Entrepreneurship and family compositionDo entrepreneurs and non-entrepreneurs differ in family

composition?

Bachelor thesis

Cornel Kok (358718ck)

1-8-2014

Supervisor: Cornelius A. Rietveld MSc.

Contents1. Introduction....................................................................................................................................3

2. Literature review............................................................................................................................4

Entrepreneurship...............................................................................................................................4

Job occupation...................................................................................................................................6

Marriage.............................................................................................................................................6

Divorce...............................................................................................................................................7

Children..............................................................................................................................................8

3. Data................................................................................................................................................9

Entrepreneur......................................................................................................................................9

Marriage.............................................................................................................................................9

Divorce.............................................................................................................................................10

Number of children..........................................................................................................................10

4. Methodology................................................................................................................................11

5. Results..........................................................................................................................................12

Descriptive statistics.........................................................................................................................12

Correlations......................................................................................................................................13

Logistic regression............................................................................................................................15

Hypothesis........................................................................................................................................16

6. Conclusion....................................................................................................................................17

7. Limitations and further research..................................................................................................18

8. Bibliography.................................................................................................................................20

1. IntroductionIn the labour force there is an important distinction between entrepreneurs and those that work for

them (wage-workers). Entrepreneurs are the people who see chances of making business and

therefore start a business. So without entrepreneurs there are no business start-ups. Functioning as

an employee within a business is another way to earn money. This is important, because not every

person has the ambition of becoming an entrepreneur: they might suit better in the role of an

employee. On the contrary, most entrepreneurs do not feel comfortable in a role as employee. Partly

therefore entrepreneurial activities are still a significant proportion of the labour force.

Entrepreneurs and employees do not only differ in their career perspective, there are more areas

where a distinction between the entrepreneur and an employee can be found. Also researchers

investigate the difference between the two groups in different fields. One of them is the field of

human capital, where is examined that entrepreneurs have different personality characteristics in

comparison to the non-entrepreneurs (Brandstätter, 1997).

This paper is in the field of social capital, which is about the personal relations somebody has. The

word relation is quite broad. To add more focus in this study, this study investigates family relations

of the entrepreneur. To see if there are general differences in the family composition as compared to

non-entrepreneurs the following research question is used:

Do entrepreneurs and non-entrepreneurs differ in family composition?

The main differences in the family composition are the relationship with their partner and the family

size. Therefore attention will be paid to civil status and number of children.

The next section contains the theoretical background of this study. It describes that different views

about the definition of the entrepreneur exist and that in the literature a lot of different definitions

for entrepreneurs are used. Next, it discusses the relation between entrepreneurship and the family

characteristics that are used in this study. Section four, the data section, describes how these

characteristics are analyzed. Section five presents the empirical results and evaluates the

hypotheses. Section six concludes and section seven summarizes limitations and recommendations

for further research.

2. Literature review

EntrepreneurshipMany researchers have been struggling in making one definition of the entrepreneur. Many papers

are written about the several elements which should be in the definition. But according to Gartner

(1988) there is no general definition of an entrepreneur, despite the effort of Hébert and Link (1989),

who wanted to have one definition which should be used after.

For some scientists, entrepreneurship is merely a state of mind and not necessarily the concrete

action of starting a business. In other words, it is possible to be an entrepreneur (to have the

characteristics of an entrepreneur) without any business start-up. This definition is the psychological

definition of entrepreneurship. The data about the psychological definition about is mostly self-

reported data. If the data is gathered by a questionnaire, there is a question about entrepreneurial

aspirations included. It is likely that someone reports having entrepreneurial aspirations, but will not

start-up any firm in his or her life.

Through the years, the definition of Shane and Venkataraman (2000) is one of the most stable

definitions: Someone who discovers, evaluates and exploits entrepreneurial opportunities. The three

elements in this definition are most important. First of all, an entrepreneur must be able to discover

entrepreneurial opportunities. Entrepreneurial opportunities are defined as situations that entail the

discovery of new means-ends relationships in which new goods or services and manners of

organizing are introduced with the aim to generate economic value (Companys & McMullen, 2007)

Innovation as a new way of thinking is part of this. The process of innovation is according to Ahuja

and Katila (2004) an important source of competitive advantage.

