Status and entrepreneurship

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Status and Entrepreneurship

Thomas Hemels and Taco Slagter

1st Supervisor: Prof. dr. C.M. van Praag 2nd Supervisor: Prof. dr. N.M. Wijnberg

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1 Introduction

Entrepreneurial activity is considered to be the engine of the economy. Evidence indicates that entrepreneurial activity leads to economic growth, job creation and innovation (Van Praag and Versloot, 2007 forthcoming). These benefits motivate policy measures to stimulate entrepreneurship. In a recent new start for the Lisbon strategy, the members of the EU emphasized the role of the development of entrepreneurship in order to reach higher rates of innovation and job creation. The importance of promoting a more entrepreneurial culture and creating a supportive environment for entrepreneurs was stressed.1

One factor that could play a role in motivating entrepreneurship is raising its social status. For this, research is required to analyze which factors are relevant for determining the socio-economic status of entrepreneurship as a profession as well as the relationship between individually perceived status and entrepreneurship ambitions. This is just what our thesis is providing.

Recently, economists have become increasingly interested in concepts such as social status due to the recognition that economic theory fails to explain a number of socio-economic phenomena due to ignoring the importance of possible interdependencies of preferences across people (Bisin and Verdier, 1998). The social status of a profession is possibly affected by other peoples’ preferences for the profession. In turn, the status itself may affect peoples’ preferences. Malach-Pines et al. (2005) showed tentative evidence, in a cross-country study amongst MBA students, of a correlation between the perceived social status of entrepreneurs and the level of entrepreneurial activity in a country. In this thesis, we proceed by exploring the social status of entrepreneurs, perceived by a sample of Dutch students, and their entrepreneurial intentions. Moreover, we analyze its determinants and its possible relationship with individuals’ intentions to become an entrepreneur. Possible determinants that are considered are the determinants of (successful) entrepreneurship activity, for instance risk attitude, social and human capital. Special attention is paid to the factor human capital.

1

See for example ‘Implementing the Community Lisbon Strategy: Fostering Entrepreneurial Mindsets through Education and Learning’ by the Commission of the European Communities (2006).

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We motivate our focus on university students and their perception of the status of entrepreneurship based on Van der Sluis et al. (2005b), who found that the return to education is very high for entrepreneurs, even 37 percent higher than for employees. Therefore, from a policy perspective, it is important to find instruments to motivate this group to become entrepreneurs, and one such instrument might be status.

This is especially important in the Netherlands. In general, evidence shows that the desirability of becoming an entrepreneur is lower in the EU than in the US (CBS, 2006). Moreover, Blanchflower (2004) shows that the probability of being self-employed is not positively related to one’s schooling level in the EU, whereas a positive relationship between education and the probability of being an entrepreneur is found for the US. Especially in the Netherlands, people with higher levels of education do not really consider entrepreneurship as a career option. Amongst Dutch students, only 9 percent is considering to start up a company.2 So it seems that an entrepreneurial culture is lacking in the Netherlands, especially amongst higher educated individuals. As their returns and benefits are highest, and higher than in wage employment (due to the high returns to education), this is a particularly interesting group to study.

To summarize, the main question of this study is: ‘What is the perceived status of

entrepreneurs among university students in the Netherlands and what are the determinants of the perceived status?’. And does status affect the willingness and likelihood of becoming an entrepreneur?, i.e. are we able to stimulate entrepreneurship through status?’.

This thesis proceeds as follows. In the next chapter we review the fields of entrepreneurship and occupational status. In Chapter 3, we describe the methods used and give insight into the determinants considered. We then proceed with the descriptive statistics in Chapter 4. Regression results are presented in Chapter 5. In Chapter 6, we further discuss the results. Chapter 7 concludes and discusses implications.

2

Rutte (2005) ‘Nederland mag niet indutten!’, website of the Dutch Ministry of Education, Culture and Science.

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2 Theoretical background

This thesis is a combination of two fields of research: entrepreneurship and occupational status. This chapter provides a short background of both fields. We concluded this chapter by linking entrepreneurship and occupational status.

2.1 Entrepreneurship

2.1.1 Definition

When defining entrepreneurship in terms of occupation, certain difficulties arise as most definitions merely refer to the new venture creation or otherwise use personal traits or behavioural aspects. An example of this kind of definition is given by Bianchi and Henrekson (2005, p.355);

“The ability and willingness of individuals, both on their own and within organizations to: (1) innovate, i.e. perceive and create new economic opportunities; (2) face uncertainty, i.e. introduce their ideas in the market, by making decisions on location, from and the use of resources and institutions; and (3) manage their business by competing with others for a share of that market.”

In an attempt to synthesize the entrepreneurial literature in order to foresee in operational definitions Carton, Hofer and Meeks (2004, p. 8) come up with the following;

“Entrepreneurship is the pursuit of a discontinuous opportunity involving the creation of an organization (or sub-organization) with the expectation of value creation to the participants. […] The entrepreneur is the individual (or team) that identifies the opportunity, gathers the necessary resources, creates and is ultimately responsible for the consequences of the organization.”

In reality the difference between an entrepreneur and a non-entrepreneur is not so obvious (Van Praag, 2005, p. 5). There is a wide area of labour market positions stretching from ‘heavily entrepreneurial’ to ‘totally non-entrepreneurial’. No clear border is defined such that an individual is considered an entrepreneur on one side of that border

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and not on the other side. This continuum of labour market positions is shown in Figure 2.1.

Figure 2.1 Unclear definition of entrepreneurship

In the empirical literature, more down to earth definitions are used. Van Praag uses such a definition (2005, p.5);

“An entrepreneur is someone who indicates either that (s)he has started a business venture alone or with a group or that (s)he has acquired a (family) business, alone or with a group”.

We (implicitly) use a subjective definition of an entrepreneur in our research, which is not based on determinants of (successful) entrepreneurship. We ask respondents to rank entrepreneurs amongst other occupations according to their own definition of an entrepreneur. This is similar to the method used by Van Praag, i.e. having respondents state themselves whether they are entrepreneurs or not (2005, p.6).

2.1.2 Determinants of (successful) entrepreneurship

2.1.2.1 Human capital

Human capital is defined as an investment in skill and knowledge that boosts earning power (Becker, 1964). Human capital, both initial and acquired, is an important determinant of entry into entrepreneurship and of entrepreneurial success (Van Praag, 2005, Lazear, 2004, Cooper et al., 1994).

Education (levels and years of education) is one of the most frequently examined components of human capital. Presumably education is related to knowledge,

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skills, problem-solving abilities, discipline, motivation and self-confidence. Taken together, these might be the reasons that higher educated entrepreneurs perform better than lower educated entrepreneurs. This might attract educated people into self-employment. There is mixed evidence though, about the relationship between education and entrepreneurship selection (Van der Sluis et al., 2005a). There is abundant evidence that education has a positive association with entrepreneurship performance, measured in various ways (Cooper et al., 1994; Ucbasaran et al., 2007 forthcoming). Van der Sluis et al. (2005b) show that the return to education is higher for entrepreneurs than for employees by using instrumental variables techniques and taking account of selectivity.

