Capital, Competence and Criminal Success / 1035
When Crime Pays:
Capital, Competence, and Criminal Success*
BILL MCCARTHY, University of California, Davis
JOHN HAGAN, Northwestern University, American Bar Foundation
Abstract
Several theoretical traditions offer insights into individual success in conventional
activities. We extend this work, suggesting that explanations of success also apply to crime:
although prosperity in licit or illicit activities has several unique antecedents, success in
either endeavor is influenced by common faactors. Most research on conventional success
focuses on the effects of human and social capital, and criminal forms of these are
important for illegal success. We argue that various aspects of conventional personal
capital — a heightened desire for wealth, a propensity for risk-taking, a willingness to
cooperate and competence — also play important roles in both legal and illegal
prosperity. We demonstrate the importance of various types of capital, particularly the
salience of personal capital, with data on drug-selling income.
In the late 1960s, Gary Becker (1996) provocatively argued that the decision to
commit economic crimes resembles other productive choices such as the decision
to seek employment. Several economists and sociologists have extended Becker’s
idea, studying crime in ways that resemble employment studies.1 Yet, labor studies
differ from research on offending: investigations of employment often extend the
examination of who does and does not work, exploring occupational success and
the attainment of prestigious jobs and higher earnings, whereas studies of crime
typically ignore illegal prosperity and criminological theories offer few insights on
this topic. Indeed, the perspective of two influential criminologists appears to be
shared by many researchers; according to Gottfredson and Hirschi (1990:44) there
* This research was supported by grants from the Social Science and Humanities Research Council
(Canada) and the University of California, Davis. It also benefited from the helpful comments
of Gary Becker, David Greenberg, Bob Hagedorn, Ross Matsueda, George Bridges, and Larry
Cohen. Direct correspondence to Bill McCarthy, Sociology, University of California, Davis,
One Shields Avenue, Davis, CA 95616. E-mail: [email protected].
 The University of North Carolina Press
Social Forces, March 2001, 79(3):1035-1059
1036 / Social Forces 79:3, March 2001
is no etiological significance in distinguishing between “crimes that are fully
executed and those that are not.” Thus, we know a considerable amount about
conditions that promote prosperity in the legal economy but little about those that
increase illegal success.
We do know however, that offenders vary considerably in their illegal accomplishments. According to U.S. Uniform Crime Reports, property crime generated
about $15 billion in 1998. Estimates indicate that, on average, purse-snatching
netted around $140, picking a pocket earned about $400 and residential burglary
provided approximately $1,400 worth of goods (U.S. Department of Justice 1999).
These amounts may be inflated and may be better indicators of a victim’s costs rather
than a perpetrator’s earnings; nonetheless, these figures suggest that some offenders
profit more than others do.
Several ethnographies (Bourgois 1995; Fleisher 1998; Maher 1997; Mieczkowski
1986; Sullivan 1989; Williams 1989), a handful of surveys (Reuter, MacCoun &
Murphy 1990; Tremblay & Morselli 2000; Viscusi 1986a) and analyses of aggregate
level data (Cobb 1973; Krohm 1973) offer further evidence of variation in illegal
success. These studies indicate that many offenders earn appreciable incomes, particularly when they combine legal and illegal work (see Table 1; Fagan & Freeman
1999); yet, others work long hours and make little profit pursuing criminal careers
characterized by ineptitude and failure (Levitt & Venkatesh 1998; Wilson &
Abrahamse 1992). Although the majority of offenders are able to avoid arrests and
convictions (Dunforth & Elliott 1984; Reynolds 1990), a sizable number are incarcerated, often on several occasions.
In this article we offer an initial exploration of the factors that affect illegal success. Most recent studies on success in conventional activities focus on the effects of
human and social capital; a smaller number have considered the role of other resources, including an array of traits described as “personal” capital. We argue that
these resources influence success in criminal, as well as conventional enterprises.
Specifically, we propose that illegal prosperity is influenced by human and social
capital, particularly their criminal forms, and by several personal capital attributes
including utility maximization, risk-disposition, cooperation and competence.
Although success has several dimensions and definitions (Bellah et al. 1985), we
explore only one component: income. We limit our investigation for two reasons:
there is broad cultural agreement that income (or wealth) is a fundamental component of success and financial resources are a particularly potent measure of success
among offenders (e.g., Adler 1993; Jacobs 1999; Shaw and McKay 1969; Williams
1989).2 We further focus our analysis on the “supply side” of illegal income, exploring the effects of capital on success with data on the drug selling incomes of street
youth.3 Our analysis is not a study of drug dealing or street youth offending per se; rather,
we use this street-based activity as one example of income-generating crime. The
background of street youth and the characteristics of street life undoubtedly influ-
Capital, Competence and Criminal Success / 1037
TABLE 1: Suggestions of Variation in Criminal Success
Study
Participants
Illegal Income
Viscusi (1986a, 1986b)
black male youth (1624) in Boston, Chicago
and Philadelphia
youth who engaged in a variety of
crimes had higher yearly earnings than
non-criminals, with a gap of over
$1,000 from illegal activities
Freeman (1994, 1996)
Boston youth
average monthly earnings of $250 for
occasional offenders and $448 for
those who worked on a weekly basis;
about $10 an hour versus $7.50 ($5.60
take home wage) for legally employed
youth
Reuter, MacCoun and
Murphy (1990)
Washington DC drugdealers
the median after-cost or net income of
$2000 per month, compared with a
$800 from licit work
Fagan (1990)
people in two
impoverished areas of
New York City
drug selling income was consistently
higher than non-drug income (i.e.,
from employment or assistance)
Study
Participants
Percent of Offenders Arrested/Convicted
Dunforth &Elliott (1984)
(National Youth Survey)
3 birth cohorts of 1117 year old U.S. youth
24% (of those who committed 3 or
more UCR part 1 offenses in 2
consecutive years)
Farrington (1994)a
London working
working-class males
60% (property offenders convicted
before age 20; 14% only once)
Horney & Marshall (1993)a
Nebraska inmates
52% (active drug sellers charged with
selling in a three year period; 40%
charged only once)
a Estimates calculated by the authors using data obtained from ICPSR
ence successful drug selling, but we argue that many of the factors that affect illegal
success are not specific to a particular crime or type of offender.
