Entrepreneurial status and health: investigation of ZZP and ZMP in the Netherlands

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Entrepreneurial status and health: investigation of ZZP and ZMP in the Netherlands

By

NYNKE DE JAGER

University of Groningen

Faculty of Economics and Business

MSc BA - Small Business and Entrepreneurship

2015/2016

Supervisor: F. Noseleit

Co-assessor: dr. W.W.M.E. Schoenmakers

Tiete Weverstraat 11

8801 BK Franeker

n.c.de.jager@student.rug.nl

student number 2032562

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2 Abstract

The purpose of this research is to give insight in the relationship between entrepreneurship and health. Earlier research shows still debate about whether entrepreneurship has a positive or negative influence on health. This research argued that the difference in the Netherlands between being self-employed with employees (ZMP-ers) or self-employed without employees (ZZP-ers) could solve this debate. It can be argued that ZZP-ers do not benefit optimally from the advantages of being employed and do face the drawbacks of being self-employed. This influences the health of the entrepreneurs negatively, i.e. the health of ZZP-ers is lower than the health of ZMP-ZZP-ers.

This study used the European Social Survey 2014 to study this relationship. The analysis revealed that there is no significant difference in perceived health between employees and self-employed with personnel and self-employed without personnel. Future research can build on this research, for example, by using multiple measures of health and entrepreneurship.

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3 Table of content Abstract ... 2 Table of content ... 3 Introduction ... 4 Background ... 5

Definitions entrepreneurship and health ... 5

The relationship between entrepreneurship and health ... 6

Heterogeneity among self-employed ... 8

Methodology ... 10

Data collection ... 10

Validity and reliability ... 10

Measurements ... 11 Data preparation ... 12 Descriptive statistics ... 12 Results ... 13 Correlation analysis ... 13 Control variables ... 13

The (in)difference between health of employees and self-employed ... 14

Self-employed with personnel versus self-employed without personnel ... 15

Discussion and conclusion ... 17

Limitations and future research ... 18

References ... 20

Appendix A: Industry categorization ... 1

Appendix B: Distribution of employees, ZZP-ers and ZMP-ers among industries ... 2

Appendix C: Descriptive statistics of independent and dependent variables ... 3

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Appendix E: Coefficients and significance levels of control variables ... 6

Appendix F: independent samples t-test hypothesis 1 ... 7

Appendix G: SPSS output linear regression hypothesis 1 ... 8

Appendix H: SPSS output ordinal regression hypothesis 1 ... 11

Appendix I: SPSS output linear regression hypothesis 2 ... 15

Appendix J: SPSS output ordinal regression hypothesis 2 ... 18

Introduction

'We have no compensation available, but you get a very nice stage, accounting for exposure #tothebaker'1 Franka Hummels on Twitter

On Monday 14th of December 2015 Franka Hummels started on Twitter with the hashtag #tegendebakker (in English #tothebaker) to gain more media attention for the way customers treat freelancers and/or self-employed without personnel. From that moment complaints were pouring in from freelancers and self-employed without personnel (in the Netherlands also known as zelfstandige zonder personeel, ZZP-er) about the way clients treat them. These clients hire ZZP-ers to avoid employment costs such as insurances and taxes, and they want to pay as little as possible. As Prantl (2015) states: 'They try to get you working without any

rights, and without paying. As if you would say to the baker, we do not have any compensation available for your bread, but we can offer you a stage, accounting for exposure'.

Unfortunately, a lot of clients expect ZZP-ers to deliver excellent work for (almost) no financial compensation. Probably, a baker or a butcher would not accept no compensation, so why should ZZP-ers? The competition is killing for ZZP-ers, because of the enormous supply of non-professionals which offer their services on the side (Gorissen, 2016). The group of self-employed without personnel has been exploding since approximately 6 years. The Chambre of Commerce of the Netherlands reports an increase of 51% in the number of

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5 companies from 2010-2015 (Chambre of Commerce, 2015). Also newspapers pay attention to the growing number of entrepreneurs in the Netherlands, and especially point out the important role of ZZP-ers in this increasing number. Trouw mentions that 94 percent of all starters (more than 150.000 people) in 2014 were ZZP-ers (ANP, 2014) and Volkskrant published an article with the title: 'Holland, ZZP-land. More sole proprietorship than ever' (de Waard, 2016). Because of the increasing number of ZZP-ers and therefore increasing competition, ZZP-ers may feel forced to work (maybe too) hard for too little compensation. What effect does this trend have on ZZP-ers? No imagination is required to agree that this trend may give many ZZP-ers headaches and stress, having detrimental effects on health.

Until now, ZZP-ers are a group of entrepreneurs which has not been studied frequently, besides in commission of the government. In general, entrepreneurs are relatively infrequent studied in occupational health psychology (Stephan and Roesler, 2010). There has been some research on health of entrepreneurs, but there is no conclusive evidence yet whether being an entrepreneur has positive or negative consequences for health. An explanation for this inconclusiveness is "the variation in choice of comparison groups and the primary reliance on self-reported health" (Stephan and Roesler, 2010: 718). This research argues that this inconclusiveness also could be explained by the heterogeneity among entrepreneurs. Entrepreneurs can be found in any industry, carrying out their duties in various areas. This research will try to solve this inconclusiveness by enhancing the heterogeneity of the entrepreneurs and therefore dividing the entrepreneurs in subgroups. The earlier mentioned group of ZZP-ers face different challenges than other entrepreneurs, and therefore the distinction between ZZP-ers and ZMP-ers (self-employed with personnel) will be made in this research.

