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Rule-breaking, crime, and entrepreneurship 1

Running head: RULE-BREAKING, CRIME, AND ENTREPRENEURSHIP

Rule-breaking, crime, and entrepreneurship: A replication and extension study with 37-year

longitudinal data

Martin Obschonka a, Hakan Andersson

b, Rainer K. Silbereisen

a, Magnus Sverke

c,d

Forthcoming in

Journal of Vocational Behavior

DOI: 10.1016/j.jvb.2013.06.007

a __ Department of Developmental Psychology and

Center for Applied Developmental Science

Friedrich Schiller University

Am Steiger 3/1

07743 Jena, Germany

[email protected]

[email protected]

Phone: +49 (0)3641 945 922

Fax: +49 (0)3641 945 202

b __ Department of Psychology

Stockholm University

SE-106 91 Stockholm, Sweden

[email protected]

c __ Department of Psychology

Stockholm Stress Center

Stockholm University

SE-106 91 Stockholm, Sweden

[email protected]

d __ WorkWell: Research Unit for Economic and Management Sciences,

North-West University

South Africa


Abstract

Is there an intimate biographical relationship between entrepreneurship and antisocial

tendencies? Drawing from Zhang and Arvey’s retrospective study [Zhang, Z., & Arvey, R. D.

(2009). Rule breaking in adolescence and entrepreneurial status: An empirical investigation.

Journal of Business Venturing, 24(5), 436-447], which found a link between entrepreneurship

status of male adults and their recalled early antisocial rule-breaking behavior in adolescence,

the present study utilized prospective longitudinal data from a Swedish cohort study to clarify

the connection between antisocial rule-breaking, crime, and entrepreneurship by applying a

developmental perspective. Regression results, which controlled for early socioeconomic

background and intellectual competencies, indeed identified early antisocial rule-breaking

behavior in adolescence as a valid positive predictor of a subsequent entrepreneurial career in

adulthood in men (but not in women). In contrast, registered crime (teenage crime, adult

crime, and prototypical trajectories of criminal behavior) as well as rule-breaking attitude in

adolescence, as a more latent form of early antisocial tendencies, were relatively unimportant

in the prediction of entrepreneurship in both genders. The results are discussed with a focus

on rule-breaking and agency theories of entrepreneurship, youth theories, and the importance

of looking at gender differences in entrepreneurial development.

Keywords: Entrepreneurship; Crime; Rule-Breaking; Entrepreneurial Development;

Adolescence


Acknowledgments

This study was supported by the PATHWAYS International Postdoctoral Fellowship

Programme for the Comparative Study of Productive Youth Development (Jacobs

Foundation) and the Center for Applied Developmental Science (CADS) of the Friedrich-

Schiller-University of Jena, Germany. The authors thank Lars Bergman and Per Davidsson

for helpful comments.


Rule-breaking, crime, and entrepreneurship: A replication and extension study with 37-year

longitudinal data

The mug shot, taken by the Albuquerque Police Department in 1977, shows a young

man, virtually still a teenager, with tinted eyeglasses and an intelligent smile. More a nice

nerdy guy than a “criminal scallywag”, one might think when inspecting the picture. Indeed,

the portrait shows none other than Bill Gates, co-founder of Microsoft and one of the most

famous entrepreneurs of our time (http://www.mugshots.org/misc/bill-gates.html). He earned

worldwide respect for his key role in the personal computer revolution. Today, he is further

known for his philanthropist activities (e.g., donating a great share of his wealth to charity).

Back in 1977, he had been arrested for a traffic violation, according to media reports

(reportedly not the only arrest in his younger years). The existence of such a mug shot might

come as a surprise for many people thinking of famous entrepreneurs as “shining” heroes and

role models who make significant positive contributions to society (for example because they

managed the risk of starting a new venture, introduced innovations, created new jobs, and

contributed to social wealth; Solomon, 1996). For others, in turn, it may confirm a general

stereotype of entrepreneurs as some sort of a homo economicus that first and foremost looks

at their own personal advantages with the central motive of profit maximization, regardless of

morality or social and ethical principles (Anderson & Smith, 2007).

But is this Bill Gates example really typical for the “ordinary” entrepreneurial career

(Dyer, 1994)? Is there indeed a systematic relationship between early antisocial tendencies

that stand against society’s conventions (cf. Caspi, Elder, & Bem, 1987) and an

entrepreneurial career that contributes to society (e.g., via job creation)? Are these two sides

of the same coin in the biographies of many entrepreneurs? On the one side of the coin, there

is this enterprising behavior in adulthood – demanding and complex activities (e.g., founding

and managing a new business, creating new jobs, and introducing innovations) that require


the successful management of risks and diverse resources (e.g., financial, human, and

technological resources), and that many economists and policymakers praise as important

drivers of economic and technological development, job creation, and social wealth in our

globalized societies (Audretsch, 2007; Hisrich, Langan-Fox, & Grant, 2007). Indeed, existing

research often points to the prosocial side of entrepreneurship. In summarizing research on

morality and ethics among entrepreneurs vs. non-entrepreneurs, Harris, Sapienza, and Bowie

(2009) note that “some research indicates that entrepreneurs may indeed generally place a

greater emphasis on ethical behaviors [e.g., fairness, procedural justice, and trust] … and

exhibit higher levels of moral reasoning” when compared to non-entrepreneurs (p. 408; see

the original work for an overview of studies). On the other side of the same coin, there might

be some sort of a “dark side” among many entrepreneurs (Kets de Vries, 1985), in this case in

the form of early maladaptation and early antisocial rule-breaking. This negative form of early

unruliness might have been the seedbed of their entrepreneurial spirit in adulthood, as a more

productive form of unruliness and rule-breaking (Schumpeter, 1934). At the same time, early

antisocial unruliness could of course also lead to a number of negative developmental

outcomes in adulthood such as persisting antisocial behavior or crime.