Jack-of-All-Trades An important theoretical contribution to the field of human capital and entrepreneurship is made by Lazear (2002, 2005), who considers the combination of abilities rather than absolute levels of abilities. His theory states that the likelihood of entrepreneurial selection and performance are increased by a broad set of balanced competences across different fields, rather than specialization in one competence. Hence, entrepreneurs would be “Jacks-of-all-trades” rather than specialized experts. Empirical evidence has supported this claim in various manners (for instance, Lazear, 2002; 2005; Hartog, Van der Sluis and Van Praag, 2007)

Entrepreneurial experience Human capital is not only related to (formal) education, it is also formed by labor market experience. We consider two kinds of experience: experience as an entrepreneur and labor market experience in general. In general, empirical evidence indicates that the likelihood and success of entrepreneurship is related to previous entrepreneurship experience (Van Praag, 2005, p. 48, Davidsson and Honig, 2003). The relationship between the willingness to become an entrepreneur and (previous) experience is not so straightforward, since the experience can have been positive or negative.

Labor market experience Labor market experience is theoretically predicted to increase human capital (Becker, 1964). Labour market experience is significantly positively related to entrepreneurial activity, after controlling for factors such as gender (Bates, 1995; Gimeno et al, 1997). It assists in the integration and accumulation of new knowledge, as well as integrating and adapting to new situations (Weick, 1996). These are important aspects of entrepreneurship. However, job experience is not only related to

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human capital. It is also positively correlated with social capital (Bourdieu, 1983; Loury, 1987) and strongly correlated with age.3

Creativity Another important human capital variable, which has received only little empirical attention, yet, is creativity. Creativity and innovativeness (see below) are closely related. West and Farr (1990, p.252) state that innovation is the conception of a new idea, transformed into an invention, and exploited as much as possible, whereas creativity is merely the conception of the idea. According to Kuratko (2004, p. 11), entrepreneurship is about continual innovation and creativity.

There is some evidence that creativity enhances the likelihood of becoming an (successful) entrepreneur (Heunks, 1998; Nandram and Samson, 2000, p. 11; Hood and Young, 1993; Driessen and Zwart, 1999).

Innovativeness is an important aspect of entrepreneurship and has been subject to more empirical research than creativity. Schumpeter (1911) was the first to treat innovation as an endogenous process and thereby positioned the entrepreneur as leader of the firm and as innovator. This made him the prime mover of the economic system (Van Praag, 2005, p. 19). Schumpeter defines an entrepreneur as a person who introduces new combinations in whatever position. Acs and Audretsch (2003) have indeed shown that smaller and younger firms are relatively more innovative than larger and older firms.4 Heunks (2004) finds that young firms are as innovative as older ones (p. 267).

Early-stage entrepreneurs claim more often to offer innovative products than established entrepreneurs. The Global Entrepreneurship Monitor 2004 shows that 13% of the early-stage entrepreneurs in the Netherlands launch a new product-market combination and that 7% claims to use a new technology (GEM, 2004, p. 51).

2.1.2.2 Background

Gender It is a stylized fact that in western industrialized countries the share of men in total self-employment is much higher than the share of women (see for example OECD 2000; Davidsson and Honig, 2003, Global Entrepreneurship Monitors).5 Blanchflower

3_{The correlation between age and the number of jobs is .309 and is significant, see Table A5.1 in Appendix}

C5. The correlation is however not strong enough to cause problems within the regressions.

4

Small firms contributed around 2.4 times as many innovations per employee as large firms did.

5

The Global Entrepreneurship Monitor (GEM 2006) shows that, in general, men are significantly more likely to start a business than women in all countries except for the Philippines.

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(2004, p.34) provides similar empirical evidence, based on millions of individuals recently sampled in a large number of countries. The GEM 2006 shows that the gender gap differs between countries. In the Netherlands there are more than twice as many male entrepreneurs than female.6 Fairlie (2005, p. 27) shows that women do not only have lower rates of entry into self-employment, they have also substantially higher exit rates from self-employment than men.7 Van der Sluis et al. (2005b) show that the gender income gap is higher for entrepreneurs than for employees.

Age Early stage entrepreneurial activity is most prevalent in the age group of individuals 25-34 years old, and least prevalent in the 55-64 year old group, (GEM, 2006). In research by Van Praag (2005, p.48) on the willingness and opportunity to start up as an entrepreneur, she concludes that 24 year olds have the worst opportunity to start up a business and that 23 year olds are the most willing to start up a business. Blanchflower (2004, p. 18) also shows that younger people say that they would prefer to be self-employed, despite the fact that older people are more likely to be self-employed.

Concerning the success of an entrepreneur, Van Praag (2005) finds an inverted U-shaped correlation between starting age and performance using a sample of young white male self-employed in the United States. Based on the duration in self-employment and incomes, there is a peak in most cases around the starting age of 34. The evidence on starting age is quite uniform according to Van Praag, and these findings hold across countries and sexes (2005, p.161).

Cultural background As discussed in the occupational status theory, there is an ongoing discussion in this field of research on the consistency among cultures of the perceived status of occupations. Entrepreneurship research addresses cultural differences and its effects on entry into entrepreneurship success too.

An important issue for Dutch entrepreneurship policy is to increase participation in entrepreneurial activity among people with a cultural or ethnic background (GEM 2006, p.49). The Monitor of Ethnical Entrepreneurship measures the number of entrepreneurs per 1.000 persons in the working population within ethnic groups and shows that this quote has risen much stronger among non-Western minorities than in the

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16.49 percent versus 7.42 percent in 2006.

7

Slightly more than one third of all self-employed women leave by the following year compared to one fourth of self-employed men.

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Dutch population in the period of 1999-2002 in the Netherlands (2004, p. 15). Kloosterman and Rath (2000) find that immigrants from less developed countries in the Netherlands have fewer opportunities in employment and are often forced into entrepreneurship. Blanchflower et al. (2003) find evidence that minorities face discrimination when trying to get credit (in the UK).

The relationship between being part of a cultural or ethnic minority in the Netherlands and the perceived status of the entrepreneur is somewhat ambiguous. The status could be lower, as less people from minorities become entrepreneurs and when they do, they have difficulties finding credit and have lower earnings. On the contrary, the entrepreneurship quota under non-Western ethnic minorities has strongly risen, which could also indicate that the willingness to become an entrepreneur is growing, for instance based on an increasing status.