Firstly, the concepts of entrepreneurship and health will be explored. Secondly, the relationship between entrepreneurship and health in general will be examined. Subsequently, the distinction between ZZP-ers and ZMP-ers will be made and it will be investigated whether the health of ZZP-ers, ZMP-ers and employees differs.

Background

Definitions of entrepreneurship and health

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6 be used, to be able to compare studies. This occupational definition stems from for example Chay (1993) and Verheul, Wennekers, Audretsch and Thurik (2002) and states that

entrepreneurship is equivalent to self-employment and business ownership ("i.e. entrepreneurs are people working for their own account and risk", Stephan and Roesler, 2010: 718).

Prior research shows all kinds of benefits and drawbacks of being an entrepreneur. Commonly mentioned advantages of entrepreneurship are financial gains, independency, freedom, flexibility, personal fulfillment, increased satisfaction and growth opportunities (Alstete, 2008). On the other hand, research shows that entrepreneurs experience

disadvantages compared to employment, for example higher levels of role ambiguity and lower job satisfaction compared to their managerial colleagues (e.g. Buttner, 1992 and Harris, Salstone & Fraboni, 1999). Furthermore, entrepreneurs work long hours, can have difficulties with keeping home and work separately, have large business responsibilities and run risks (e.g. Alstete, 2008 and Uy, Foo & Song, 2012). Alstete (2008) argued that these findings are "in agreement with the books on entrepreneurship (Katz and Green, 2007; Kuratko and

Hodgetts, 2007; Longenecker [Moore, Petty and Palich], 2006) which state various issues that entrepreneurs must be prepared to encounter, such as long hours, risk management, hiring policies, management duties and financing challenges" (p. 590).

It is commonly known that these drawbacks of entrepreneurship can have negative health consequences. In this research health is defined as "a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity" (WHO, 1948). However this definition is the most quoted, it is also criticized because this definition is very broad and difficult in measuring and achievement (Kindig, 1997). He states that the difficulty in defining health stems from the everyday usage of the word: "To most of us, being healthy or unhealthy is a part of life's routine, and is most often related in our minds to the presence or absence of disease" (Kindig, 1997: 1). Kindig (1997) also mentions that modern thinking does support that health is more than just the absence of diseases, but also less than complete well-being. This study is interested in general health, including more than just the presence or absence of diseases and therefore the broad definition of WHO will be used.

The relationship between entrepreneurship and health

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well-7 being and behavior. Examples of these negative effects of heavy job demands include among others anxiety and depression (Jex and Beehr, 1991 in Prottas and Thompson, 2006), exhaustion and fatigue (van Yperen and Janssen, 2002) and work strain can also lead to absenteeism (Darr & Johns, 2008). More specifically, Cardon and Patel (2015) mention that "stress is known to cause health-related problems such as high blood pressure, weight gain, and dysfunctional coping through smoking or drinking" (p. 384).

In short, Cardon and Patel (2015) found evidence that entrepreneurs have more stress than employees and that stress has a negative effect on physical health. Therefore, the following hypothesis can be formulated:

H1a: Entrepreneurs are less healthy than employees

On the other hand, it also has been argued that being an entrepreneur has positive health consequences. Stephan and Roesler (2010) used the job-demand-control (JDC) model to substantiate that entrepreneurs are healthier than employees. Karasek (1979) introduced this JDC-model which describes the health consequences of the combination of job control and job demands. Job control is defined as "decision-making authority that job incumbents have in their job over when and how to do their tasks as well as being able to use and develop their skills" (Stephan and Roesler, 2010: 718). Entrepreneurs have very high decision-making authority since they are their own boss and can decide how to do their tasks and how they want to use and develop their skills (e.g. Hébert & Link; 1989; Rau et al., 2008 in Stephan and Roesler, 2010). Research shows that entrepreneurs experience high job control and more job control than employees (e.g. Parslow et al., 2004; Prottas & Thompson, 2006; Rau et al., 2008; Stephan, Lukes, Dej, & Richter, 2005 in Stephan and Roesler, 2010) and that high job control has positive health consequences (e.g. de Lange, Taris, Kampier, Houtman & Bongers., 2003). Because entrepreneurs experience higher job control than employees, it can be argued that entrepreneurs experience better health than employees.

Job demand is defined as "experienced work intensity such as time pressure and conflicting demands" (Stephan and Roesler, 2010:718). Most research shows that entrepreneurs work more hours (e.g. Eurofound, 2010; 2012) and experience higher job demands than employees (e.g. Rau et al., 2008; Stephan et al., 2005 in Stephan and Roesler, 2010).

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8 job incumbent feels like (s)he can cope with them effectively (Karasek & Theorell, 1990; Theorell & Karasek, 1996; Holman & Wall, 2002 in Stephan and Roesler, 2010). Active jobs have positive health consequences: lower mortality rates, less atherosclerosis, and higher well-being (Amick et al., 2002; Rosvall et al., 2002; Tsutsumi, Kayaba, Hirokawa, & Ishikawa, 2006; Wall, Jackson, Mullarkey, & Parker, 1996 in Stephan and Roesler, 2010). Summarizing, because entrepreneurs have more job control and because entrepreneurs are more likely to experience 'active' jobs than employees, which both are said to have positive health consequences, the following hypothesis can be formulated:

H1b: Entrepreneurs are healthier than employees

Summing up, there are two competing models of explaining the health of entrepreneurs. On the one hand, it can be argued that the health of entrepreneurs is worse than the health of employees because entrepreneurs have more stress. On the other hand, one could say that entrepreneurs experience better health than employees because entrepreneurs are more likely to have active jobs which are known to entail positive health consequences.