Entrepreneurship is about discovering, evaluating and exploring the entrepreneurial opportunities.

According to Shane (2000) entrepreneurs discover opportunities related to the information they

already possess. However the particular entrepreneurial characteristic of discovering a business-

opportunity is not easy to measure.

Secondly, the entrepreneur has to evaluate the profitability of the opportunity. A good evaluation

leads in general to a distinction between lucrative and non-lucrative business opportunities. At this

point there might arise some friction. If someone starts to exploit a business opportunity and the

start-up goes bankrupt, the person can be accused to have done no thorough evaluation. According

to the definition, it is even possible to regard the person as a non-entrepreneur. The definition

enables people to see only the people who start-up a profitable business as entrepreneurs. This is

written monochrome to indicate that it is impossible to have a clear measurement of the evaluation

of an entrepreneur. Of course, there are risks that influence the profitability of the business, which

are not evaluated by the entrepreneur. To regard the entrepreneur no longer as entrepreneur,

because of mistake in evaluation is undesirable.

Finally, an entrepreneur is someone who exploits the opportunity. In fact, only this aspect of the

definition is truly measurable, since it means starting up an own business. This last aspect of the

definition of Shane and Venkataraman is related to the definition which is mostly used in studies,

namely the exploitation of a business-possibility. This this is measured as business-ownership

(Parker, 2009). The advantage of this definition above the others is the measurability. It is relative

easy to indicate if someone is owner of a business or not. Naturally, someone is aware about his or

her own situation if he or she is an entrepreneur, but also the Chamber of Commerce knows this. In

the case of entrepreneurial aspirations (the psychological definition), only the respondent can give an

answer if he or she has entrepreneurial intentions, which can be depending on the mood in which he

or she finds his- or herself.

Besides finding a suitable and researchable definition of the entrepreneur, there is a lot more

research done in the field of entrepreneurship. There are the regional studies (Munemo, 2012),

cultural studies (McGrath & Macmillan, 1992). Even within a culture inside a country there are

different areas, which have different characteristics in the field of entrepreneurship. Differences in

entrepreneurial activities are mostly a result of differences between areas in the type of industry

(Verheul, Carree, & Santarelli, 2009). Which means that differences in entrepreneurial activities are

often externally defined. It depends on the (economic or technical) development of the region the

entrepreneurs finds him/her in. In The Netherlands, there are also differences between certain

regions. Therefore it is expected that entrepreneurship differs among The Netherlands.

There are also internally defined causes for differences between entrepreneurial activities, such as

the intentions to start up a business. There are two main reasons: the refugee effect and innovation

perspective. The refugee effect is the effect of becoming self-employed because there is no option of

becoming employed otherwise (Carree, 2002). The other perspective is the innovation perspective,

meaning that people start up a business, because there is no business that offers a comparable

product or service. Generally spoken, the refugee effect is mostly visible in poor countries. In western

countries, for example countries in the OECD1, the innovation perspective is more a reason to start a

business. The Netherlands is seen as a developed country (also member of the OECD). However, this

do not implicate that all Dutch entrepreneurs are innovation-driven.

1 Organisation for Economic Co-operation and Development

Next to the regional studies, there are studies that are done in the field of human capital. Those

types of studies take personal competences as independent variable to predict entrepreneurship or

describe characteristics an entrepreneur should have. This is done in general terms by indicating if

the entrepreneur should be more a generalist in comparison to the employee, who should be more a

specialist (Lazear, 2004). Some researchers also use a limited number of specific characteristics and

see how the entrepreneurs own these characteristics (Ciavarella, Buchholtz, Riordan, Gatewoord, &

Stokes, 2004). However, this study about the family characteristics is done in the field called social

capital.

In the field of social capital, research focusses on the relations a person has. In the entrepreneurial

field, this type of research is mostly executed to indicate the relationship of the entrepreneur with a

costumer of supplier (Casson & Giusta, 2007). However this research focusses on the family

relationship between the entrepreneur and his or her family.