Parental education The education level of the parents has an effect on the education level of their children. Higher educated parents tend to have higher educated children. And as discussed above, there is a positive effect of education on entrepreneurial entry and success. Van Praag (2005, p. 160) concludes that having a highly educated father (and sometimes mother) has a significant positive effect on becoming an entrepreneur. The effect on entrepreneurial success is however totally mixed. Self-employed parents Entrepreneurs are more likely to come from families in which the parents are entrepreneurs. The conventional wisdom that “breeding entrepreneurs starts at home” is confirmed by the results from Grilo and Thurik (2005, p. 13). Having self-employed parents increases the odds of all engagement levels, potentially leading to an effective entrepreneurial activity relative to not considering such activities and it makes giving up on starting a business less likely. The probability of self-employment is two or three times higher among the children of business owners than among the children of non-business owners (Lentz and Laband, 1990; Dunn and Holtz-Eakin, 2000; Hout and Rosen, 2000). Children growing up in such families see their parents as role models. These persons often develop valuable knowledge of running a business at a young age (Cooper et al., 1994, p.377). Or as Parker (2004, p. 85) puts it, self-employed parents might offer their offspring informal education in business methods,

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transfer business experience and provide access to capital and equipment, business networks, consultancy and reputation.

Van Praag (2005, p. 160) concludes in a review of studies that self-employed fathers (and sometimes mothers) are positively related to the likelihood of becoming an entrepreneur, but not to the likelihood of being successful as an entrepreneur.

2.1.2.3 Social capital

Social capital theory refers to the ability of individuals to extract certain benefits from their social structures, networks and memberships (Lin et al., 1981; Portes, 1998). Family- or community-based social networks supplement the effects of education, experience and financial capital (Bourdieu, 1983; Loury, 1987). Emerson (1972) refers to social capital in terms of social exchange of resources. Exchanges may range from providing resources, such as loans, to intangible resources, such as information about the location of a new potential market or client. These resources may be provided by direct or indirect ties. We are mainly interested in factors or ties related to social relations.

In Granovetter’s (1973) seminal work, the importance of maintaining an extended interpersonal network of ties in obtaining the most fruitful resources is highlighted. Weak ties are loose relationships between individuals, as opposed to strong ties which could be found within a tight family. Granovetter stresses the cohesive power of weak ties, in order to obtain information that would otherwise be unavailable or costly to locate. Weak ties extend one’s network by linking individuals together, thereby providing the link for exchanges to take place.

From an entrepreneurial perspective, social capital can provide networks that facilitate the discovery of opportunities, as well as the identification and collection of resources (Birley, 1985; Greene and Brown, 1997; Uzzi, 1999). Davidsson and Honig (2003, p. 320) state that social capital is even more influential in determining the probability of nascent entrepreneurship than human capital variables. They also suggest that social capital may help the entrepreneur succeed, by providing critical information or other important resources (p. 309). In order to test social capital aspects in our research, we use a number of variables that capture the weak and strong ties dimensions.8

8

We excluded parental entrepreneurship from this section, as its influence probably is not restricted to social capital only.

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Entrepreneurial environment The first measure we use for social capital is having an entrepreneurial environment. Having close friends or relatives in your environment who are entrepreneurs is associated positively with the odds of being a nascent entrepreneur and being successful as such (Davidsson and Honig, 2003).

Another social capital aspect of positive influence is having a social network (Davidsson and Honig, 2003; Uzzi, 1999). Our sample consists exclusively of students who mostly lack business networks but may build up networks through extracurricular activities in, for instance, student associations or sororities or by being a student assistant, doing an internship or having an administrative function within an association or sorority.

Social environment The population density of an area in which a person lives has a positive relationship with one’s willingness to become an entrepreneur (Van Praag, 2005, p.49). Therefore, it might be of influence how densely populated the area is in which people have (mainly) grown up.9 In a densely populated area, interacting in a social environment and learning to develop social skills and maybe a somewhat more stressful environment of a city might prepare an individual better for entrepreneurship.

2.1.2.4 Attitudes

Brockhaus (1982) identifies three dimensions determining entrepreneurial orientation: risk-taking propensities, internal locus of control beliefs, and need for achievement. The same three dimensions were identified by Driessen and De Zwart (1999) as characteristics of successful entrepreneurs. Another dimension that has received much attention and has been found to be related to entrepreneurship intentions and behaviour is self esteem or self-efficacy (Boyd and Vozikis, 2004).

Risk attitude Various studies have shown that entrepreneurship is riskier than wage employment, in terms if incomes (De Wit, 1993; Caroll et al., 2001), due to demand uncertainty (Applebaum and Katz, 1986) or cost uncertainty (Kanbur, 1979). Risk averse individuals would therefore be more willing to work as an employee than as an entrepreneur (Ekelund et al, 2005, p.650; Nandram and Samsom, 2000; Cramer et al., 200x). The same holds for people who fear failure (Minniti and Bygrave, 1999; Wagner,

9

All respondents are students in a central city. Therefore, the population density of the current living area is uninformative, although previous empirical evidence relates to that.

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2005). In general, the evidence indicates that risk attitude influences the willingness and likelihood of entrepreneurship, but not the performance.

There are three methods to measure risk according to Ekelund et al. (2005). First, people can be challenged by a situation that simulates risk and can be observed, for instance, while playing a gambling game. Second, risk taking behaviour, such as the insurance behaviour of people can be observed. This kind of data however is very hard to obtain. And third, data from psychological tests, based on survey questions that measure risk aversion in a broader fashion can be used. We use two measures of risk aversion of the latter kind.

Locus of control was first introduced by Rotter (1966) in his social learning theory and is defined as an individuals’ general expectancy of the outcome of an event as being either within or beyond her or his personal control and understanding.10 Individuals with an internal locus of control believe that they have control over their own destiny and hence believe that events are contingent upon their own actions. Individuals with an external locus of control believe that their behaviour is guided by chance, luck, or other external circumstances, and perceive events as beyond their control.

In the psychological literature, locus of control is considered to be an important and stable aspect of personality with clear behavioural consequences, and is generally seen as desirable (Boone and De Brabander, 1993; Mamlin, Harris and Case, 2001; Beugelsdijk and Noorderhaven, 2005; Boone, Van Olffen and Van Witteloostuijn, 2005).

Brockhaus and Horwitz (1986) specified locus of control as a useful measure for identifying potential successful entrepreneurs. Ahmed (1985) shows that an internal locus of control has a positive effect on the start-up of a business. Rauch and Frese (2000) show that small business owners have more internal locus of control beliefs, based on a meta-analysis of psychological studies analyzing whether small business owners have a higher internal locus of control than other populations. They also found a clear, although small, relationship between an internal locus of control and success. Brockhaus (1980) and Hornaday and Bunker (1970) show that business survival is positively related to an internal locus. Thus, both business start-up and success are related positively to internal locus of control beliefs.

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Locus of control beliefs can be measured in various ways. Grilo and Thurik (2005) have measured it based on the following question and answer categories: “When one runs a business, what do you think most determine its success (max two answers)? (a) The director’s personality; (b) The general management of the business; (c) The overall economy; (d) The political context; (e) Outside entities.” Locus of control is internal if a or b are mentioned and external if c, d or e are mentioned. The variable turns out insignificant in their analysis of entrepreneurship outcomes.