Heterogeneity among self-employed

A possible explanation for this discrepancy is lack of acknowledging the heterogeneity of the group entrepreneurs. Therefore, in this research an attempt will be made to enhance this heterogeneity, by distinguishing two groups of entrepreneurs. As mentioned earlier, only since decades the self-employed are distinguished in self-employed with personnel (ZMP-er) and self-employed without personnel (ZZP-er). According to the CBS a ZZP-er is an

individual who's main job is to work on own account or at own risk in an own company or practice, or in an independent occupation and has no employees (Hooftman et al., 2013).

The largest difference between ZZP-ers and ZMP-ers is, of course, that ZMP-ers have employees and ZZP-ers do not. As a consequence, in contrary with the ZMP-ers, absence of the ZZP-er, for example because of sickness, causes that the whole company is down and that there is no income for the ZZP-er. This leads to 47% of the ZZP-ers to work when they are actually sick (Hooftman et al., 2013). Hooftman et al. (2013) also found that the smaller the company, the less health and safety actions are taken. Working when you are actually sick and having a minimum of health and safety actions will not contribute positively to the

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9 Secondly, financial gains of ZZP-ers are disappointing compared to the financial gains of ZMP-ers. The CBS (2014) showed that the average yearly personal income level of ZZP-ers in 2012 was lower (almost 33 thousand Euros) than the average yearly pZZP-ersonal income level of ZMP-ers (more than 53 thousand Euros). Therefore, ZMP-ers benefit more from the financial advantage of being an entrepreneur than ZZP-ers.

Furthermore, Aerts (2007) states that these small self-employed are (economically) just as dependent on their client as employees are on their employer. Therefore it can be argued that these self-employed without personnel do not have very much freedom. Larger self-employed, such as ZMP-ers, are less dependent on individual clients and therefore have more freedom.

Another important difference between ZZP-ers and ZMP-ers is that entrepreneurs with employees more often work on their own job site whereas entrepreneurs without employees more often work at home or at the client's (Ybema et al., 2012). Therefore, it can be argued that for entrepreneurs without employees it is harder to keep home and work separately than for entrepreneurs with employees.

Lastly, also political figures worry about the ZZP-ers. For example, Member of

Parliament in the Netherlands Mei Li Vos (PvdA) stated that it is difficult for ZZP-ers to grow into a medium-small business, because of limited access to financial capital and unattractive tax rules (Captein, 2014). She also states that for ZZP-ers it is risky to hire personnel, because of the high costs. CBS (2014) recognizes this, from the self-employed which started in 2007 without employees, only two percent of this group had employees after one year. After four years this percentage was 3.2 percent.

Summarizing, ZZP-ers can reap less benefits of being an entrepreneur than ZMP-ers, and do suffer from the drawbacks which come with entrepreneurship. Because ZZP-ers have no employees, lower financial gains than self-employed with personnel, limited freedom because of dependency on client, limited ability to grow in terms of employees and are less able to separate home and work than ZMP-ers, ZZP-ers are likely to have more stress than ZMP-ers. As earlier mentioned, having stress is negatively related to health and therefore it can be argued that ZZP-ers are less healthy than ZMP-ers. Therefore the following hypothesis is formulated:

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10 In short, entrepreneurship can have positive health consequences, at least when the entrepreneur has employees. In figure 1 a conceptual model can be found which shows the distinction of jobs and predicted health.

Figure 1: Conceptual model

Methodology Data collection

For this research only secondary data will be used. To avoid reliability and validity issues, a well-known database has been selected, namely the database of the European Social Survey (ESS) 2014. The website of the ESS (http://www.europeansocialsurvey.org/) mentions that this survey has been conducted every two years since 2001 in more than thirty nations, measuring attitudes, beliefs and behavior patterns. The survey of 2014 is filled in by 28221 respondents in 15 countries. In the Netherlands 1919 respondents filled in this survey.

Validity and reliability

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11 paying attention to proper operationalization of the different concepts, (2) using the Survey Quality Predictor (SQP) program to assess the structure of questions, (3) designing

experiments to evaluate measures tested in the pilot, (4) translated questionnaires are checked with the SQP on comparability, (5) a supplementary questionnaire is designed to the main questionnaire, to estimate validity and reliability of a limited set of measures and (6) optimal composite scores are estimated for the complex contexts (ESS, n.d.).

Measurements

Health is measured with subjective general health, by asking how the respondent rates his/her health in general (1=very good, 2=good, 3=fair, 4=bad, 5=very bad). This question has been used most frequently in sociological health since the 1950s, but also in epidemiological, medical and economic research (Jylhä, 2009). Among others, WHO recommends this measurement scale for evaluating subjective general health (WHO, 1996).

Following the studies of Stephan and Roesler (2010) and Cardon and Patel (2015) entrepreneurship is measured by one question where participants report whether they are employed (=1) or self-employed (=2).

To make the distinction between self-employed with and without employees, another variable measures the number of employees the respondent had/has. A variable is computed to make the distinction between ZZP-ers (=0), ZMP-ers (=1) and employees (=2).