One should also keep in mind that the teenage years are in general associated with an

increased prevalence of antisocial behaviors (e.g., Hirschi & Gottfredson, 1983; Sampson &

Laub, 1992), a fact that developmentalists often explain by changes in the brain structure and

functions (e.g., increased risk-taking and sensation seeking, Giedd, 2008; Steinberg, 2007),

negative peer group effects, or the so-called maturity gap in adolescence, which motivates

antisocial behavior by a misfit between biological and social maturity (Moffitt & Caspi,

2001). This does not mean though that antisocial behavior during the teenage years is

normative (so that it cannot be, in principle, more typical for certain groups such as

prospective entrepreneurs). Whereas the teenage years are often regarded as a period of


“storm and stress”, of rebellion and non-conformism to the adult world and societal norms,

research has found the majority of teenagers to go through adolescence in a relatively

unproblematic and prosocial fashion (Lerner & Steinberg, 2009). In other words, antisocial

behaviors are more prevalent but far from being the norm in adolescence.

The possible relationship between developmental aspects of crime or antisocial rule-

breaking and entrepreneurial status in adulthood has long remained untackled in empirical

research. The few existing studies on crime and antisocial rule-breaking among entrepreneurs

have mainly focused on a) specific subgroups such as entrepreneurial behavior among

criminals (e.g., drug entrepreneurs, Zaitch, 2003), b) ethics and morality in entrepreneurship

(Harris et al., 2009), and c) region-level relationships between crime and entrepreneurship

rates (research that indicates that, at the regional level, entrepreneurship is associated with

lower crime rates; e.g., Kreft & Sobel, 2005). Of course, like in (almost) every occupation,

there should be “good” and “bad” entrepreneurs. For example, in his seminal theoretical

article Baumol (1990) differentiated between productive, unproductive, and destructive

entrepreneurship and assumed that an entrepreneurial spirit can find expression in very

different ways including unproductive or destructive entrepreneurship (e.g., organized crime).

In his view, it is mainly the reward structure of the economy and the set of prevailing rules

that influence the manifestation of an entrepreneurial spirit into either productive or

unproductive entrepreneurship. According to this view, an entrepreneurial spirit per se is not

destructive or antisocial (but may have the potential for such negative expressions).

Such an emphasis on the context as something that has the potential to turn somebody

into a “bad guy” is quite popular. In crime research, this is a well-known notion derived, for

example, from the famous Stanford Prison Experiment and related studies (Haney, Banks, &

Zimbardo, 1973; see also Zimbardo, 2007). It also figures prominently in today’s media

landscape, for example in the award-winning TV series Breaking Bad, describing the


transformation of an ordinary high school chemistry teacher into a drug entrepreneur and

murderer. However, research has also presented convincing evidence that crime is not purely

dictated by the context. Recent research suggests that the psychological determinants of crime

may be best understood by applying a biopsychosocial model of human development in

context, a model that stresses the role of early developmental factors and trajectories of early

maladaptation and antisocial behavior over the life course (Caspi et al., 2002; Laub &

Sampson, 2003; Moffitt & Caspi, 2001; see also Andrews & Bonta, 2010).

Hence, the personal history matters, a notion that is not only a leading approach in

psychological crime research but is also receiving growing attention in entrepreneurship

research (for example in studies on early developmental precursors of an entrepreneurial

career in adulthood; Obschonka & Silbereisen, 2012; Schmitt-Rodermund, 2004, 2007;

Schoon & Duckworth, 2012). The question that is still on the table is whether many

entrepreneurs from the normal population do indeed have a “dark past” in terms of crime and

antisocial rule-breaking as a developmental precursor of an entrepreneurial career. This is a

fascinating question because it not only concerns a possible “dark side” of entrepreneurs – an

antisocial and even criminal personal history – but also combines crime and entrepreneurship

research with a developmental career perspective (Vondracek, 2001).

Zhang and Arvey’s (2009) Study on Rule-Breaking in Adolescence and Entrepreneurship

There is one existing study, published in the Journal of Business Venturing, that

already examined a possible connection between antisocial rule-breaking in adolescence and

entrepreneurship in adulthood (Zhang & Arvey, 2009). In this study, the authors analyzed a

sample of 165 men from the United States and tested whether recalled antisocial rule-

breaking behavior in adolescence would predict entrepreneurship status in adulthood.

The authors hypothesized a positive relationship, thereby drawing upon Willis’ (1963)

theory on nonconformity and related research (e.g., Anderson & Stoffer, 1979), which suggest


a connection between rule-breaking and central entrepreneurial characteristics such as

independence, autonomy, creativity, and innovation. Zhang and Arvey argue that “the ability

to question socially accepted norms and challenge the status quo is just what is needed to

become a successful entrepreneur” and that “autonomy and independence underlying modest

rule-breaking in adolescence can help the individual to form a habit of thinking ‘out of the

box’ and behaving in an innovative way” (p. 440). This fits with Schumpeter’s (1934) classic

definition of the entrepreneur, who seeks social distinction, thinks out of the box, has an inner

drive to innovate, and is willing to break through traditional structures and to challenge the

accepted way of doing things. Zhang and Arvey further refer to Gould (1969), who “believes

that entrepreneurs are something close to juvenile delinquents” (p. 440). Following Kaplan

(1980), they make a distinction between modest rule-breaking (e.g., taking part in group

fights, deliberately damaging school property, accepting stolen merchandise, defying their

parents’ authority to their face, or being placed on school probation or expelled) and severe

rule breaking (e.g., being picked up by the police, driving a car when under the influence of

alcohol or drugs, buying illegal drugs, or stealing a valuable item).

Zhang and Arvey’s (2009) results showed entrepreneurship status in adulthood (at age

37) to be positively predicted by recalled modest rule-breaking behavior in adolescence. Such

an effect did not hold true for the recalled severe rule-breaking behavior in adolescence. Their

study thus provides first empirical evidence suggesting that early antisocial tendencies are

indeed associated with entrepreneurship.