A second measure of locus of control beliefs is based on a simplified Rotter (1966) test and was used by Van der Sluis et al. (2005), Van Praag and Van Ophem (1995); and Evans and Leighton (1989). The results are mixed, both with respect to the likelihood and the success of entrepreneurship.

Need for achievement (nAch) is, according to Brockhaus (1982), one of the main dimensions determining entrepreneurial orientation and was first formulated in the 1950s (McClelland et al., 1958). McClelland and his colleagues argued that high-nAch people are more likely than low-nAch people to engage in energetic and innovative activities that require planning for the future and entail an individual’s responsibility for task outcomes. He also argued that entrepreneurial positions have more of these characteristics than other types of positions.

Collins et al. (2004) found supportive evidence for the hypothesis that people who pursue entrepreneurial careers have a higher need for achievement than others in a meta-analysis. This is in line with Green et al. (1996) and Cooper and Gimeno-Gascon’s (1992). The Ray-Lynn AO scale (Ray, 1979) is a validated measure of nAch.

Self-efficacy is derived from social learning theory (Bandura, 1977a, 1977b, 1982) and plays an important role in the development of entrepreneurial intentions and actions. Self-efficacy is defined as a person’s belief in his or her capability to perform a task (Gist, 1987) and is clearly distinct from locus of control, as it is more task- and situation-

specific.

In a model of entrepreneurial intentionality Bird (1988) argues that it may be possible to distinguish entrepreneurs from potential entrepreneurs on the basis of how concepts as career, work, risk and rewards align with venture concept. In an attempt to further develop and strengthen Bird’s (1988) model, Boyd and Vozikis (1994) argue that

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self-efficacy is the underlying construct influencing the complex process of new venture creation. A person will only initiate entrepreneurial actions when self-efficacy is high in relation to the perceived requirements of a specific opportunity.

2.2 Occupational Status

Occupational research traditionally has two lines of interest (Halaby, 1993). The first is represented by stratification studies of status attainment, which is discussed in Section 2.2.1. The final two sections of this chapter discuss the second line: individual beliefs about the properties that make a job desirable.

2.2.1 Measurement

The relative status11 of occupations is a field of research that goes back a long way. There are traditionally two ways of measuring status. The occupational stratification as we know it today started with the occupational prestige study by North and Hatt (1947) in the late 1940s. Their study, performed at the National Opinion Research Centre and better known as the NORC study, analyzed public attitudes regarding the prestige of 90 selected occupations. A variety of people were asked to rank these occupations according to their perceived status. The 1989 NORC general social survey includes an evaluation of the status of occupations (Hodge, Siegel and Rossi, 1964). Respondents evaluate occupations according to their social standing and rank them on a nine-point scale. We call this subjective status measurement.

This original NORC study was broadened by Duncan (1961). He developed the so called socioeconomic index (SEI) score as follows. By linking the prestige scores from the NORC study to the income and education information in the census, he produced a formula to calculate and predict prestige (Nakao and Treas, 1994). This was solely based on education and income, thereby ignoring the subjective aspect of ranking occupations (Hodge, 1981). Siegel (1971) constructed these scores for all occupations, leading to the 1989 Total based SEI index. This second measure is objective and implies a uniform

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status for every occupation. Treiman (1977) merged survey data from societies to create a uniform International Prestige Scale. He compared the data of many different cultures and societies and concluded that prestige rank ordering among major occupational groupings is generally similar across cultures and societies. His study supports the ’structuralists’ view regarding the international uniformity of the occupational stratification. As opposed to ‘culturalists’, who argue that there is a difference between people, cultures and countries.12 13

We used the method of the original (1989) NORC study. Thus, respondents simply state their perceived status of the entrepreneur and of 19 other occupations.

2.2.2 Definition and interpretation

Max Weber (1864-1920) was the first to introduce status as a term, as part of his three component theory of stratification (social class, social status and religion). He defined status as “An effective claim for social esteem”. He defined occupations as status groups, i.e. a group of persons who successfully claimed a specific social esteem within a larger group. He argued that occupational status depends, above all, on the amount of training required and the opportunities for earnings (Weber, 1978, p.144). This objective definition (in line with the structuralists’ view) mainly focuses on status aspects of the job (earnings, education). Various other occupational characteristics may determine the status of occupations. Individual factors however would play no role here; the status of occupations is known and set (Balkwell et al., 1982). According to Bisbin and Verdier (1998), individuals are supposed to have two preference components. These consist of a private (job status) and a social utility component (e.g. a taste for social recognition or social status).

In line with the above, Kwon says that one of the most basic social phenomena is that people compare to each other (2006). Status derived from their ‘relative standing’ (with respect to wage, authority, or beauty) with their reference groups (such as, co-workers, neighbours, or friends). The outcome of this comparison process leads to frustration or

12_{The first to present this hypothesis were Inkeles and Rossi (1956).} 13

An alternative method to measure occupational status has been developed in Great Britain by Goldthorpe and Hope (1974). However, the method based on the NORC studies in the United States in the 1960s has remained the primary standard for evaluating occupational status (Nakao and Treas, 1994).

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satisfaction, which in turn has behavioural implications. An example of this is that social origin (father’s occupation) affects social destination (own occupational choice), and thereby also the status of that occupation (Hendrickx et al., 1998; Katz, 1992).

Abbot (1981) argues that assigning a uniform status to a group or profession is difficult; (intra-)professional status is based on very different issues than public status. Duncan (1968) and Haller (1970) have also argued that status is multidimensional and cannot always be reduced to a single dimension.

In our study, status is measured subjectively and is defined by our respondents. Potential determinants of status are discussed below.

2.2.3 Determinants of status

Brown (1955) identifies eleven possible occupation-related determinants of occupational status, based on North-Hatt (1947): i) necessity to the public welfare, ii) respect, iii) cleanness of the job, iv) education or training needed, v) talent or skills needed, vi) income, vii) leisure time/vacations, viii) personal reverences (“Do you know people that perform the occupation, and is that a positive association?”), ix) rich history, x) hard work needed and xi) the social or altruistic level of the job. Villemez (1974) finds also that xii) power has a strong association with status.

Subsequent to the ranking of the professions, respondents could state what aspects determined the status they had given to the occupations. Options were given based on Brown (1955) (accept iii, viii and xi) and Villemez (1974). We will test empirically which determinants co-determine the perceived status of the occupation ‘entrepreneur’.

2.3 Economic Status

Work in economics has provided research on status as well and has formalized status as a component of the worker’s utility function. According to Frank (1984) a person’s status among his peers is no less important than his absolute income level in determining his sense of well being. Frank (1988) furthermore argued that striving for status leads to strong motivational effects if status depends on the relative productivity of a worker.

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Ederer and Patacconi (2004) also state that a worker’s utility depends on a reference of typical wage, and not only on wage and effort. Differences relative to the reference wage can be interpreted as a measure of the magnitude of the worker’s status concerns relative to the chosen reference group. The reference group is derived from objective elements, such as the (tournament) structure of the game, as well as subjective ones, for example ambition.

