to policy
makers.
In conclusion, by studying determinants of self-employed workers with and
without employees separately, we created new knowledge on this topic but we
also confirmed some earlier findings in the literature, thereby contributing to
replication by using more recent data for a broader range of countries (Davidsson,
2015).

International Review of Entrepreneurship, Article #1587, 16(3) 423
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426 Are the Solo-Self-Employed Different Individuals from Job Creators?
Appendix 1: Sample Descriptive Statistics for Ph.D. Holders Only
Note: Self-employed and Employed only. Post-stratification weights applied.
Var i ab le Frequency (%) N
Self-employed with Employees with Ph.D. (=1) 10.4 1,213
Self-employed without Employees with Ph.D. (=1) 11.4 1,213
Employed with Ph.D. (=1) 78.2 1,213
Variable/Category with Employees
(Freq. %)
without Employees
(Freq. %)
Employees
(Freq. %)
Female (=1) 33.7 35.9 43.5
Migrated (=1) 4.3 11.6 15.6
Subject of Discrimination (Race, Ethnic) (=1) 2.7 0.1 1.0
Subject of Discrimination (Disability) (=1) 0.0 3.1 0.5
Living with a Partner/Spouse (=1) 83.5 78.4 74.2
Partner/Spouse Works Full-time (=1) 11.0 11.7 14.4
Partner/Spouse Works Part-time (=1) 1.9 0.0 2.9
Having One Child under 15 (=1) 16.7 20.7 16.6
Having Two Children under 15 (=1) 9.4 16.3 15.6
Having Three and More Children under 15 (=1) 4.4 2.1 6.0
Var i a b l e Mean SD Min Max N
Age (S. with Employees) 48.3 9.2 26 65 121
Age (S. without Employees) 47.0 11.1 23 65 129
Age (Employees) 42.9 10.3 22 65 954
Working Hours per Week (S. with Employees) 45.7 11.7 8 80 117
Working Hours per Week (S. without Employees) 40.2 16.5 2 75 118
Working Hours per Week (Employees) 40.5 10.9 2 84 942
Years of Experience (S. with Employees) 13.8 9.1 1 38 120
Years of Experience (S. without Employees) 10.2 9.5 1 43 125
Years of Experience (Employees) 10.5 9.6 1 43 953
Number of People in Household (S. with Employees) 2.9 1.4 1 6 123
Number of People in Household (S. without Employees) 2.7 1.3 1 6 131
Number of People in Household (Employees) 2.8 1.4 1 6 958