Job occupationOccupational choice theory explains that individuals choose an occupation that is expected to give

them the highest utility, not only in terms of finances, but also in terms of benefits on the area’s

which they identify as important (Taylor, 1999). For example, the flexibility to combine childcare with

work (Parker, 2009). If you are free to define your own working time and be flexible at the moments

your social or family life requires that, it gives you less stress than if working hours are dictated and

there is no flexibility to combine this with (unexpected) events in your family life. In case of becoming

entrepreneurs to be flexible for the childcare, there is another profit of entrepreneurship, namely the

non-monetary benefits of spending more time with your children (Edwards & Field-Hendrey, 2002).

MarriageA century ago, the literature of marriage was definitely different from nowadays. Before the sexual

revolution, marriage was the default option of the civil status. During the 1960s started the sexual

revolution, which resulted in sexual liberation. Living together without being married became

accepted. The social obligation to marry disappeared, which has a decreasing marriage rate as result.

There are two exceptions on this regularity. The first exception is rise in marriages due to the baby-

boomers. This happened between 1983 and 1990 (Centraal Bureau voor de Statistiek, 2006). The

second exception is the year 2002. In 2002 the number of marriages increased also. This is not due to

the legalization of the same-sex marriage in 2001. Other reasons partly explain the increase, such as

special dates. On 02-02-02, 20-02-2002 and 22-02-2002 (Dutch notation) significantly more people

got married (Centraal Bureau voor de Statistiek, 2002). It is not clear if this is connected with the

marriage of the current King of The Netherlands, which was on the second of February).

To involve entrepreneurs in this context, we suppose a difference between entrepreneurs who are

married or have a family and entrepreneurs who have not. Being married or having a family implies

that the entrepreneur has a greater responsibility and therefore it is likely that the entrepreneur will

take less financial risk, compared to unmarried people (Renzulli, Aldrich, & Moody, 2000). There is

also an opposite approach, namely the possibility of the entrepreneur having a partner with a secure

and stable income can increase the likelihood of becoming an entrepreneur. However, there is no

evidence found for this approach (Hurst & Lusardi, 2004). Due to the combination between risk

taking as an entrepreneur and responsibility taking as a partner it can be easier if you, as an

entrepreneur, are unmarried. Therefore the first hypothesis is:

Hypothesis 1: Entrepreneurs are less likely to be married than non-entrepreneurs.

DivorceThe literature on divorce has overlap with the literature on marriage, with is described above. In the

1960s only ten percent of the women had lived together before they married. Nowadays this figure is

inverted; nine of ten women have lived together before they are getting married. There is also

another trend visible, namely the decrease of marriage out of cohabitation. The cohabitation

relationship is less often converted into a marriage (Centraal Bureau voor de Statisiek, Minder

huwelijken na samenwonen, 2006). For some people, the cohabitation is just a trial period. With this

trial period, it is likely that the choice for marriage is well thought and less an impulsive decision.

Therefore a decrease in number of divorces is expected. As seen before the number of marriages is

decreasing which also have impact on the number of divorces. If people are not married, but live

together it is impossible to divorce, because divorcing is only possible if the couple is married.

However data of the Dutch Central Bureau for Statistics (CBS) indicates that the number of divorcing

is stable since 1990. With the decreasing number of marriages, the chance of getting divorced is

increasing for the married people (Centraal Bureau voor de Statistiek, 2012).

In this paper attention is paid to see if the general trend of divorcing is applicable for entrepreneurs.

As seen before, the entrepreneur wants to spend more time on his or her occupation in comparison

to non-entrepreneurs (Merz & Lang, 1997). There are situations that there is a time-conflict between

the family and work, since the family and business compete for the same time. This can result in a

stressful situation. The stress has a bad effect on the marriage. Sometimes the entrepreneur has to

choose between the family and the business. Even on long term, some entrepreneurs are more

satisfied by being an entrepreneur than a family member. The business has a higher priority. This can

result in a divorce. After the divorce, people have more freedom to go. However there is a gender

effect. In a case of divorcing with children, most children stay with their mother (Starr & Yudkin,

1996). To investigate whether the occupation of being a business owner has a connection with a

divorce the next hypothesis is examined:

Hypothesis 2: Entrepreneurs are more likely to be divorced than non-entrepreneurs.