Kwon and Milgrom (2006) show that identification of the reference group is critical. Workers care about the firm’s group status against other firms, rather than their individual status (or relative wage) within the firm. In this context, a worker may prefer a lower relative wage in order to work with relatively higher-quality co-workers (with higher wages) who can raise the group status.14

Status might also be valued by itself (e.g. Frank, 1985; Zizzo, 2002). Huberman, Loch and Önçüler (2004) provide experimental evidence that individuals value status independently of monetary measure and are willing to trade off some material gain in order to obtain it.

2.4 Entrepreneurship and status

Krueger and Carsrud (1993) argued that entrepreneurial behavior is intentional and thus best predicted by intentions towards behavior, not by attitudes, beliefs, personality, or demographics. Intentions are assumed to capture the motivational factors that influence (planned) behavior. Kolvereid (1996) investigated what determines entrepreneurial intentions and found that the more favorable the attitude and subjective norm with respect to entrepreneurship, the stronger the individual’s intention to become self-employed. Hence, a positive association between the perceived status of entrepreneurship and the likelihood of choosing this occupational status can be expected.

Indeed, a (tentative) positive link has been found empirically between the social status of entrepreneurs and entrepreneurial activity (Malach-Pines et al., 2005). They show in a cross-cultural study amongst MBA students in Israel, the United States and Hungary that the view of high-tech entrepreneurs as cultural heroes, i.e. they have a high

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social status, will go together with higher levels of entrepreneurial activity within all three countries. Students in the United States and Israel rated high-tech entrepreneurs as having higher social status relative to other professions than Hungarian students, and this difference was also reflected in the countries entrepreneurial activity index, thereby linking the micro with the macro level.

Our research differsfromthat of Malach-Pines et al. in various ways. First, we do not specify the (kind of) entrepreneur, such that respondents were free to think of every kind of entrepreneur, ranging from a local store-owner to a Bill Gates.15 Second, we directly look at the effect of the status given by the respondent and its effect on the respondents’ willingness and likelihood of becoming an entrepreneur.16 Third, we analyze the determinants of entrepreneurial status, which we have divided in human capital, background, social capital, and attitude. This is relevant for finding instruments to stimulating entrepreneurship, because status and entrepreneurial activity are positively related and perhaps not completely determined by the same set of factors. In the next chapter we discuss our methods of analysis in a more detailed fashion.

15_{Also the ‘high-tech’ specification might induce individuals to rate the entrepreneur as having higher}

social status than a ‘normal’ entrepreneur, purely based on the choice of words.

16

Malach-Pines et al. had students rate professions and linked that to the countries entrepreneurial activity indices.

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3 Methodology

3.1 Sample

Our quantitative research is based on a sample of students in the Netherlands. Questionnaires were distributed among students in libraries, during exams, through email, through websites. We gathered 1134 questionnaires, of which 818 were complete. Participation was on a voluntary basis and no (financial) incentives were given. Students in the Economics or Business department are overrepresented and make up 62 percent of the sample.17 These students might have a more positive ‘business’ attitude or focus relative to other students. More descriptive statistics are provided in Chapter 4.

3.2 Questionnaire

A questionnaire was developed for the purpose of this study including survey questions of a subjective nature. Bertrand and Mullainathan (2001) discuss some of the problems with the use of subjective survey data. First, respondents may suffer from cognitive problems, i.e. the ordering of questions, the exact phrasing and the precise measurement scales can have substantial effects on the responses. Furthermore, respondents may tailor their answers to what they think is socially desirable. Finally, it is sometimes unclear whether the attitudes researchers are trying to measure really exist in a coherent form.18

The questionnaire consists of 37 questions (see Appendix C3), categorized into four groups, i.e., human capital, social capital, (family) background and attitude. The core question, question 19, the ranking question, is asked to establish the ranking of the occupation ‘entrepreneur’ within a selection of 20 occupations.19 The order in which the occupations were listed is random. Each occupation was graded by each respondent on a

17_{See Table A4.1a, Appendix C4}

18_{Bertrand and Mullainathan (2001) discuss how these problems may bias the empirical analysis from a}

measurement-error perspective. When the subjective variables are explanatory variables (as in our case), some of the problems mentioned above translate into noise.

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scale from 1 to 10. These twenty grades were ranked from 1 to 20 in which the occupation with the highest grade received the highest ranking.

Question 20 establishes the occupation related determinants of occupational status, questions 36 and 37 establish the determinants of (un)willingness to become an entrepreneur.20

We have set up the questionnaire with extreme caution in order to suppress the cognitive problems discussed above as much as possible. For example, the questions concerning entrepreneurship were asked after the questions concerning the status of the various professions.

3.3 Dependent variables

Three variables are considered endogenous and used as dependent variables in the regression analyses. Their distributions are shown in Table 3.1, together with the valid number of observations for each of them. The first is the perceived status of the entrepreneur. We measure status as defined in the previous section (and estimate this by means of OLS) along with three permutations. The second measure of status (the first permutation) positions the status rank in the average of the percentile in the sample distribution of the rank. Thus, each rank receives a slot of 5 percent, ranging from 0.025 to 0.975. This status measure is estimated by means of OLS. The third measure of status used as a dependent variable is a dummy variable that takes on the value one if an individual ranks entrepreneur at the highest status position and zero otherwise. This measure is estimated in a binary logistic regression. The fourth and final measure of status we estimate (also in a binary logit regression) is a dummy variable taking on the value one for individuals who rank the entrepreneur in the status top three and zero otherwise.

The second dependent variable measures the willingness of individuals to become an entrepreneur. It is a dummy variable taking on value one if the respondent answers

20

Becoming an entrepreneur is only possible when someone is willing and has the opportunity to become one (Van Praag, 2005). We cannot examine the opportunity of our respondents, as we lack the information for this. So determinants of opportunity are not included in this research.

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‘entrepreneur’ to the question “If you could choose, would you rather be an entrepreneur or an employee?” and zero if they answer ‘employee’, based on question 34.

The third dependent variable measures the likelihood of becoming an entrepreneur. It is the answer, on a 10-points scale, to question 35: “What is the likelihood that you will become an entrepreneur within the next ten years?” This likelihood variable is estimated by means of OLS.

All three types of dependent variables are related to the explanatory variables, including a set of control variables. All equations are estimated both including and excluding the other endogenous variables (see Chapter 5).

Table 3.1 Distribution dependant variables

Status freq. Willingness freq. Likelihood freq.

1 5 Willing 498 1 104 2 3 Unwilling 320 2 97 3 11 3 119 4 28 4 83 5 73 5 79 6 169 6 94 7 219 7 99 8 190 8 75 9 89 9 30 10 31 10 38 Total 818

3.4 Explanatory variables

We are particularly interested in the similarity and differences of the determinants of the perceived status of entrepreneurship and the factors that determine (i) the likelihood to become an entrepreneur –where likelihood is the product of willingness and opportunity-, and (ii) the performance of entrepreneurs. Hence, the questionnaire includes the most important potential determinants of this likelihood and performance that have been derived from the entrepreneurship literature. We further assess to what extent one’s willingness and likelihood to become an entrepreneur have a direct effect on perceived

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entrepreneurial status and vice versa. Thus, we use entrepreneurial status both as a dependent and an independent variable.