However, Zhang and Arvey’s study also has major limitations that restrict the validity

of their findings, as also stressed by the authors themselves. First, their study solely relies on

self-reports with regard to antisocial tendencies. Second, and even more important, all

information on early rule-breaking in adolescence refers to retrospective statements made in

adulthood (at age 32). These statements are thus subject not only to a social desirability bias


(self-reports) but also to a recall bias (Rutter, Maughan, Pickles, & Simonoff, 1998).

Arguably, such biases could be particularly prevalent in the case of crime or rule-breaking

data because such information refers to very private information that may disclose a dark

personal past. Third, Zhang and Arvey’s study does not consider official crime records, which

should be a valuable objective source for a study on antisocial tendencies. Fourth, and this is

another major limitation, their study solely focused on entrepreneurs vs. managers when

predicting entrepreneurial status (who becomes a manager vs. entrepreneur?). The results are

thus restricted to these two groups. Fifth, their study did not consider antisocial tendencies in

adulthood. The authors see this, however, as a “critical question” for entrepreneurship

research, namely whether early antisocial tendencies among prospective entrepreneurs persist

in adulthood “in terms of neglecting or bypassing social codes of ethical standards” (p. 444).

The Present Study

Inspired by Zhang and Arvey’s (2009) results, the present study was set up as a

replication test and an extension of their study by making use of longitudinal data that covers

childhood, adolescence, and adulthood and includes rich data on crime and antisocial rule-

breaking. The data used in the present study stemmed from the Individual Development and

Adaptation (IDA) study, a prospective study that followed the lives of a complete school

grade cohort from a Swedish urban region well into middle adulthood (Bergman, 2000;

Magnusson, 1988). Particularly important for the purpose of the present study, IDA is an

established dataset in the research field on crime and antisocial rule-breaking (Bergman,

Andershed, & Andershed, 2009; Mahoney, Stattin, & Magnusson, 2001; Stattin, Kerr, &

Bergman, 2010; Stattin, Magnusson, & Reichel, 1989). IDA is representative of the Swedish

population on registered crime (Stattin, Magnusson, & Reichel, 1989) and central findings in

crime research have been replicated in the IDA data (e.g., Stattin et al., 2010 broadly

replicated Moffitt’s famous typology of developmental trajectories of delinquent behavior and


underlying developmental pathways, Moffit & Caspi, 2001). Moreover, IDA has been widely

used in research on a variety of other topics of human development such as longitudinal

predictors of educational and occupational attainment (e.g., Andersson & Bergman, 2011;

Huang & Sverke, 2007; Kokko, Bergman, & Pulkkinen, 2003). One can thus conclude that

the IDA study lends itself as a suitable dataset to longitudinally investigate the research

questions Zhang and Arvey (2009) raised and to clarify the connection between crime or

antisocial rule-breaking and entrepreneurship from a developmental perspective.

The present study aimed at testing three research questions. The first refers to

replication (Pashler & Wagenmakers, 2012) and asks whether Zhang and Arvey’s (2009)

central finding, that recalled modest (but not severe) antisocial rule-breaking in adolescence

predicts entrepreneurship in adulthood, can be replicated in a longitudinal study that examines

a non-selected sample from the normal population and also considers crime data.

The second and third research questions then go beyond replication and test something

new, as explained in the following. The second question makes a distinction between latent

and manifest antisocial tendencies by looking at antisocial thinking vs. antisocial acting

separately (Engels, Luijpers, Landsheer, & Meeus, 2004). The cognitive perspective, which

does not primarily focus on behavior but rather on mental states and processes, has become a

key approach in contemporary psychological entrepreneurship research (Hisrich, Langan-Fox,

& Grant, 2007). Following the logic of Zhang and Arvey’s (2009) unruliness-hypothesis of

entrepreneurial development, one developmental precursor of an entrepreneurial mindset in

adulthood (Carsrud & Brännback, 2009) might be the early unruly mindset (which may not

necessarily be manifested in juvenile antisocial behavior, for example due to a possible

inhibiting effect of high social control in the proximal environment; Hirschi, 2002). Hence, a

pure focus on behavioral aspects of antisocial rule-breaking might bring along the danger of

overlooking a possible role of more latent forms of antisocial tendencies. For example, it


might be the case that prospective entrepreneurs differ from others in that they think and feel

in antisocial ways in adolescence (and not so much in terms of actual antisocial behavior).

Hence, a study interested in shedding light on a possible link between antisocial tendencies

and entrepreneurship should look at both behavioral and non-behavioral aspects of antisocial

tendencies. The present study thus not only tested whether prospective entrepreneurs would

differ from non-entrepreneurs in terms of manifest antisocial behavior (i.e., early rule-

breaking behavior, teenage/adult crime), but also considered a non-behavioral and more latent

form of antisocial rule-breaking, namely antisocial attitudes in adolescence as an indicator of

adolescents’ antisocial thinking (Butler, Leschied, & Fearon, 2007).

Third, while Zhang and Arvey’s (2009) study examined men only (male entrepreneurs

vs. male managers), the present study focuses on entrepreneurial development among both

men and women. Although crime is often described as a “male phenomenon”, it is an

established tradition in developmental research on antisocial tendencies to consider both

genders (e.g., Moffitt, Caspi, Rutter, & Silva, 2001). Moreover, entrepreneurship research has

a strong interest in knowing more about the development of entrepreneurship not only in men

but also in women (Kelley, Brush, Green, & Litovsky, 2011; Kourilsky & Walstad, 1998).

The present study asked: Do results differ between the genders?