The most frequent used control variables in similar researches are age, gender, education, occupation, marital status and industry. Therefore in this research these control variables are also included. Self-employed in the Netherlands are more likely to be middle-aged or older people rather than younger people (e.g. CBS, 2014 and Kösters, 2009). Also people tend to report lower health when they become older (e.g. Case and Deaton, 2003). In the Netherlands self-employed are more often men than women (e.g. CBS, 2014) and the biological differences between men and women lead to different types of diseases and health outcomes (WHO, n.d.). Also, men and women perceive their health differently (Ross & Bird, 1994). People with lower education are often associated with poorer health than people with higher education (e.g. WHO, n.d.). Lastly, marital status influences the model in a way that self-employed are more often married or living together than employees (Ybema et al., 2013). Therefore, these control variables are included: gender (1=male, 2=female), education

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12 Data preparation

First of all, the database is delimited to employed and self-employed respondents in the Netherlands which resulted in 1780 respondents. Secondly, to test whether having employees or not when a person is self-employed influences the relationship between entrepreneurship and health, a linear regression will be executed. A linear regression can be used to predict an dependent interval variable by (multiple) independent variables and can also test an

interaction effect. To perform this test, the independent variables must be interval or dummy-variables need to be created for the other dummy-variables. Therefore, dummy-dummy-variables were created for the independent variable entrepreneurship and the moderator which indicates the

difference between employees, ZZP-ers and ZMP-ers. Also, dummy-variables were created for the control variables gender and marital status. Lastly, the control variable industry was aggregated in broader categories which are easier to draw conclusions from than from all the 99 subcategories or 21 categories. I used the distribution of self-employed and employed participants to aggregate multiple into a category 'other'. The employed participants were nicely divided among industry categories, but the self-employed participants were more scarcely and concentrated. Therefore, I put all industry categories with less than 10 self-employed participants and the code 'other service activities' in the category 'other'. The new classification can be found in appendix A. For the variables self-reported health, education and age normality checks have been executed, and the histograms show that these variables are normally distributed.

Descriptive statistics

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13 The aggregation of industry classification resulted in 892 respondents (50,1%)

operating in the 'other' category. An overview of the distribution of employees, ZZP-ers and ZMP-ers can be seen in appendix B and an overview of all descriptive statistics on the sample can be found in appendix C.

Results

Correlation analysis

To explore the relationships between the constructs, first a correlation analysis has been conducted. All variables have been inserted, including the control variables. Here solely the correlations with the variables of highest interest will be discussed, because all other

correlations were less than .300 which indicate very weak correlations. Nevertheless, a complete overview of the correlations can be found in Appendix D.

The correlation analysis showed that subjective general health does not correlate significantly with entrepreneurial status (self-employed or employed) and neither with the variable which distinguished self-employed without personnel and self-employed with personnel (p>0.05).

Another interesting finding is that subjective health does correlate with age, years of full-time education completed and marital status. Older participants report lower health than younger participants (r= .261, p< .01) and higher educated participants report higher health than lower educated participants (r= .195, p< .01). People who live with a partner report slightly higher health than people who do not live with a partner (r= .063, p<.01).

Regarding the entrepreneurial status, of course, a very high correlation is apparent (r= -.959) between entrepreneurial status and the variable which measures self-employed with personnel or without personnel, because it is actually a further specification of entrepreneurial status. Furthermore, entrepreneurial status and gender correlate with each other: employed

participants are slightly more likely to be males than females (r= -.094).

Control variables

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14 age has a negative effect on subjective health (B=.011, p=.000). Number of years of full-time education completed influences subjective health positively, meaning that people with a longer period of time education are healthier than people with a shorter period of full-time education (B=-.031, p=.000).

The (in)difference between health of employees and self-employed

To test hypothesis 1a and 1b the same tests needed to be performed. Because there is still discussion whether a five-point Likert-scale can be interpreted as ordinal or scale

measurement-level, tests at both levels have been performed. Performing all appropriate tests improves the robustness of the results.

The first two tests that have been performed are based on the assumption that the five-point Likert-scale is interval measurement level. These tests are the independent samples t-test and a linear regression.

The goal of the independent samples t-test is to see whether the average subjective health of self-employed differs from the subjective health of the employees. This independent samples t-test was not significant, t(1778)= .827, p= .408 (for SPSS output, see Appendix F). The average subjective health of employees (M= 2.19, SD= .821) does not differ significantly from the average subjective health of self-employed (M= 2.14, SD= .788).

Additionally, to analyze whether self-employment influences subjective health, a linear regression has been conducted. There has been controlled for age, gender, education, marital status and industry. The dependent variable was subjective health. In the first block all the control variables were inserted, and in the second block the dummies for entrepreneurship were added. This regression was not significant (p= .332, for SPSS output see Appendix G). R² stayed .095, F(1,1778) = 16.849. The health of employees and self-employed does not differ significantly, B= -.058, p = .332.

To see whether the results would be consistent when it is assumed that subjective health is measured on ordinal level, also a Mann Whitney U-test (MWU-test) and an ordinal regression have been performed. This MWU-test was not significant, MWU = 160619.5, p = 0.386. Employed (MR = 894.06, median = good health) have the same health as

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15 Figure 2. Subjective health distribution among self-employed and employees

The second test to analyze whether entrepreneurship influences subjective health, an ordinal regression has been conducted, while controlling for age, gender, education, marital status and industry. This ordinal regression showed a coefficient for entrepreneurship of 0.128 but it was not significant (p=.386).