ChildrenThe previously described trends have some impact on the children. After the sexual revolution of the

1960s the presumption that children are born inside a marriage became untenable. Early sixties, one

of thirty first-born-children was born outside a marriage. Nowadays, a third of the first-born-children

are born outside a marriage. However the difference of these figures is deeper. Now, most children

are born in a relationship, but outside a marriage, in the past however, the most children who were

born outside a marriage were born after an unwanted pregnancy (Centraal Bureau voor de Statistiek,

2001).

Married people have to makes choices in their time management between the business and the

family (Shelton & Daphne, 1996). Due to the responsibilities a business owner has for his or business

it is expected that he or she has less time to spend on his or her family. Therefore the next

hypothesis is formulated:

Hypothesis 3: Entrepreneurs are likely to have less children than non-entrepreneurs.

3. Data The data which is used in this paper is gathered and administrated by CentERdata 2. The dataset is

called the LISS- dataset (Longitudinal Internet Studies for the Social sciences). The data is a panel

data. The data is gathered by internet-surveys which are filled in by Dutch individuals. This panel is

representative for the Dutch population. To have a true probability sample, the sample is drawn from

the Dutch population register. Computers with internet connection are provided to households that

were otherwise unable to participate in the panel. A large variety of domains (including work,

education, income, housing, time use, political views, values and personality) is annually investigated

(Scherpenzeel & Das, 2010). However in this research only the last wave is used, because this wave

contains the most recent data. This data is gathered from April to May 2013.

EntrepreneurIn the dataset, there is no variable which gives direct information about being an entrepreneur or

not. However, there is a variable which describes the type of job. There are eight possibilities. The

first four refer to being an employee (employee in permanent employment, employee in temporary

employment, on-call employee or temp-staffer). The last four (self-employed, independent

professional, director or majority shareholder director) are regarded as being an entrepreneur.

Therefore a binary variable is created, namely entrepreneur, which is zero for the first four

possibilities and one for the last four possibilities of the original variable. This means that

entrepreneurship in this case is the same as business-ownership. As mentioned before in the

literature review, there are many different definitions. In this research the business-ownership is

used because the variable is clearly measurable. Being a business-owner is not necessary self-

reported data, it is possible to control this in the Chamber of Commerce. As seen in the literature

review, there are externally and internally caused differences between entrepreneurs. Different

surroundings make different entrepreneurs. While the study focusses on the full country, differences

between regions inside The Netherlands are neglected. The intentions of becoming an entrepreneur

are also different. In this research there is no distinguished between the different type of

entrepreneurs.

MarriageIn the dataset there is a variable about civil status. There are five options: married, separated,

divorced, widow(er) or never been married, so this is a nominal variable. Only the first answer is

regarded as being married in this research. To analyse these data, the variable is transformed in a

binary variable named marriage. This variable is one for the first option of the LISS variable about

2 http://www.lissdata.nl

civil status (and zero for the other possibilities). As seen in the literature review, there is a difference

between living together and marriage. In this research only marriages are investigated. Therefore

this variable gives no indication of the number of relationships.

DivorceTo study the number of divorces among individuals, the same variable, which describes civil status, is

used. Again, a new binary variable is created, called divorce. The value of this new variable is one for

the option divorced of the original variable and zero for the other options. Because the original

variable is only about the current civil status, there is no attention paid on someone’s history of civil

status. Therefore, if someone chooses the divorced option, it does not give any indication of the

number of divorces that person has had before.

Number of childrenTo analyze how many children an individual has, this research makes use of an ordinal variable. This

gives the number of living-at-home children in the household. There are 10 different options, starting

with the option no children and ending with the option nine children or more. A descriptive analysis

makes clear that six children is the maximum in this dataset. Because the coding (no children is

coded as 0 and six children is coded as 6) the outcome is equal to the number of children. However,

this may not be the total number of children begotten by the respondent. Only the children who live

at home are included in this variable.