3.4.1 Determinants of (successful) entrepreneurship

To answer the research questions, we focus on the literature on (successful) entrepreneurship and distill potential determinants of becoming an (successful) entrepreneur (see Section 2.1.2). The determinants of entrepreneurial success are policy relevant, because social costs are attached to unsuccessful entrepreneurship. We categorize potential determinants into human capital variables, social capital variables, background variables and attitude variables. We lack information on financial capital. 3.4.1.1 Human capital

Because of the strong positive relationship between schooling and entrepreneur performance (but not entry), it is interesting to see whether the education of subjects is positively related to the status (willingness and likelihood) of entrepreneurship. We do this in two ways. First, we gathered information about the progress of the education of the students in our sample, i.e. whether they are first year, bachelor or master students. Because the return to education is positive we expect a positive effect of the amount of schooling on the status.

Second, the level of education students are following, i.e. university (WO), school of professional higher education (HBO), or school of professional intermediate education (MBO) or otherwise, is used to test the relationship between the level of formal education and entrepreneurship status. We expect to find:

Hypothesis 1: The number of years and level of schooling have a positive association

with the perceived status of entrepreneurs. The willingness and likelihood of becoming an entrepreneur are not associated with the number of years and level of education.21

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The subject field of education might also relate to our dependent variables. One might suspect that it is more common to become an entrepreneur in certain fields, like Economics and Business. We distinguish eight subject fields of education: Economics, Social sciences, Health, Nature, Law, Technique, Religion & Philosophy, and Language, Culture & Art.22 Since theory cannot guide us here, we do not formulate any hypothesis. Theory does predict, however that jacks-of-all-trades are more likely to become a(n) (successful) entrepreneur. Our questionnaire includes a JAT measure: Respondents rate their own various abilities (similar to Hartog et al., 2007) on a 1-10 scale and we measure the variation of these five abilities as an inverse measure of JAT. Based on previous findings, we hypothesize:

Hypothesis 2: Being a Jack-of-all-trades has a positive effect on the status of

entrepreneurs, the willingness and the likelihood of becoming one.

In general, empirical evidence indicates that the likelihood and success of entrepreneurship is related to previous entrepreneurship experience (Van Praag, 2005, p. 48; Davidsson and Honig, 2003). Respondents have indicated whether they are currently or have been an entrepreneur. We expect to find a positive relationship between the perceived status and (previous) experience, but the relationship between willingness to become an entrepreneur and (previous) experience is not so straightforward. The experience may have evoked enthusiasm about entrepreneurship, or, on the other hand, if the entrepreneurship experience was negative, it might have decreased their motivation:

Hypothesis 3: Being or have been an entrepreneur has a positive effect on the status of

entrepreneurs, the willingness and the likelihood of becoming one.

Likewise, since a positive relationship between successful entrepreneurship and labor market experience in general is expected, we hypothesize that:

22

A complete list in which all studies are subdivided in these eight fields can be found in Table A4.1g in Appendix C4.

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Hypothesis 4: The number of different previous jobs held is related positively to the

perceived status of entrepreneurs, the willingness and the likelihood of becoming one.

Although there is only scant empirical evidence on the relationship of creativity with the willingness to become an entrepreneur and its effect on entrepreneurial outcomes, we hypothesize that creativity is a positive attribute for an entrepreneur. Respondents rated their own levels of creativity on a 10-point scale:

Hypothesis 5: Creativity and the perceived status of entrepreneurs, the willingness and

the likelihood of becoming one are positively related.

Based on theory and empirical evidence, we expect that people who perceive their own innovation level as high, will also give higher status to entrepreneurs, because entrepreneurs are more innovative. Hence, their willingness to become an entrepreneur will also be higher. Respondents rated their own level of innovativeness on a 10-point scale:

Hypothesis 6: Innovativeness and the perceived status of entrepreneurs, the willingness

and the likelihood of becoming one are positively related.

3.4.2.2 Background variables

We use gender as a control variable in our study, and use a 0-1 dummy to test what the effect of being female is on the perceived status of entrepreneurship. As research shows that women are less likely to become entrepreneurs, have a lower probability of preferring to be an entrepreneur and have a lower entrepreneurial performance, we expect to find that women give a lower status to entrepreneurs than men, and that women are less willing to become an entrepreneur.

Hypothesis 7: Being female has a negative effect on the status of entrepreneurs, the

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The second background variable is the age of the respondents. We want to test whether the perceived status of entrepreneurs is dependent of age and if willingness to become an entrepreneur is decreasing or increasing in age. As written above the evidence on the relationship of age and the willingness and opportunity to become an entrepreneur is diverse and so it is hard to make clear predictions. As almost all of our respondents are younger than 23, we presume, based on Van Praag (2005) that the willingness to become an entrepreneur increases in age. A similar pattern is expected for the status of entrepreneurs, despite the fact that 24 year olds have the worst opportunity.

Hypothesis 8: Age has a positive effect on the status of entrepreneurs, the willingness and

the likelihood of becoming one.

Regarding cultural background, being part of an ethnic or cultural minority in the Netherlands could affect the likelihood and willingness to become an entrepreneur positively, see Section 2.1.2.2. The relationship between belonging to a minority group and the perceived status of entrepreneurship is not clear from the literature and remains an empirical matter. The survey is informative about respondents’ country of origin, as well as their fathers’ and mothers’. The following countries are distinguished explicitly in the questionnaire: The Netherlands, Morocco, Turkey, and the set of Asian countries.23 However, due to too little observations we simply distinguish Dutch and not-Dutch.

Because of the strong increase in the entrepreneurial activity and the rising entrepreneurship quote of minorities, we expect to find a positive relationship between not having a Dutch background and the likelihood and willingness to become an entrepreneur:

Hypothesis 9: Not having a Dutch background has a positive relationship with the

willingness and the likelihood of becoming an entrepreneur. The relationship with perceived entrepreneurial status is ambiguous.

23

Morocco and Turkey are important non-Western minorities in the Netherlands, of which the economic participation rate is low in general (Monitor of Ethnical Entrepreneurship, 2004, p. 12). Asia as a country of origin has the highest entrepreneurship rate (Fairly, 2005).

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The parental education level is also found to have effect on the choice of becoming an entrepreneur, as Van Praag (2005) shows. The effect on the status of entrepreneurship is not found in the literature. In our survey, we have asked the respondents to state the education of their father and mother, for which they could choose from WO & HBO, MBO, VWO & HBS, HAVO, or MAVO & MULO. Based on the results of Van Praag we hypothesize:

Hypothesis 10: Having relatively high educated parents has a positive effect on the

perceived status of entrepreneurs, the willingness and the likelihood of becoming one.