Method

Sample

This study is based on data from the longitudinal research program Individual

Development and Adaptation (IDA; Bergman, 2000; Magnusson, 1988). In IDA, an entire

school grade cohort of approximately 1,000 children in the medium-sized town of Örebro in

Sweden were followed to adult age, starting in 1965 at age 10. Children who moved into the

town after age 10 were added to the cohort. Bergman (1973) compared the IDA sample in

Grade 6 with a fairly representative national sample with regard to a composite general


achievement test given in 1968 and 1969, and no significant difference was found.

Throughout the program, the dropout rate has been very low (below 5% at school age in each

data collection and 11% at age 43 for the women and 18% for the men at age 47; see

Bergman, 2000; Trost & Bergman, 2004). In the present study, data from age 10, 13, 15, 16,

and 43 (women) or 47 (men) were used (which sums up to an overall length of data collection

of 37 years). In addition, data from official records were used to measure criminality.

Variables

Teenage and adult crime. Registered criminality was obtained from the records of the

local police, social authorities, and child welfare authorities in all the police districts in which

any one of the participants had lived up to his or her 35th birthday (Stattin, Kerr, & Bergman,

2010). Data were also collected from the National Police Board. Complete register data were

secured for all participants except one (see Stattin & Magnusson, 1991). Only offences that

led to some sort of societal sanction were counted (e.g., crimes against the Swedish Penal

Code, the Swedish Road Traffic Offences Act, the Swedish Drug Offences Ordinance, or the

Swedish Currency Ordinance). For children below the age of criminal responsibility, a crime

was counted only if the child, after a hearing by the local police to determine guilt, was turned

over to the child welfare authorities. Following Bergman and Andershed’s (2009) assessment

of registered crime in adolescence and adulthood, we used dummy variables. Teenage crime

was coded as 1 if the person had at least one recorded crime before age 18 (23.3% of the male

sample) and 0 otherwise. Due to the very low prevalence of teenage crime in women (2.2%),

this variable was not considered in the statistical analyses in women. Adult crime was coded 1

if the person had at least one recorded crime between age 18 and 35 (29.3% of the male

sample; 8.7% of the female sample) and 0 otherwise.

Rule-breaking behavior in adolescence. Measures of rule-breaking behavior stemmed

from the Norm Inventory (Magnusson, Dunér, Zetterblom, 1975; see also Stattin et al., 2010)


and were given at age 15. The measure comprised eight norm-breaking activities covering

activities at home (ignoring parents’ prohibitions, staying out late without permission), at

school (cheating in an exam, truanting), and during leisure time (smoking hashish, getting

drunk, shoplifting, loitering in town in the evening). They reported how many times they had

engaged in each norm-breaking behavior: ‘never’ (1), ‘once’ (2), ‘2–3 times’ (3), ‘4–10 times’

(4), and ‘more than 10 times’ (5). A mean score was created from the eight items (Cronbach’s

α = .82) (Men: M = 2.03, SD = 0.70; Women: M = 1.86, SD = 0.71).

Rule-breaking attitude in adolescence. Consistent with the rule-breaking behavior

items, measures of rule-breaking attitudes were also taken from the Norm Inventory

(Magnusson et al., 1975) and were also given at age 15. The measure comprised the

adolescents’ attitudes toward the same eight norm-breaking activities as described above

(how acceptable such antisocial behavior is in their view). The question was “What do you

think about?” followed by the eight behaviors rated on a scale from 1 (Very stupid) to 7

(Perfectly acceptable). A mean score was created from the eight items (Cronbach’s α = .82)

(Men: M = 2.63, SD = 0.91; Women: M = 2.59, SD = 0.91). Consistent with prior research on

delinquent attitudes (e.g., Engels, Luijpers, Landsheer, & Meeus, 2004), these rule-breaking

attitudes were positively correlated with the actual antisocial behavior [with rule-breaking

behavior in adolescence (Men: r = .66, p < .001; Women: r = .73, p < .001) and also with

teenage crime (Men: r = .21, p < .001) and adult crime (Men: r = .14, p < .001; Women: r =

.13, p < .01)].

Entrepreneurship in adulthood. Information about entrepreneurship was taken from an

extensive interview conducted at age 43 for the women and age 47 for the men. One major

aim of these extensive interviews was to collect in-depth data on the individual occupational

career. The interview assessed the current work situation and also carefully reconstructed the

person’s total working and educational career in intervals of at least six months (in other


words, only those jobs that lasted for at least six months were considered; for more details see

Trost & Bergman, 2004 and Huang & Sverke, 2007). Two measures of entrepreneurship in

adulthood were used in the present study.

First, to achieve a comparable measure of entrepreneurial activity as employed in

Zhang and Arvey’s (2009) study (which focused on entrepreneurship status at a certain age),

entrepreneurship status at age 43/47 was assessed. This variable indicates if the person at the

time of the interview (when the men were 47 and the women 43) was currently running their

own business (0 = employed, 1 = own business [Men: 21.4%; Women: 8.1%]).

However, such a measure is unlikely to reflect a person’s entrepreneurial activities

over longer periods of their occupational career (for example, it may underestimate the

prevalence of entrepreneurial activity over the career). In this vein, Davidsson (2007) stressed

that “if we are to successfully explain or predict entrepreneurial action and success with

(distal) variables on the individual level, then entrepreneurship has to be assessed broadly on

the individual level, and preferably over longer periods of time” (p. 295). Likewise,

Sarasvathy (2004) called for more entrepreneurship research that actually considers

enterprising activities over a longer period of time to better understand the (development of

the) enterprising individual. Following this line of thinking, the present study, in contrast to

Zhang and Arvey’s (2009) analysis, also employed a second measure of entrepreneurship in

adulthood: Entrepreneurship over the career. This variable was derived from the in-depth

data on the individual occupational career collected in the age 43 (women) and age 47 (men)

interviews, which reconstructed the total working career history in six-monthly intervals, as

explained above (until the age of 43/47). The variable indicates if the person has been an

entrepreneur (running their own business for at least six months) at least once over the course

of their career in adulthood (vs. all others) (no = 0, yes = 1 [Men: 32.1%; Women: 10.1%]).