4. Methodology The empirical analysis of the data is performed in three steps. In all steps a 5% significance level is

being used. The results of each step are summarized in tTables. The first step considers analyzing the

data with descriptive statistics. In the tTable with descriptive statistics mean values of variables are

given for the total sample, and entrepreneurs and non-entrepreneurs separately. The significance of

difference between the two groups is examined with an independent sample T-test, of which the P-

value is given. The results of this are shown in Table 1.

Next, the correlation between the different variables employed in this study is investigated in a

correlation matrix. This tTable helps finding underlying connections between different variables. A

few other correlations in specific subgroups are investigated as well. Children of divorced people

tend to stay with their mother (Graaf, 2005). Therefore the correlation between gender with number

of children is examined for divorced people only. The correlation of the number of children with

other variables is examined. It is likely that the correlation between age and the number of children

is negative, because in some point of time the children leave home. Therefore this correlation is also

done with a filtering for the age between 25 and 41 (the ages between most families are growing).

The correlations of the variables which are used in the logistic regression are shown in Table 2. The

correlations of the filtered conditions are not included in this tTable, but reported in the text.

Finally, a binary logistic regression is performed on entrepreneurship. With this model the relation of

all different variables on the dependent variable entrepreneur is tested. There is controlled for

several factors in the regression, namely age and gender. This is done to check if the variables are

significantly associated with entrepreneur without an indirect effect via the variables age and gender.

In the statistics the variables marriage and divorce are binary variables. The zero-group of the

variable is the reference-group.

5. ResultsIn this part, the results are described and discussed. The data is analysed with SPSS 20 for Windows.

As mentioned in the previous chapter, there are three tTables. Table 1 displays the descriptive

statistics; Table 2 displays the correlations between different variables and Table 3 displays the

regression results. First the tTables are discussed. After that the link to the hypotheses is made.

Descriptive statisticsTable 1. Descriptive statistics of the sample. Mean values are reported for the total sample, non-entrepreneurs and entrepreneurs. Standard deviations are given between brackets. The difference between the subsamples non-entrepreneurs and entrepreneurs are tested with a T-test. The P-value, which is the result of the T-test , is given in the last column.

Analyzed Full Sample

Non-entrepreneurs Entrepreneurs

P-value for

differenceIndependent variablesMarriage (0: no; 1: yes) 0.42 (0.49) 0.42 (0.49) 0.44 (0.50) 0.366Divorce (0: no; 1: yes) 0.07 (0.25) 0.07 (0.25) 0.07 (0.25) 0.922Number of children 1.18 (1.25) 1.18 (1.26) 1.15 (1.19) 0.596Age 39.78 (22.01) 39.66 (22.11) 40.73 (21.32) 0.357Gender (1: Male; 2: Female) 1.52 (0.50) 1.52 (0.50) 1.47 (0.50) 0.050N 3381 2979 402

In the LISS-dataset there are 12517 respondents. However, not everyone did respond to all the

questions. This is due to the design of the questionnaire. If someone is too young or too old to have

an occupation, occupational questions are left out in that particular survey. As a result the number of

non-entrepreneurs added to the number of respondents of entrepreneurs does not add up to 12517,

but to 3381.

The independent variables marriage, divorce and number of children are examined. Slightly more

entrepreneurs are married in comparison with non-entrepreneurs. However this difference is not

statistically significant. There are no differences visible between the analyzed sample, the non-

entrepreneur sample and the entrepreneur sample in terms of divorce rates. There is no statistically

significant difference between entrepreneurs and non-entrepreneurs in the number of children.