Another relevant background characteristic is the occupational status of the parents, i.e. whether they were entrepreneurs. This increases the likelihood (and willingness) of entrepreneurship, but not performance. As the probability of becoming an entrepreneur and the entrepreneurial outcomes increase for individuals coming from an entrepreneurial nest, we expect that the perceived status of entrepreneurs is also higher under those individuals:

Hypothesis 11: Having entrepreneurial parents is associated positively with the perceived

status of entrepreneurs, the willingness and the likelihood of becoming one.

3.4.2.3 Social capital

From an entrepreneurial perspective, social capital can provide networks that facilitate the discovery of opportunities, as well as the identification and collection of resources (Birley, 1985; Greene and Brown, 1997; Uzzi, 1999; Davidsson and Honig, 2003).

To test social capital aspects in our research, we use a number of variables that capture the weak and strong ties dimensions. The first measure of social capital is having an entrepreneurial environment. Based on previous studies, discussed in Section 2.1.2.3, we hypothesize:

Hypothesis 12: Having a relatively strong entrepreneurial environment has a positive

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The second measure of social capital relates to social networks that can be obtained through extracurricular activities. Extracurricular activities considered are memberships of a student association or sorority, having spent considerable time on administrative duties within a student association or sorority, being a student assistant, and internships. There has been no empirical research on this matter for students. However, based on results related to business networks and weak ties in general (see Section 2.1.2), we expect:

Hypothesis 13: Having experience in an extracurricular activity has a positive association

with the perceived status of entrepreneurs, the willingness and the likelihood of becoming one.

The social environment is further measured by the population density of an area in which a person has grown up:

Hypothesis 14: Growing up in a city is related positively to the perceived status of

entrepreneurs, the willingness and the likelihood of becoming one. 3.4.2.4 Attitudes

We measure risk attitude based on survey questions in two manners. The first measure of risk aversion is the reservation price for one of ten tickets in a hypothetical lottery with a single prize of €1000. The reservation price p reflects the individual attitude towards risk (see Cramer et al., 2001). A drawback of this measure is that it reflects the attitude towards upside risk only. We also calculate some permutations of this measure of risk attitude based on Cramer et al. (2001).

Our second measure of risk aversion is based on Dohmen et al. (2005) and is the answer to the question: “Are you generally a person who is fully prepared to take risks or do you try to avoid taking risks? Please tick a box on the scale, where the value 0 means: ‘unwilling to take risks’ and the value 10 means ‘fully prepared to take risk.”(p.7). According to Dohmen et al. this general risk question is the best all-around predictor of risk taking behaviour in different contexts, and outperforms a lottery question or domain-specific measures. We convert this measure into a dummy variable where scores of 7 till

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10 were considered as risk prepared, scores of 0 till 4 as risk averse and the score of 5 and 6 as risk neutral.24 Based on previous research findings, we hypothesize that:

Hypothesis 15: Risk aversion has a negative relationship with the perceived status of

entrepreneurship, the willingness and the likelihood of becoming one.

Entrepreneurship is associated with more internal locus of control beliefs (see Section 2.1.2.4). We hypothesize:

Hypothesis 16: Having a relatively internal locus of control belief is associated with a

higher perceived status of entrepreneurship and increased levels of willingness and likelihood of becoming one.

Our first measure of locus of control beliefs is similar to Grilo and Thurik (2005), see Section 2.1.2.4). The survey contains exactly the same question. However, the dummy variable construct is slightly different. If a and/or b are mentioned, locus of control is defined to be internal, whereas c, d or e are associated with external locus of control beliefs. If a combination of these sets is selected locus is defined to be neutral, our reference category.

A second measure of locus of control beliefs is a simplified Rotter (1966) test. Respondents have stated on a five-point Likert scale whether they (dis)agree with a series of propositions derived from Pettijohn (1999). The scores obtained for six propositions (see question 28 of questionnaire, Appendix C3) were added. High scores (20-30 points) are considered as internal locus of control, low scores (6-16) are considered as external locus of control. Any score in between is considered neutral.

Need for achievement is one of the main determinants of entrepreneurial orientation and outcomes. This is supported by empirical evidence (see Section 2.1.2.4). We expect:

24

Another measure of risk attitude can be formed by the reason indicated why one would not become an entrepreneur (question 37). One of the possible answers was: “too much risk”.

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Hypothesis 17: Having a relatively strong need for achievement has a positive effect on

the status of entrepreneurs, the willingness and the likelihood of becoming one.

Our measure of nAch is based on the validated Ray-Lynn AO scale (Ray, 1979). We altered the three-point scale they used into a five-point scale to make it comparable to the scales related to locus of control (see question 28 of questionnaire, Appendix C3). High scores (20-30 points) are considered to be high need for achievement, low scores (6-16) are considered as low need for achievement. Any score in between is considered neutral.

Self-efficacy and self-esteem measures are based on the self-assessed expectancy of finding a job after graduation (see Oosterbeek and Van den Broek, 2006, p. 21).25 We also consider the simple average of the five self-assessed levels of ability as a measure of self-efficacy. Based on Boyd and Vozikis (1994) who argue that self-efficacy positively influences the development of both entrepreneurial intentions and actions or behaviour, we hypothesize that:

Hypothesis 18: Self-efficacy is related positively to the perceived status of

entrepreneurship and the willingness and the likelihood of becoming one.

3.4.3 Endogenous variables used as determinants

We are not only interested in what determines entrepreneurial status, willingness and the likelihood, but also in the interrelations between the endogenous variables, i.e. whether the perceived entrepreneurial status affects the willingness to become an entrepreneur and the likelihood of becoming an entrepreneur. We therefore use the dependent variables also as explanatory variables in our regression models.

Based on weak and scarce evidence (Malach-Pines et al., 2005) we expect:

25

However, we think that self-assessed labor market opportunities are also related in other ways than through self-esteem to the likelihood of becoming an entrepreneur and can be either positive or negative.

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Hypothesis 19: The relationships between perceived status, willingness and likelihood of

becoming an entrepreneur are positive.

The measures of these endogenous variables are described in Section 4.3.

3.4.4 Determinants Summary

An overview of all determinants is presented in Table 5.4 in Chapter 5. It states the full name of the determinant, the name of the determinant as it can be found in the tables containing the regression results (also in Chapter 5) and the hypothesis with the direction of the expected effect on the dependent variables.

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4. Descriptive statistics

4.1 Basic Descriptives

All the 818 participants are students in the Netherlands mostly between the ages of 18 and 26. 92.7 percent of the participants are Dutch. 7.3 percent of our dataset is of another nationality.26 Most of the respondents are WO students (81.4 percent), and primarily Economics students (62.0 percent).27 Of the respondents the majority (49.5 percent) are Bachelor phase students. 27 percent are in their first year of education (Propedeuse), and the rest (23.5 percent) are Master phase students. Tables A4.1a to A4.1e in Appendix C4 provide descriptive statistics for most of the determinants. In these tables we review the average status for every determinant category, the distribution between the different categories of a determinant, the willingness and the likelihood.