Concluding, all four tests indicate that there is not enough evidence to say that there is a relationship between the entrepreneurial status (self-employed or employed) and the subjective health. In other words, it can be argued that the subjective health of self-employed does not differ from the subjective health of employed.

Self-employed with personnel versus self-employed without personnel

To test whether further specification of the group self-employed can shed another light on the relationship between entrepreneurship and health, multiple tests have been conducted

(because of the earlier mentioned debate on whether a five-point Likert-scale is interval or ordinal measure).

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16 whether the health of ZZP-ers, ZMP-ers and employees differ. The One-Way ANOVA is the appropriate test when it is assumed that subjective health is measured on interval level and the Kruskal Wallis is the non-parametric alternative for the One-Way ANOVA. Both tests were not significant. The Kruskal Wallis test showed a test statistic of .904 (degrees of freedom = 2, p= .636). The One-Way ANOVA was also not significant, F(2,1777) = .582, p=.559 (for SPSS output, see Appendix I). Therefore, it can be said that the health of ZZP-ers, ZMP-ers and employees do not differ significantly. In figure 3 the distribution of self-reported health among employed and self-employed can be seen, with also the distinction between ZZP-ers and ZMP-ers.

Figure 3: Subjective health distribution among ZZP'ers, ZMP'ers and employees

Furthermore, an ordinal and a linear regression have been performed, to test whether having employees influences the relationship between entrepreneurship and health. Again there has been controlled for age, gender, education, marital status and industry. The linear regression was not significant (p=.369). R² stayed .095, F(1,1767) = 16.863. The health of entrepreneurs with employees does not differ from the health of entrepreneurs without employees, B= -.063 , p =.369.

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17 cannot say that health of ZZP-ers and ZMP-ers differs significantly. Concluding, all four tests indicate that there is not enough evidence to say that the relationship between the entrepreneurial status (self-employed or employed) and the subjective health is moderated by having employees or not. In other words, it can be argued that the subjective health of self-employed with personnel, self-employed without personnel and employees does not differ significantly.

Discussion and conclusion

In summary, this research does not provide evidence that health differs among groups with different entrepreneurial status. In contrary, this research suggests that the health is the same among employed and self-employed. Also, the two subgroups of self-employed with personnel and self-employed without personnel do not explain differences in health.

This conclusion is in contrary with earlier research. The most probable explanation is that the average health in the Netherlands is relatively high in comparison to other countries. The average high health can be explained by the high quality of the healthcare system in the Netherlands. The Health Consumer Powerhouse Ltd. has already performed eight studies on healthcare systems in 36 European countries from a consumer/patient view (Björnberg, 2015). This ranking rates several aspects of the healthcare system, for example such as patient rights and information, prevention and accessibility (Björnberg, 2015). The Netherlands has been in the top three of this ranking since 2005 and now still leads the ranking with 898 points on a scale from 1 to 1000 (Björnberg, 2015). This report also mentions that the Netherlands performs high on all aspects of this ranking. Also the Netherlands scores overall the best in another research, where health care experiences were compared in seven countries, i.e. Australia, Canada, Germany, Netherlands, New-Zealand, United Kingdom and United States (Schoen , Osborn, Doty, Bishop, Peugh & Murukutla, 2007). It can be argued that because of the good quality of the healthcare system, problems with health are likely to be prevented and/or fixed accurately.

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18 measure of health is more widely used and more poorly understood than self-rated health (or self-assessed health, or self-perceived health)' (p. 307). Already in 1958, Suchman, Phillips & Streib researched the validity of health questionnaires, and concluded that self-ratings of health can measure something different than 'actual' or 'objective' health. Suchman et al. (1958) state that these self-rating of health is only a valid measure of health, when the purpose of the study is to determine the way people perceive their health, instead of actual medical state. Therefore, the conclusion of this research might be limited to saying there is not enough evidence to say that entrepreneurial status influences the perception of health.

Thirdly, it could be that there still exists too much heterogeneity among entrepreneurs to get significant results. Especially, because in this research health is measured with perceived health, more variables, such as personal characteristics (e.g. ability to cope with stress and capabilities to succeed) or motive to become entrepreneur, could, or maybe even should, have been included.

Limitations and future research

The largest limitation of this research is the use of an existing database, i.e. the European Social Survey 2014. Therefore, the choice of measurements of the constructs was limited. For example, it was not possible to use multiple measures of entrepreneurship. In this research I used a single measurement of health, being the subjective health of participants and a single measurement of entrepreneurship, i.e. whether the participant is self-employed or employed. Future research can elaborate on this research, by using for example not just self-reported well-being as measure of health, but also using additional objective measures of health and behavioral health indicators (following Stephan and Roesler (2010)). Also future research can extend measures of entrepreneurship. From earlier research a wide variety of determinants of entrepreneurship are known. Audretsch (2003:29/30) mention 'a spectrum range of determinants, ranging from economic to historical, psychological, social, cultural and political'.

Secondly, in this research the heterogeneity among ZMP-ers has been ignored. There is not accounted for the size of the company. Future research should not only distinguish ZZP-ers and ZMP-ers, but should also control for the size of the company the ZMP-er has.