Control variables. To consider differences in the socioeconomic developmental

background and in intellectual competencies, measures of parental socioeconomic status

(SES), creativity, and intelligence were used as control variables in the analyses. The

consideration of such factors is well established in both crime and career research (Schoon &

Duckworth, 2012; Stattin et al., 2010; Ward, 2004; Wiesner, Capaldi, & Kim, 2010).

Parents’ SES was measured using a survey of parents undertaken when the children

were 13 years old. The measure is a composite of the educational attainment of the parent

with the highest education and family income (Andersson & Bergman, 2011). Educational

attainment was measured by a seven-point scale ranging from 1 (Compulsory school) to 7

(University degree). Family income was measured on a seven-point scale indicating income

intervals (1 indicated low income and 7 high income). If such data from age 13 were missing,

data from age 10 (survey of parents) was used. The mean score of the two (z-standardized)

items was computed (Men: M = -0.06, SD = 1.76; Women: M = 0.07, SD = 1.86).

Early creativity was measured by two scales assessing divergent thinking (Guilford,

1967) at age 13 and 16 (details are provided in Magnusson, Dunér, & Zetterblom, 1975). The

first scale (age 13) is a verbal test (“Consequences test”) designed by Larsson and Sandgren

(1968). The test contains five items of the type “What do you think would happen, what

would be altered and what would follow if water could burn?”. The verbal fluency of the

answers was scored (i.e., the number of replies given). Cronbach’s α is between .84 and .88

for girls/boys. The test is similar to Guilford’s (1967) Divergent Semantic Units. The second

scale, measured at age 16, is the Titles Test, which is a Swedish version of the Plot Titles Test

(Guilford, 1967). The task was to formulate titles to fit texts. The absolute number of fitting

titles that were produced in a given time was scored. We combined the two scales (mean of

the z-standardized scale scores) to achieve a trait-like, multi-method measure of early

creativity in adolescence (Men: M = -0.04, SD = 0.89; Women: M = -0.04, SD = 0.89).


Intelligence was assessed in grade 6 (age 13). The study participants completed six

written group tests (two verbal, two logical-inductive, and two spatial tests) from the

Differential Intelligence Analysis battery (DIA, Härnquist 1961). Together these tests, which

were part of the ordinary school program in grade 6, assessed participants’ overall intelligence

(Magnusson, Dunér, & Zetterblom, 1975; Kiuru, Salmela-Aro, Nurmi, Zettergren, Andersson,

& Bergman, 2012). The mean of the six subscales was used to form the variable intelligence

(Cronbach’s α = .84) (Men: M = 24.26, SD = 4.62; Women: M = 24.92, SD = 4.49).

Results

The results are presented for men and women separately. Given that this study examines

relatively distal factors of entrepreneurship in adulthood so that effects should be relatively

small and hard to detect, results up to the 10% significance level are reported (see also

Schmitt-Rodermund, 2004).

Preliminary Analyses

To achieve a first preliminary picture on the relationships between the variables, zero-

order relationships between the two entrepreneurship variables on the one hand

(Entrepreneurship over the career – no/yes; Entrepreneurship status at age 43 in women and

age 47 in men), and the other variables on the other were calculated. To check which

variables distinguish entrepreneurs from non-entrepreneurs, two-tailed t-tests were conducted

in the case of continuous variables (the three control variables and the two rule-breaking

variables) and χ2 tests in the case of dichotomous variables (the two crime variables). These

zero-order relationships are summarized in Table 1.

The following control variables were associated with the entrepreneurship variables.

Parental socioeconomic status was positively associated with entrepreneurship status (at age

43/47) in both genders. Those boys and girls who grew up with higher-status parents were

more likely to be working as an entrepreneur at that point in time in middle adulthood.


Moreover, parental SES showed a positive relationship with entrepreneurship over the career

in women. Those girls who grew up with higher-status parents had a higher likelihood of

having worked at least once as an entrepreneur over their occupational career by the age of

43. Intelligence, measured at age 13, showed a tendency with entrepreneurship over the career

in women. Those girls who achieved higher scores in the intelligence tests were more likely to

have worked at least once as an entrepreneur in their occupational career by the age of 43.

With regard to the central study variables, rule-breaking behavior in adolescence

showed a zero-order relationship with the entrepreneurship over the career variable. Those

boys who showed more rule-breaking behavior in adolescence were more likely to have

worked as an entrepreneur at least once in their occupational career (assessed until the age of

47). In contrast, neither rule-breaking attitudes in adolescence nor the crime variables

(teenage crime, adult crime) showed a relationship with entrepreneurship in adulthood. In

other words, compared to non-entrepreneurs, entrepreneurs did not differ in terms of

registered teenage or adult crime and early attitudes towards rule-breaking.

Such an analysis looking at teenage crime and adult crime separately may overlook the

role that developmental patterns in criminal activity could play in entrepreneurial careers.

Such developmental patterns, for example Moffitt’s famous developmental taxonomy of

adolescence-limited and life-course persistent criminal behavior (Caspi & Moffitt, 2001),

figure prominently in contemporary crime research (Stattin, Kerr, & Bergman, 2010). Hence,

an additional analysis using the male sample tested whether male entrepreneurs differ from

male non-entrepreneurs in terms of developmental patterns of criminal behavior (not an

offender, adolescence-limited offender, adult onset offender, life-course persistent offender,

Caspi & Moffitt, 2001). Consistent with earlier research (e.g., Bergman & Andershed, 2009;

Stattin et al., 2010), each male respondent was grouped into one of these categories (using the

dichotomous teenage and adult crime variables). Then a χ2 test revealed that these


developmental patterns of crime were not associated with any of the entrepreneurship

variables. This underscores the result that crime is not related to entrepreneurship, neither

teenage/adult crime nor developmental patterns of crime.