A few control variables are included in this tTable, namely age and gender. As mentioned before, age

is measured as a scale variable. The business-owner is on average slightly older than the non-

entrepreneur, however this difference is not statistically significant. The other descriptive variable is

significantly different between the two groups. On average there are more male entrepreneurs than

female entrepreneurs. The difference in gender is not influenced by not-working mothers, because

that type of women is included in the database but not in the analyzed samples. This is due to the

design of the questionnaire. However, the working mothers are included in all samples. This can

explain why there are more male entrepreneurs than female entrepreneurs. If it is easier for a

mother to combine her motherhood with a non-entrepreneurial career than an entrepreneurial

career it seems possible that having children can have an effect on the choice of career. This is a

possible reason why the proportion differs among the subsamples.

CorrelationsTable 2. Correlations of different variables. The reported values are Pearson correlation coefficients.

Entrepreneur Marriage DivorceNumber of

children Age GenderEntrepreneur 1.000 Marriage 0.016 1.000 Divorce -0.002 -0.227** 1.000 Number of children -0.009 -0.127** -0.119** 1.000 Age 0.016 0.557** 0.187** -0.522** 1.000 Gender -0.034 -0.018 0.030** -0.008 -0.003 1.000* Signifcant at the 1% significance-level** Significant at the 5% significance-level

Between the variables, there are many linkages. First marriage is analysed. There is no significant link

between marriage and entrepreneur. This gives an indication that there is not enough evidence to

hold the first hypothesis. The correlation between marriage and divorce is significant, which was

expected. The different response-options exclude each other, because one person is not able to have

multiple civil statuses. Therefore these correlations are only a confirmation that the variables are

well-measured. The correlation between marriage and number of children is negative. This may raise

questions on the first sight. It should be kept in mind that the variable number of children measures

the total number of children who live at home. As seen from the literature, more and more people

are getting married after the birth of their child (Centraal Bureau voor de Statistiek, 2001). On the

other hand, marriages do not end when children leave home. As a result, marriages are registered

including children, who leave home before the marriage ends. This may be the reason why the

correlation between marriage and number of children is significantly negative.

The second variable which is analyzed is divorce. As said before, the correlations between divorce

and other civil statuses are significant negative. The correlation between number of children and

divorce is significant negative. When children are born before someone is divorced, and the children

leave home before the civil status has changed, there is a negative correlation between divorce and

number of children.

The correlations between the three most important variables are already discussed. But there is

more, in Table 2 also the two control variables are analyzed, namely age and gender. Both variables

will be discussed now.

The first control variable is age. This is significantly correlated with the civil status. The variables

marriage, divorce and widow are all significantly positive correlated with age. For the variable widow

the significant correlation was expected, because when someone gets older, the probability that the

partner of the respondent dies becomes larger. Also the correlation between age and marriage is

positive. How older people are, the more likely they are to be married, which is taken over by the

variable widow. Switches between the civil status variables are possible over time. After getting

married a switch is possible to the variable divorced or widow. After being divorced or losing the

partner by death it is possible to marry again. Switches are impossible to the variable never been

married. Once a marriage is solemnized it is not possible to return to the variable never been

married. As expected, the correlation between never been married and age is significant negative.

The correlation between age and number of children is significant negative. As seen before, the other

correlations where number of children is involved are negative as well. The reason for this was the

design of the variable number of children. That explanation holds in this case as well. The point is

that in this variable only the number of children who live at home are included. The fact that almost

all children leave home some time might cause the negative correlation. If the regression analysis is

executed again for the respondents aged 25 until 40 years (those are the ages in which people raise

children) the correlation coefficient becomes significantly positive. In this period, children live at

home with their parents.

The only variable which is significantly correlated with gender is divorce. The correlation is positive.

Because gender gives a one for male and a two for female, a positive relation indicates that the

proportion of females that is divorced is higher than the proportion of males that is divorced. This is

probably connected to the fact that children stay living with their mother after the divorce (Starr &

Yudkin, 1996). The childcare may hold back mothers to marry again. When the correlation between

gender and number of children is done for divorced people, the correlation gives a significant positive

correlation, which means that there are more divorced women with home-living children than

divorced men.