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19 control variables more, as this research only included the most mentioned control variables. Furthermore, the problem of reversed causality should be addressed. It could be that there is some kind of self-selection for becoming an entrepreneur. With the research design used for this research, it is not possible to determine the direction of the relationship. Does becoming an entrepreneur influence your health, or does your health influence your decision to become an entrepreneur? To solve this problem, future research can use an instrumental variables analysis (Angrist and Krueger, 1991).

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Appendix A: Industry categorization

1. Agriculture, forestry and fishing 7. Other, i.e.:

Mining and quarrying

Electricity, gas, steam and air conditioning supply

Water supply; sewerage, waste management and remediation activities

Construction

Transportation and storage Information and communication Financial and insurance activities Real estate activities

Professional, scientific and technical activities Administrative and support service activities

Public administration and defense; compulsory social security Education

Other service activities

Activities of households as employers; undifferentiated goods- and services-producing activities of households for own use

Activities of extraterritorial organizations and bodies 2. Manufacturing

3. Wholesale and retail trade; repair of motor vehicles and motorcycles 4. Accommodation and food service activities

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Appendix B: Distribution of employees, ZZP-ers and ZMP-ers among industries Industry Total Agriculture, forestry and fishing Manufacturing Wholesale and retail trade Accommodation and food service activities

Human health and social work activities Arts, entertainment and recreation Other Employees* 50,0% 92,0% 85,0% 72,5% 94,6% 54,5% 90,0% 88,1% ZZP* 37,5% 5,3% 8,4% 11,8% 2,3% 41,8% 7,4% 8,1% ZMP* 12,5% 2,7% 6,6% 15,7% 3,0% 3,6% 2,6% 3,8% Total 100,0% 100,0% 100,0% 100,0% 100,0% 100,0% 100,0% 100,0%

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3 Appendix C: Descriptive statistics of independent and dependent variables

Subjective general health Employment relation Respondent has/had employees

Age Gender Marital

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4 Appendix D: Correlation analysis

Subjective general health Self-employed or employed3 Self-employed with or without employees4 Gender5 Age of respondent, calculated Years of full-time education completed Lives with partner6 Industry 7# Industry 1# Industry 2# Industry 3# Industry 4# Industry 5# Industry 6# Subjective general health2 1 -,020 ,023 ,029 ,261 ** -,195** ,063** ,017 ,037 -,012 -,033 -,001 ,015 -,024 Self-employed or employed3 -,020 1 -,959 ** _-,094** _,060* _,038 _-,020 _-,060* _,159** _-,046 _,037 _,082** _-,091** _,185** Self-employed with or without employees4 ,023 -,959 ** ₁ _,079** _-,037 _-,038 _,009 _,047* _-,160** _,045 _-,023 _-,058* _,098** _-,211** Gender5 ,029 -,094** ,079** 1 -,082** -,078** ,045 -,111** -,003 -,231** ,072** ,049* ,253** ,034 Age ,261** ,060* -,037 -,082** 1 -,194** -,090** ,066** ,057* ,025 -,094** -,069** ,004 -,043 Years of full-time education completed -,195 ** ,038 -,038 -,078** -,194** 1 -,079** ,097** -,100** -,046 -,126** -,071** ,046 ,098**

Lives with partner6 ,063** -,020 ,009 ,045 -,090** -,079** 1 -,040 ,006 -,033 ,076** ,023 -,009 ,026

2_{measured on a scale from 1 (very good) to 5 (very bad)} 3_{1=employed, 2=self-employed}

4_{0= self-employed without employees, 1 = self-employed with employees, 2 = N.A. (i.e. employed)} 5_{1=male, 2=female}

6

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5 Subjective general health Self-employed or employed Self-employed with or without employees Gender Age of respondent, calculated Years of full-time education completed Interviewer code, lives with husband/wife/partner Industry 7# Industry 1# Industry 2# Industry 3# Industry 4# Industry 5# Industry 6# Industry 7# 0,017 -,060* ,047* -,111** ,066** ,097** -0,04 1 -,136** -,381** -,382** -,172** -,450** -,179** Industry 1# 1,037 ,159** -,160** -0,003 ,057* -,100** 0,006 -,136** 1 -,051* -,052* -0,023 -,061* -0,024 Industry 2# -0,012 -0,046 0,045 -,231** 0,025 -0,046 -0,033 -,381** -,051* 1 -,145** -,065** -,171** -,068** Industry 3# -0,033 0,037 -0,023 ,072** -,094** -,126** ,076** -,382** -,052* -,145** 1 -,065** -,171** -,068** Industry 4# -0,001 ,082** -,058* ,049* -,069** -,071** 0,023 -,172** -0,023 -,065** -,065** 1 -,077** -0,031 Industry 5# 0,015 -,091** ,098** ,253** 0,004 0,046 -0,009 -,450** -,061* -,171** -,171** -,077** 1 -,080** Industry 6# -0,024 ,185** -,211** 0,034 -0,043 ,098** 0,026 -,179** -0,024 -,068** -,068** -0,031 -,080** 1

**. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

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6 Appendix E: Coefficients and significance levels of control variables