Regression Analyses

To test the three research questions of the present study, hierarchical logistic regression

analyses were employed (Cohen, Cohen, West, & Aiken, 2003). These analyses estimated the

controlled effects of the central predictor variables (rule-breaking tendencies and crime) on

the two entrepreneurship variables. The first step of the regression considered the control

variables; the second step then added the rule-breaking in adolescence variables, and the third

the crime variables. Multicollinearity analyses (variance inflation factor, VIF) indicated that

the regression results are unbiased in this regard.

Among the control variables (Step 1 of the regression), parental SES showed a

positive tendency with entrepreneurship over the career and a positive effect significant at the

5% level on entrepreneurship status at age 47 in men. Moreover, in women creativity in

adolescence showed a positive tendency with entrepreneurship over the career and a positive

effect significant at the 5% level on entrepreneurship at age 43. In contrast, intelligence did

not predict entrepreneurship in adulthood.

Among the rule-breaking in adolescence variables (step 2 of the regression), rule-

breaking behavior in adolescence showed a positive effect significant at the 5% level on

entrepreneurship over the career in men. Hence, the zero-order relationship between such an

early unruly behavior and entrepreneurial activity over the career (as illustrated in Table 1)

persisted in this regression analysis. Early rule-breaking attitude, the more latent form of early

unruliness, was irrelevant as a predictor of entrepreneurship in adulthood.

Finally, the crime variables (teenage crime, adult crime) showed no relationships with

entrepreneurship in adulthood in the third step of the regression. In other words, registered


crime was irrelevant as a predictor of entrepreneurial activity in both genders. Again, among

the study variables, early rule-breaking behavior showed up as a valid predictor here.

Drawing from prior research pointing to the relevance of intraindividual constellations

of personal factors when describing and understanding the entrepreneur (e.g., Obschonka,

Schmitt-Rodermund, Silbereisen, Gosling, & Potter, in press) and Schumpeter’s (1934)

theorizing suggesting that the entrepreneur may actually need both creativity and rule-

breaking (“creative destruction”), a follow-up analysis checked for interactions between the

early antisocial tendencies (rule-breaking attitudes and behaviors in adolescence, and teenage

crime) and early intellectual competencies (intelligence and creativity). This tests the idea that

when both a bright, creative mind and rule-breaking tendencies come together early in life,

then a developmental pathway towards entrepreneurship may be particularly likely. It seems

plausible to assume that early antisocial rule-breaking may be canalized into productive rule-

breaking in adulthood (e.g., entrepreneurial agency) by high levels of intellectual

competencies. Interaction tests were conducted following Cohen et al.’s (2003)

recommendations. However, there was no indication for interactions between intellectual

competencies and early antisocial tendencies in the prediction of entrepreneurship.

Discussion

The first research question of the present study examined whether Zhang and Arvey’s (2009)

retrospective finding suggesting that entrepreneurs are special in that they show (modest but

not severe forms of) early antisocial rule-breaking in adolescence more often than others can

be replicated longitudinally. The data revealed that antisocial rule-breaking behavior in

adolescence (modest early rule-breaking) indeed matters as a valid predictor of

entrepreneurship in adulthood, but only among men and particularly in the prediction of

entrepreneurship over the career (whether the male participants worked at least once as an

entrepreneur in their occupational career up to the age of 47). Here, it was in fact the strongest


predictor among all variables considered in this study (control and study variables). In

contrast, more severe antisocial rule-breaking behavior in adolescence, namely registered

teenage crime, did not forecast enterprising activity in adulthood. For men, the present study

thus replicates Zhang and Arvey’s (2009) central finding that modest but not severe antisocial

rule-breaking in adolescence is associated with entrepreneurship in adulthood, but one should

keep in mind that this applied only for the long-term entrepreneurship measure. This

underscores the importance of considering a career perspective (Vondracek, 2001) and

looking at (potential) entrepreneurship over longer periods in the occupational career,

particularly if one studies distal predictors (Davidsson, 2007).

The second research question asked: What matters more as a precursor of

entrepreneurship in adulthood, latent or manifest antisocial tendencies? Regarding men, the

clear message from the present analysis is that the actual modest but not severe rule-breaking

behavior, as a manifest form, matters more than the latent form (early antisocial attitudes).

This concurs with behavioral approaches in entrepreneurship research stressing the role of

actions and human agency (Frese, 2009). It is further consistent with a number of existing

developmental studies indicating that behavioral measures targeting the teenage years (e.g.,

early leadership, inventive, and commercialization activities as early age-appropriate forms of

entrepreneurial human agency) are particularly predictive for entrepreneurship in adulthood

(e.g., Obschonka, Silbereisen, & Schmitt-Rodermund, 2010; Schmitt-Rodermund, 2007).

Entrepreneurs are often described as the type of persons that first and foremost act and such

an emphasis on human agency may also apply to their early personal development. In

adolescence, they may have a certain drive to behave in antisocial ways (with regard to

modest rule-breaking). Among women, the picture was less clear as both latent (attitude) and

manifest forms of antisocial tendencies (rule-breaking behavior, crime) did not show any

relationships with entrepreneurship in adulthood.


In view of these results and the results referring to the first research question, one must

answer the third research question (whether results would differ between men and women)

with a clear yes. Aspects of early antisocial tendencies (rule-breaking behavior) appear to

matter much more in men as a developmental precursor of engagement in entrepreneurship in

adulthood. Severe rule-breaking (registered crime) did not show any effects on

entrepreneurship at all in either gender, but one should keep in mind that in the present study

unfortunately no measure of early severe antisocial rule-breaking behavior in women was

available. These gender-specific results illustrate the importance of looking at both genders

separately in developmental research on entrepreneurship. Career paths and their determinants

may substantially differ between male and female entrepreneurs as suggested by a number of

existing studies (Kelley, Brush, Green, & Litovsky, 2011; Schoon & Duckworth, 2012).