Also the variable widow (0: non-widow(er); 1: widow(er)) is significantly positive correlated with

gender. This correlation can be explained by the fact that on average women live longer than men

(Alders & Tas, 2001). The variable never been married (0: married at least once; 1: never married) is

significantly negative correlated with gender. This probably is due to the fact that men have on

average less willingness to marry (Latten, 2004).

Logistic regressionTo investigate the relation between the different variables and entrepreneurship a binary logistic

model is used. Generally this model has the form of:

Entrepreneurship = β0 + β1*Marriage + β2*Divorce + β3*Number of children + β4*Age + β5*Gender + ε

Table 3. Binary logistic regression explaining entrepreneurship.

Coefficient Standard Error

Marriage 0.,062 0.,146Divorce -0.,017 0.,239Number of children -0.,012 0.,051Age 0.,001 0.,004Gender -0.,212* 0.,107Constant -1.,748** 0.,230N 3,381χ2 5.,077p-value 0.,407Pseudo R2 0.,003 * Signifcant at the 1% significance-level** Significant at the 5% significance-level

In this study the model is as follows:

Entrepreneurship = –1.748 + 0.062*Marriage – 0.017*Divorce – 0.012*Number of children +

0.001*Age – 0.212*Gender + e

However it is not possible to use this model for making predictions with explanatory power. The only

significant parts of this model are the gender and the constant.

HypothesisH1: Entrepreneurs are less likely to be married than non-entrepreneurs.

The first hypothesis states that entrepreneurs are generally spoken less often married than

employees. This is examined with an independent samples T-test (Table 1). In the group of non-

entrepreneurs 41.6% state they are married. This is slightly different from the entrepreneur group in

which 44.0% is married. However the difference is not statistically significant. The correlation

between entrepreneur and marriage is not statistically significant (Table 2). In the logistic analyses

(Table 3) the relation between marriage and entrepreneurship is insignificant again. The result of all

Tables is the same: there is no evidence found that entrepreneurs are less likely to be married than

non-entrepreneurs. Therefore, there is no evidence to hold the first hypothesis.

H2: Entrepreneurs are more likely to be divorced than non-entrepreneurs.

The second hypothesis concerns divorced people. 6.85% of the non-entrepreneurs is divorced. In the

entrepreneur group 6.72% is divorced. This difference is not significant, which is confirmed by Table

2. In the logistic regression, the relation between being an entrepreneur and the civil status is

statistically insignificant again. There is no evidence that the proportion of divorced people is higher

among the entrepreneurs than among the non-entrepreneurs. Therefore there is no evidence to hold

the second hypothesis.

H3: Entrepreneurs are likely to have more children than non-entrepreneurs.

The last hypothesis supposes a difference between entrepreneurs and employees in the number of

children living at home. Entrepreneurs have on average 1.18 children at home, while non-

entrepreneurs have on average 1.15 children at home. This difference is not statistically significant.

Between entrepreneur and number of children is no significant correlation. This is confirmed in the

logistic regression. Therefore no evidence is found which indicates a difference in the number of

children between entrepreneurs and non-entrepreneurs. Hence there is no evidence to hold the

third hypothesis.

6. ConclusionThe aim of this paper is to investigate if an entrepreneur differs from a non-entrepreneur in terms of

family composition. Firstly, this study investigated the dependent variable entrepreneur. There are a

lot of different definitions of an entrepreneur in literature. As thoroughly explained in the first

chapters, this study uses the variable business-owner to indicate a person is an entrepreneur. With

this definition, a person who has a job but does not own a business, is equal to an employee.

The indications for civil status are used for a long time, but the demographical figures of the civil are

not stable. Developments are visible in how the people choose for a certain civil status. After the

sexual revolution of the sixties it is accepted to live together without a marriage. Even a lot of

children are born outside a marriage nowadays, something that was unthinkable before the sexual

revolution. The civil status and the size of the family, expressed in the number of children, are seen

as the basic elements of a family composition. Therefore those variables are used to investigate if the

entrepreneurs and non-entrepreneurs differ in family composition. This is tested on basis of three

hypotheses. The first hypothesis assumes that entrepreneurs are less likely to be married, but there

is no statistical evidence that this hypothesis holds. The second hypothesis suggests that the divorce

rate among entrepreneurs is higher than among non-entrepreneurs. From the data, there is no

indication that there is a significant difference in number of divorced people between the two

groups. The last hypothesis states that the entrepreneur has less children compared to non-

entrepreneurs. However there is no solid indication for this in the data.