Unstandardized

Coefficients Standardized Coefficients

B Std. Error Beta t Significance

(Constant) 2.161 .104 20.756 0,000

Gender (males) -.054 .040 -.033 -1.351 .177

Marital status (lives with partner) -.121 .038 -.072 -3.152 .002

Age .011 .001 .232 9.805 .000

Years of full-time education completed -.031 .005 -.147 -6.141 .000

Industry (agriculture, forestry and fishing) .042 .141 .007 .297 .766

Industry (manufacturing) -.047 .059 -.019 -0,792 .429

Industry (wholesale and retail trade; repair of vehicles and

motorcycles) -.094 .059 -.038 -1.581 .114

Industry (accommodation and food service activities) -.020 .114 -.004 -.174 .862

Industry (human health and social work activities) .005 .054 .002 .093 .926

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7 Appendix F: independent samples t-test hypothesis 1

Group Statistics

self-employed or employed N Mean Std. Deviation Std. Error Mean

Subjective general health Employed 1568 2,19 ,821 ,021 Self-employed 212 2,14 ,788 ,054

Levene's Test for Equality

of Variances t-test for Equality of Means

F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference

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8 Appendix G: SPSS output linear regression hypothesis 1

Model Summary Mode l R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df 1 df2 Sig. F Change 1 ,308a ,095 ,090 ,778 ,095 18,441 10 175 7 ,000 2 ,309b ,095 ,090 ,778 ,000 ,942 1 175 6 ,332

a. Predictors: (Constant), dummy for industry 6: arts, entertainment and recreation, dummy for industry 1: agriculture, forestry and fishing, dummy for participant lives with partner, dummy for industry 4: accommodation and food service activities, dummy for males, Age of respondent, calculated, dummy for industry 3: wholesale and retail trade; repair of vehicles and motorcycles, dummy for industry 2: manufacturing, Years of full-time education completed, dummy for industry 5: human health and social work activities

b. Predictors: (Constant), dummy for industry 6: arts, entertainment and recreation, dummy for industry 1: agriculture, forestry and fishing, dummy for participant lives with partner, dummy for industry 4: accommodation and food service activities, dummy for males, Age of respondent, calculated, dummy for industry 3: wholesale and retail trade; repair of vehicles and motorcycles, dummy for industry 2: manufacturing, Years of full-time education completed, dummy for industry 5: human health and social work activities, dummy for participant is self employed

ANOVAa

Model

Sum of

Squares df Mean Square F Sig.

1 Regression 111,537 10 11,154 18,441 ,000b Residual 1062,696 1757 ,605 Total 1174,233 1767 2 Regression 112,107 11 10,192 16,849 ,000c Residual 1062,126 1756 ,605 Total 1174,233 1767

a. Dependent Variable: Subjective general health

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9

with partner, dummy for industry 4: accommodation and food service activities, dummy for males, Age of respondent, calculated, dummy for industry 3: wholesale and retail trade; repair of vehicles and motorcycles, dummy for industry 2: manufacturing, Years of full-time education completed, dummy for industry 5: human health and social work activities

c. Predictors: (Constant), dummy for industry 6: arts, entertainment and recreation, dummy for industry 1: agriculture, forestry and fishing, dummy for participant lives with partner, dummy for industry 4: accommodation and food service activities, dummy for males, Age of respondent, calculated, dummy for industry 3: wholesale and retail trade; repair of vehicles and motorcycles, dummy for industry 2: manufacturing, Years of full-time education completed, dummy for industry 5: human health and social work activities, dummy for participant is self employed

Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 2,161 ,104 20,756 ,000

dummy for males -,054 ,040 -,033 -1,351 ,177

dummy for participant lives with partner -,121 ,038 -,072 -3,152 ,002

Age of respondent, calculated ,011 ,001 ,232 9,805 ,000

Years of full-time education completed -,031 ,005 -,147 -6,141 ,000

dummy for industry 1: agriculture, forestry and fishing ,042 ,141 ,007 ,297 ,766

dummy for industry 2: manufacturing -,047 ,059 -,019 -,792 ,429

dummy for industry 3: wholesale and retail trade; repair of vehicles

and motorcycles -,094 ,059 -,038 -1,581 ,114

dummy for industry 4: accommodation and food service activities -,020 ,114 -,004 -,174 ,862

dummy for industry 5: human health and social work activities ,005 ,054 ,002 ,093 ,926

dummy for industry 6: arts, entertainment and recreation ,012 ,111 ,002 ,105 ,916

2 (Constant) 2,156 ,104 20,681 ,000

dummy for males -,050 ,040 -,031 -1,250 ,211

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10

and motorcycles -,089 ,059 -,037 -1,500 ,134

dummy for participant is self employed -,058 ,060 -,023 -,971 ,332

Excluded Variablesa

Model Beta In t Sig.

Partial Correlation

Collinearity Statistics Tolerance

1 dummy for participant is self employed -,023b -,971 ,332 -,023 ,906

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11 Appendix H: SPSS output ordinal regression hypothesis 1

Case Processing Summary

N Marginal Percentage

Subjective general health Very good 328 18,6%

Good 916 51,8% Fair 418 23,6% Bad 94 5,3% Very bad 12 ,7%

dummy for participant is self employed employed 1557 88,1%

self-employed 211 11,9%

dummy for industry 1: agriculture, forestry and fishing ,00 1736 98,2%

1,00 32 1,8%

dummy for industry 2: manufacturing ,00 1544 87,3%

1,00 224 12,7%

dummy for industry 3: wholesale and retail trade; repair of vehicles and motorcycles ,00 1542 87,2%

1,00 226 12,8%

dummy for industry 4: accommodation and food service activities ,00 1718 97,2%

1,00 50 2,8%

dummy for industry 5: human health and social work activities ,00 1470 83,1%

1,00 298 16,9%

dummy for industry 6: arts, entertainment and recreation ,00 1715 97,0%

1,00 53 3,0%

dummy for males ,00 963 54,5%

female 805 45,5%

dummy for participant lives with partner ,00 678 38,3%

lives with partner 1090 61,7%

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12 Valid 1768 100,0% Missing 12 Total 1780

Model Fitting Information

Model -2 Log Likelihood

Chi-Square df Sig.