Likewise, gender aspects have been shown to play an important role in developmental

research on patterns and determinants of antisocial behavior (Moffitt, Caspi, Rutter, & Silva,

2001). If female entrepreneurship, in contrast to male entrepreneurship, indeed does not

develop so much out of early antisocial tendencies, the question arises whether this gender

difference establishes a developmental fundament for gender differences in the actual

entrepreneurial behavior in adulthood (for example in gender differences in the “radicality” of

constructive rule-breaking as an entrepreneur, or in risk-taking and growth aspirations when

starting and growing new businesses, Morris, Miyasaki, Watters, & Coombes, 2006).

Regarding effects of the control variables, it was interesting to see that general

intelligence, assessed in adolescence, was irrelevant in the prediction of entrepreneurship.

This is consistent with research indicating that it is not general intelligence but, if anything,

specific facets of intelligence that matters for entrepreneurship (e.g., analytical, creative, and

practical aspects of intelligence, Baum & Bird, 2010; Sternberg, 2004).

The present study also delivers interesting results regarding early creativity and


socioeconomic background. Creativity is an often-named prototypical entrepreneurial

characteristic because seminal theorizing on the entrepreneur gives creative behaviors a

unique role (e.g., with regard to innovation, business idea generation, and venture creation;

Audretsch, 2007; Schumpeter, 1934; see also Ward, 2004). Indeed, some studies have found

entrepreneurs to differ from non-entrepreneurs in terms of higher levels of creativity (e.g.,

Engle, Mah, & Sadri, 1997). Due to their cross-sectional or short-term focus, such studies

usually cannot disentangle (long-term) selection and socialization processes (for example

whether higher levels of creativity are a cause or a consequence of entrepreneurship; cf. Kohn

& Schooler, 1982). In the present study, creativity differences in adolescence mattered in the

prediction of entrepreneurship in women, but did not show any zero-order relationships with

entrepreneurship in either gender. One should keep in mind that creativity assessed as early as

in adolescence is relatively distal to the occupational career, which may explain some of these

weak relationships. Future studies could put a special focus on the mutual interplay between

creativity and entrepreneurial activity. It might be particularly interesting to explore possible

socialization processes associated with entrepreneurial work experiences – does

entrepreneurial work enhance creativity over time?

Finally, early interindividual differences in parental socioeconomic status appear to

exert long-term (or cumulative) effects on entrepreneurial career choices in adulthood. The

regression result that those boys who grew up with wealthier and more educated parents had a

higher likelihood of entrepreneurship in adulthood may have to do with the provision of

certain resources by the parents (e.g., financial capital or networks) that facilitate

entrepreneurial behavior among their offspring.

Limitations

Although the present study has several advantages (e.g., longitudinal data, the richness of the

rule-breaking and crime data, etc.), it also has a number of limitations. First, it does not look


at the actual kind of entrepreneurial activity in adulthood (e.g., which type of business,

economic sector, innovation) or at entrepreneurial success. However, this is similar to the

original study by Zhang and Arvey (2009). Future studies looking at antisocial tendencies in

the individual development of entrepreneurs may also consider these entrepreneurial work

outcomes. Do, for example, those entrepreneurs who showed higher levels of antisocial

tendencies in adolescence found different types of businesses or achieve better entrepreneurial

success than less unruly entrepreneurs?

Second, regarding crime data only registered crime was considered (Bergman &

Andershed, 2009). The present study thus cannot draw any conclusion on potential “hidden”

criminal activities in entrepreneurial development that were not registered in official records.

This study could thus somewhat underestimate the effect of severe rule-breaking.

Third, the reader should keep in mind that this study could not consider a measure of

severe rule-breaking behavior in adolescence in the female sample, but the results point to the

direction that early rule-breaking is in general rather unimportant as a developmental

precursor of female entrepreneurship.

Conclusion

Despite these potential limitations, the longitudinal results presented here support the

unruliness hypothesis of adolescent entrepreneurial development in men (Zhang & Arvey,

2009). Taking Zhang and Arvey’s and this study together, the relationship between modest

antisocial behavior in adolescence and male entrepreneurship in adulthood becomes clearer

and clearer. While Harvard Business School Professor Abraham Zaleznick once noted: “I

think if we want to understand the entrepreneur, we should look at the juvenile delinquent”

(cited in Solomon, 1996, p. 85; see also Gould, 1969), one might also say: “If we want to

understand the (development of the male) entrepreneur, we should look at the juvenile

(modest) rule-breaking behavior”.


The fact that the present study found no systematic relationship between crime and

entrepreneurship at all (in either gender) implies that the “dark side” of male entrepreneurship

in terms of early antisocial tendencies may actually be limited to modest misconduct in their

younger years (misconduct that may indeed indicate a certain early antisocial tendency but

does not fundamentally violate societal principles or cause damage to society as more severe

antisocial behaviors such as crime do). These results thus do not draw an overly negative

picture regarding antisocial tendencies among entrepreneurs. The results rather suggest that

male entrepreneurs, when compared to male non-entrepreneurs, may go through a somewhat

stronger rebellious and non-conformist phase in adolescence with regard to their behaviors;

they may “drift” towards antisocial involvements in their adolescent years (Hagan, 1991)

without becoming outlaws or developing into notorious criminals. According to Zhang and

Arvey’s (2009) analysis, an increased level in the trait risk-taking among prospective

entrepreneurs might contribute to these more unruly teenage years in this group.