The research question “Do entrepreneurs and non-entrepreneurs differ in family composition?” has to

be answered with no, there is no difference found between the entrepreneurs and non-entrepreneur

in family composition. However there are certainly differences between an entrepreneur and non-

entrepreneur. Concerning this research, significant differences are found in the control-variable

gender. Most entrepreneurs are men and most non-entrepreneurs are women.

7. Limitations and further researchBeing complete is utopia. This research has its own focus and sub-optimal approach. Therefore this

section describes the limitations of this research and provides recommendations for further research.

First of all there are some characteristics of the dataset that have influence on the research. The

dataset contains a lot of observations and variables, therefore the dataset is recommended for

further research. Although everyone who has done statistics wants a larger dataset, all statistics

which are done in this study have enough observations to draw some reasonable conclusions. Beside

this accolade, there are points of critique at the address of the dataset.

Firstly, the entrepreneurs are measured as business-owners. As seen before, this is a complex

phenomenon in literature. In this research, the entrepreneur is defined as a business-owner. There

are a number of scientists who do not agree that this definition could be used to measure

entrepreneurship. This should be kept in mind during further research. However, a bigger problem is

that it is impossible distinguish entrepreneurs on basis of different types of entrepreneurship. In the

literature, different types of entrepreneurship are described. There are two main reasons

distinguished to start up a business, namely the refugee-effect and the innovation-effect. People who

start a business because they are otherwise unemployed are different from people who start a

business because they see a business opportunity (Thurik, Carree, Stel, & Audretsch, 2008).

Therefore it is expected that the innovation-driven entrepreneurs differ stronger from non-

entrepreneurs compared to refugee-entrepreneurs. Besides this, entrepreneurship differs among

regions and industries. Therefore it is important to see in which regions or industries the difference

between the entrepreneur and the non-entrepreneur is most evident. In the used dataset no

distinction is possible between the different type of entrepreneurs, regions or industries. However

this is an opportunity for further research.

Secondly, the civil status variable lacks several options. There is only one option which indicates that

someone is not married yet. All the other response options implicates that the person has been

married at least one time. As seen in the literature, there are a lot of other possible relationships.

Response options like living together without being married and living apart together (LAT) should be

included.

Besides this, there is no cross-sectional variable indicating how many relations, in particular how

many marriages, the responded has had, despite the effort which is done in the research to picture

families and relationships. Therefore there is no indication of the total number of partners someone

has lived with. Especially for the divorce-variable this would be interesting, because with the divorce

history it is visible if a particular subpopulation has a significant difference in the number of divorces.

This reveals if any subpopulation is more serial-polygamous than the total population. For further

research, it should be investigated if there is a difference between the entrepreneur and the non-

entrepreneur in serial-polygamousness.

Also, the variable about the number of children is complicated. In the dataset there is asked for the

number of children living at home. It is even possible that the respondent is one of the children.

Therefore the variable does not only measure the number of children of the respondent. There are

also cases that the respondent indicates how many siblings he or she has in the same house. This

results in a spurious correlation between age and number of children. The other phenomenon which

makes the correlation complex is children leaving home at a certain moment. This has so much

impact on the correlation that the correlation is significant positive, which means that the number of

children decreases over time. In the age of the growing families (age between 25 and 41) the

correlation is significantly positive. For further research, this should be kept in mind.

In this research only one wave of the panel data is used. This has several advantages, like easiness to

interpret and do statistics and the number of observations. There is also a large disadvantage,

namely the lack of possibility to infer causality. If this research would be done with the panel data, it

is possible that a causality of the occupation on the family composition or vice-versa may become

visible. If this research is done in the future, the researcher should keep in mind that the occupation

and family composition can be a result of the personal properties of the respondent.

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