Intercept Only

4052,384

Final 3860,747 191,637 11 ,000

Link function: Logit.

Goodness-of-Fit

Chi-Square df Sig.

Pearson 6788,471 6453 ,002

Deviance 3734,201 6453 1,000

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14 [accommodation_foodservice_activities=,0 0] -,011 ,282 ,001 1 ,969 -,563 ,541 [accommodation_foodservice_activities=1, 00] 0a 0 [humanhealth_socialwork=,00] -,011 ,132 ,007 1 ,935 -,269 ,247 [humanhealth_socialwork=1,00] 0a 0 [arts_entertainment_recreation=,00] -,164 ,277 ,350 1 ,554 -,707 ,379 [arts_entertainment_recreation=1,00] 0a 0 [Male=,00] ,093 ,098 ,903 1 ,342 -,099 ,285 [Male=1,00] ₀a 0 [livingwithpartner=,00] ,261 ,094 7,637 1 ,006 ,076 ,446 [livingwithpartner=1,00] 0a 0

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15 Appendix I: SPSS output linear regression hypothesis 2

Model Summary Mode l R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df 1 df2 Sig. F Change 1 ,308a ,095 ,090 ,778 ,095 18,441 10 175 7 ,000 2 ,309b _,095 _,090 _,778 _,000 _,806 ₁ 175 6 ,369

a. Predictors: (Constant), dummy for industry 6: arts, entertainment and recreation, dummy for industry 1: agriculture, forestry and fishing, dummy for participant lives with partner, dummy for industry 4: accommodation and food service activities, dummy for males, Age of respondent, calculated, dummy for industry 3: wholesale and retail trade; repair of vehicles and motorcycles, dummy for industry 2: manufacturing, Years of full-time education completed, dummy for industry 5: human health and social work activities

b. Predictors: (Constant), dummy for industry 6: arts, entertainment and recreation, dummy for industry 1: agriculture, forestry and fishing, dummy for participant lives with partner, dummy for industry 4: accommodation and food service activities, dummy for males, Age of respondent, calculated, dummy for industry 3: wholesale and retail trade; repair of vehicles and motorcycles, dummy for industry 2: manufacturing, Years of full-time education completed, dummy for industry 5: human health and social work activities, new dummy for ZZP

ANOVAa

Model

Sum of

Squares df Mean Square F Sig.

1 Regression 111,537 10 11,154 18,441 ,000b Residual 1062,696 1757 ,605 Total 1174,233 1767 2 Regression 112,024 11 10,184 16,836 ,000c Residual 1062,209 1756 ,605 Total 1174,233 1767

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16

b. Predictors: (Constant), dummy for industry 6: arts, entertainment and recreation, dummy for industry 1: agriculture, forestry and fishing, dummy for participant lives with partner, dummy for industry 4: accommodation and food service activities, dummy for males, Age of respondent, calculated, dummy for industry 3: wholesale and retail trade; repair of vehicles and motorcycles, dummy for industry 2: manufacturing, Years of full-time education completed, dummy for industry 5: human health and social work activities

c. Predictors: (Constant), dummy for industry 6: arts, entertainment and recreation, dummy for industry 1: agriculture, forestry and fishing, dummy for participant lives with partner, dummy for industry 4: accommodation and food service activities, dummy for males, Age of respondent, calculated, dummy for industry 3: wholesale and retail trade; repair of vehicles and motorcycles, dummy for industry 2: manufacturing, Years of full-time education completed, dummy for industry 5: human health and social work activities, new dummy for ZZP

Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 2,161 ,104 20,756 ,000

dummy for males -,054 ,040 -,033 -1,351 ,177

and motorcycles -,094 ,059 -,038 -1,581 ,114

2 (Constant) 2,162 ,104 20,762 ,000

dummy for males -,052 ,040 -,032 -1,305 ,192

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17

and motorcycles -,092 ,059 -,038 -1,560 ,119

new dummy for ZZP -,063 ,070 -,021 -,898 ,369

Excluded Variablesa

Model Beta In t Sig.

Partial Correlation

Collinearity Statistics Tolerance

1 new dummy for ZZP -,021b -,898 ,369 -,021 ,921

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18 Appendix J: SPSS output ordinal regression hypothesis 2

Model Fitting Information

Model -2 Log Likelihood Chi-Square df Sig.

Intercept Only 4053,771

Final 3862,542 191,228 11 ,000

Goodness-of-Fit

Chi-Square df Sig.

Pearson 6792,577 6449 ,001

Deviance 3737,382 6449 1,000

Pseudo R-Square

Cox and Snell ,103

Nagelkerke ,113

McFadden ,046

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19 Parameter Estimates

Estimate Std. Error Wald df Sig. 95% Confidence Interval

Lower Bound Upper Bound

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20

[Male=,00] ,100 ,098 1,043 1 ,307 -,092 ,291

[Male=1,00] 0a 0

[livingwithpartner=,00] ,263 ,094 7,744 1 ,005 ,078 ,448

[livingwithpartner=1,00] 0a ₀