4.2 Status definition

Status is a very subjective concept and as Table 4.1 shows our respondents also state that status is not just associated with one aspect. Required education to perform an occupation was mentioned most, being the most important aspect of status. This is especially the case for the higher educated.28 Respect too enters the definition for more than 62 percent of the respondents. High income, social importance, talent, power and hard work also are almost of similar importance in aspects of status.29 No differences were found in the definition of status between the total population and the subjects that are willing to become an entrepreneur.

We have also used the aspects in the statistic regressions, in order to see whether an individual aspect has a significant effect on the status.

26

We initially subdivided the other nationalities. See Table A4.1f in Appendix C4 for the original subdivision of the nationalities.

27_{See Table A4.1g in Appendix C4 for an overview of the mentioned education fields.} 28_{See last column of Table 4.1}

29

Other aspects that were mentioned besides the options that we suggested based on the literature were; responsibility, risk and difficulty of the occupation. Several respondents stated that everybody has the same status and that occupations do not determine someone’s status.

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Table 4.1 Definition of status

Status Total % Willing % Education

required

Education required 622 76,0% 372 74,7% WO 77,3%

Respect 513 62,7% 332 66,7% HBO 73,7%

High income 398 48,7% 262 52,6% MBO 47,4%

Social importance 387 47,3% 221 44,4% Talent 341 41,7% 202 40,6% Power 263 32,2% 175 35,1% Hard work 263 32,2% 155 31,1% Rich history 121 14,8% 68 13,7% Spare time 8 1,0% 6 1,2% Other 25 3,1% 11 2,2% 4.3 Dependant variables

Table 4.2 shows that of the population 498 are willing to become an entrepreneur and the average likelihood is 4.75. They gave an average status of 6.97 to entrepreneurs, placing the entrepreneur on average on the 8th place (Table 4.3).

Table 4.2 Descriptive statistics Total Population

Total Status

(Avg.) Rank Willing Willing %

Status Willing (Avg.) Likelihood (Avg.) Likelihood Willing (Avg.) 818 6,97 7,49 498 60,9% 7,16 4,75 5,95

The rank that was given most to the entrepreneur is the first. Of the participants, 100 (12.2 percent) ranked the entrepreneur first giving an average status of 8.93. 179 participants (21.9 percent) thought entrepreneurs are in the top 3 giving an average status of 8.59.30 Willing student rate entrepreneurs higher (7.16 versus 6.97). Willingness and likelihood are also dependant of each other as the willing students are more likely to become entrepreneurs (5.95 versus 4.75).

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Based on the literature on occupational status we gathered two rankings of the occupations that we have chosen for reference.31 As shown in Table 4.3 you can clearly see a big difference in the positions that the entrepreneur takes in our study in comparison to the rankings from the theory.

Table 4.3 Occupational Status and reference rankings Rank Occupation Average

Status (s.d.) var 1989 NORC 1989 Total based SEI index

1 High Court judge 8,7 1,36 1,85 Physiscian (86) Physiscian (97)

2 Physiscian 8,5 1,25 1,57 Lawyer (75) University Professor (94)

3 University Professor 8,3 1,47 2,17 University Professor (74) Lawyer (92)

4 Lawyer 7,9 1,34 1,79 Architect (73) Actuary (90)

5 Mayor 7,7 1,68 2,83 Engineer (71) Engineer (88)

6 Engineer 7,6 1,51 2,28 High Court judge (71) High Court judge (87)

7 Architect 7,4 1,39 1,93 Mayor(70) Architect (84)

8 Entrepreneur 7,0 1,55 2,41 Teacher (High School) (66) Management Consultant (83) 9 Accountant 6,9 1,55 2,4 Accountant (65) Teacher (High School) (80) 10 Marketing manager 6,7 1,53 2,35 Management Consultant (61) Accountant (76)

11 Management Consultant 6,7 1,51 2,27 Computer programmer (61) Computer programmer (76)

12 Actuary 6,1 1,64 2,69 Journalist (60) Journalist (75)

13 Journalist 6,1 1,57 2,48 Policeman (60) Marketing manager (73) 14 Real estate agent 5,9 1,67 2,79 Marketing manager (59) Entrepreneur (64) 15 Teacher (High school) 5,6 1,6 2,54 Entrepreneur (51) Real estate agent (64) 16 Computer programmer 5,5 1,63 2,66 Electrician (51) Policeman (63) 17 Policeman 5,3 1,84 3,37 Real estate agent (49) Mayor (59)

18 Electrician 4,4 1,7 2,98 Mailman (47) Mailman (54)

19 Barber 3,8 1,66 2,76 Actuary (44) Electrician (45)

20 Mailman 3,7 1,69 2,84 Barber (36) Barber (30)

We must conclude, if possible, that the rankings based on both the NORC and SEI index are not representative for our study. We so far are of the opinion that occupational status is not universal and will probably divert between countries and/or societies. This is in contrast with the theory of the ‘structuralists’ that status is universal.

4.4 Descriptive findings on the determinants

Men give a slightly higher status to entrepreneurs and are more willing then women. Status is highest among 26 year olds, and the lowest among 28 year olds. Status seems to be independent of age. When people are young the willingness is high but this willingness declines when someone ages. Status is increasing in education level, but the

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willingness seems to be decreasing. Status seems independent of the education phase and the willingness is even decreasing. The education level of the parents has no effect on status but an increasing effect on the willingness. On the other hand, having an entrepreneurial environment has a very strong positive effect on the status and the willingness.

Nationality either own or parental has no influence on the average status of entrepreneurship, a non-Dutch background however does increase the willingness.

In Table 4.4 we present the results of the two different measures of risk aversion. The results are based on the observations presented in Table A4.4 (Appendix C4). You can clearly see that the willing to become an entrepreneur are less risk averse than the participants in the total population.

Table 4.4 Measures of risk aversion

Variable Participants Willing

Mean (s.d.) Mean (s.d.)

Reservation price 53.82 102.88 64.45 125.35

Arrow Pratt 0.0343 0.1182 0.0376 0.1412

In line with the predictions willingness appears to be increasing in risk attitude. Status is only higher for risk neutral subjects.32

4.5 Reasons to (not) become an entrepreneur

In Tables 4.5 and 4.6 we have summed up the reasons to (not) become an entrepreneur.33 Most of our subjects want to become entrepreneurs because they want to be independent (Own boss, 69.7 percent) or because they want a challenge (68.8 percent). High income is

32_{See Table A4.1d in Appendix C4}

33_{We asked all of our respondents both to state reasons why they do or do not want to become an}

entrepreneur. We are equally interested in both. If someone prefers to be an entrepreneur we want to know why that is, but we also want to know what might deter him or her from becoming an entrepreneur in the future. And if someone is not willing to become an entrepreneur we want to know why that is, but also why he might want to become an entrepreneur.

Status and entrepreneurship