To sum up, there are indeed two sides of the same coin: on the one side

entrepreneurial agency and on the other early antisocial tendencies in males in the form of

modest rule-breaking behavior. However, the data also points to the direction that these

prospective male entrepreneurs are then well-adapted in adulthood. This would integrate, on

the one hand, the research stressing the prosocial side of many entrepreneurs and their

business activities (Harris, Sapienza, & Bowie, 2009) and, on the other hand, the growing

evidence of particularly frequent (modest, not severe) antisocial rule-breaking behaviors

during the teenage years of many male entrepreneurs. And, last but not least, it would solve

the apparent paradox of finding early mug shots (associated with minor offences) of those that

became famous entrepreneurs and a positive role model and inspiration for generations of

new entrepreneurs.


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Table 1

Zero-Order Relationships between the Control, Crime, and Rule-Breaking Variables and the

Entrepreneurship Variables

Note. Values in bold refer to the male sample; underlined values refer to the female sample. a The

variable Teenage crime was not considered in the female sample due to the low prevalence (2.2% of

the females had registered teenage crime). b The values in this column refer to mean scores in the case

of continuous variables (mean of non-entrepreneurs / mean of entrepreneurs), and to percentages in the

case of dichotomous variables (prevalence of entrepreneurship among those who showed no teenage

or adult crime / prevalence of entrepreneurship among those who showed teenage or adult crime).

†p < .10. *p < .05. **p < .01.

Entrepreneurship over the career Entrepreneurship status

(Males: Age 47 / Females: Age 43)

no / yes b

Statistical test no / yes Statistical test

Control variable:

Parents’ SES

-.07 / .22

.05 / .68

t(359) = -1.50

t(455) = -2.18*

-.13 / .51

-.00 / .63

t(366) = -3.08**

t(447) = -1.98*

Creativity (Age 13/16)

-.03 / .01

-.03 / .16

t(324) = -.39

t(493) = -1.38

-.06 / .09

-.04 / .16

t(335) = -1.35

t(483) = -1.40

Intelligence (Age 13)

24.72 / 25.10

25.12 / 26.30

t(321) = -.70

t(435) = -1.69†

24.49 / 25.27

25.10 / 25.98

t(332) = -1.26

t(429) = -1.11

Central variables:

Rule-breaking attitude in

adolescence (Age 15)

2.57 / 2.63

2.58 / 2.79

t(295) = -.47

t(51) = -1.22

2.56 / 2.62

2.59 / 2.71

t(304) = -.47

t(435) = -.78

Rule-breaking behavior in

adolescence (Age 15)

1.93 / 2.17

1.82 / 1.98

t(155) = -2.10*

t(443) = -1.50

1.96 / 2.06

1.82 / 1.96

t(304) = -1.06

t(37) = -.93

Teenage crime (no/yes) a

31.6% / 34.3%

−

χ(1) = .19

−

22.1% / 18.6%

−

χ(1) = .42

−

Adult crime (no/yes)

30.6% / 36.7%

9.9% / 10.0%

χ(1) = 1.15

χ(1) = .00

21.3% / 21.7%

7.9% / 7.7%

χ(1) = .01

χ(1) = .00


Table 2

Hierarchical Logistic Regression Analyses

Entrepreneurship over the career

(1 = yes)

Entrepreneurship status

(Males: Age 47 / Females: Age 43) (1 = yes)

Step 1 Step 2 Step 3 Step 1 Step 2 Step 3

B SE B OR B SE B OR B SE B OR B SE B OR B SE B OR B SE B OR

Control variable:

Parents’ SES 0.14

0.10

0.08

0.10

1.154

1.107

† 0.15

0.13

0.08

0.10

1.157

1.134

†

0.16

0.13

0.08

0.10

1.173

1.135

*

0.23

0.09

0.09

0.12

1.260

1.090

* 0.23

0.11

0.09

0.12

1.261

1.114

**

0.23

0.11

0.09

0.12

1.264

1.113

**

Creativity

(Age 13/16)

-0.04

0.42

0.16

0.22

0.963

1.526

†

-0.09

0.43

0.17

0.22

0.918

1.535

†

-0.06

0.43

0.17

0.22

0.940

1.537

†

0.02

0.56

0.18

0.25

1.015

1.757

*

0.00

0.56

0.18

0.25

1.001

1.748

*

0.02

0.56

0.19

0.25

1.019

1.746

*

Intelligence

(Age 13)

0.00

0.08

0.03

0.06

1.000

1.082

0.02

0.08

0.04

0.06

1.015

1.083

0.02

0.08

0.04

0.06

1.021

1.082

0.00

0.06

0.04

0.06

1.004

1.063

0.01

0.06

0.04

0.06

1.010

1.065

0.01

0.06

0.04

0.06

1.012

1.065

Central variables:

Rule-breaking attitude

in adolescence (Age 15)

-0.33

0.01

0.21

0.30

0.721

1.014

-0.34

0.01

0.22

0.30

0.713

1.007

-0.12

-0.01

0.23

0.35

0.890

0.996

-0.12

0.01

0.23

0.35

0.889

1.002

Rule-breaking behavior

in adolescence (Age15)

0.65

0.33

0.27

0.38

1.907

1.384

* 0.58

0.35

0.28

0.40

1.778

1.413

*

0.26

0.28

0.30

0.43

1.294

1.323

0.24

0.27

0.30

0.45

1.269

1.303

Teenage crime a

(1 = yes)

0.18

−

0.38

−

1.196

−

-0.13

−

0.44

−

0.877

−

Adult crime

(1 = yes)

0.36

-0.18

0.33

0.80

1.429

0.835

0.32

0.12

0.37

0.82

1.377

1.122

R2 (Nagelkerke) .02

.06

.05

.07

.05

.07

.04

.07

.05

.07

.07

.08

N 285

370

285

370

285

370

294

364

294

364

294

364

Values in bold refer to the male sample; underlined values refer to the female sample. a The variable Teenage crime was not considered in the female

sample due to the low prevalence. OR = Odds ratio.

†p < .10. *p < .05. **p < .01. ***p < .001.

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