1038 / Social Forces 79:3, March 2001
Capital and Illegal Success
Human capital theory suggests that prosperity is profoundly influenced by returns
to investments in education and training (Becker 1996). These investments have
a transformative capacity analogous to that of tools, machinery or money. Thus,
education and training can transform individual aptitudes, helping people to
develop marketable skills and expertise and improving their prospects for success.
According to Becker (1996:146), one of the most important contributions of human
capital analysis is its distinction between general and specific skills and the
recognition of the greater return specialization can provide.
Several writers — most notably Bordieu and Coleman — suggest that social capital
also contributes to success. Coleman (1994:170) describes social capital as “any
aspect of informal social organization that constitutes a productive resource for one
or more actors.” Thus, associations generate social capital when they provide information and create opportunities and obligations that allow people to achieve their
interests. Social capital is both an individual or private asset (Paxton 1999), as well
as a group-level or community good (Sampson, Morenoff & Earls 1999): people use
their associations as resources to achieve desired outcomes and they profit from the
accumulated social capital of their community. Coleman (1990) emphasizes the
social capital that arises in the closeness, stability, circumscription and reciprocity of
social relations found in families, intimate friendships and informal social organizations, whereas others (e.g., Granovetter 1985) draw attention to larger, less intimate associations and their potential for providing new information and connections. Notwithstanding this difference, social capital theorists agree that the potential
for social capital typically increases with the size of one’s network, and that the
probability of prosperity rises with social capital.
Human and social capital undoubtedly play some role in illegal success. However, several scholars suggest that conventional forms of capital are less consequential for illegal prosperity (Grogger 1998; Tremblay & Morselli 2000; Uggen & Thompson 1999). Extending Hagan and McCarthy’s (1997; also see McCarthy & Hagan
1995) work, they suggest that criminal social capital — arises from associations
with skilled offenders — and its human counterpart — specialized skills and knowledge about offending — likely contribute more to illegal success than does conventional capital (e.g., ties to family and years of education).
The recognition that legal and illegal prosperity may arise from different manifestations of human and social capital has encouraged scholars to explore factors
that uniquely enhance these two outcomes; yet, it is possible that licit and illicit
achievements share common antecedents. We suggest that some aspects of personal
capital operate this way, having similar effects on success in either endeavor. According to Caspi et al. (1997) personal capital is the psychological complement to human and social capital; it includes attitudes, tastes or preferences often evident in
Capital, Competence and Criminal Success / 1039
“behavioral characteristics and resources” (428). Others offer different definitions
(e.g., see Becker 1996:4; Nagin & Paternoster 1994:586) but there is general agreement that personal capital refers to attitudes, preferences and personal characteristics that are potential resources for securing desired outcomes. Personal capital has
several dimensions and may occur in conventional as well as criminal forms: for
example, a preference for using rhetoric versus one favoring violence to achieve
desired ends. We focus on four personal capital variables specified in research on
conventional success: the desire for wealth, risk-taking propensity, a willingness to
cooperate and, most important, competence.
Drawing on classical theory, human and social capital theories assume that people’s
actions reflect their stable preferences, instrumental rationality and the desire to
maximize their welfare. Although these theories acknowledge other attributes
(e.g., Becker 1996; Coleman 1990), they privilege the aforementioned characteristics as the engines of economic action, typically treating them as if they were equally
distributed across the population or leaving them unmeasured in their models. We
assume that these characteristics vary and should be measured. We also argue that
people with a heightened sense of instrumentality and a greater desire to improve their
well-being, direct more time and energy toward productive activities and are thus more
likely to be successful.
Economists dissatisfied with the limits of classical theory assumptions draw attention to the effects of other factors. For example, Knight ([1921] 1964:238-42)
notes that people vary in their responses to the risk (measurable) and uncertainty
(unmeasurable) that accompany action: “some individuals want to be sure and will
hardly ‘take chances at all,’ while others . . . seem to prefer rather than shun uncertainty.” von Neumann and Morgenstern’s (1944) expected utility theory makes a
similar point, suggesting that some people are risk seekers, others are risk neutral
and some risk averse. Thus, comfort with risk and uncertainty should influence
economic action independently of instrumental rationality.4 Others have suggested
that attitudes toward risk also influence the decision to commit a crime (e.g., Becker
1968; Bueno de Mesquita & Cohen 1995; Ehrlich 1974). Fairlie (1999) extends this
work, arguing that a willingness to take risks, together with a sense of autonomy and
other entrepreneurial attributes contribute to successful offending. Extrapolating
from these works, we propose that entrepreneurial characteristics, particularly attitudes toward risk-taking, likely contribute to differences in prosperity in illicit as
well as licit endeavors.5
A third form of personal capital, a willingness to work cooperatively, also contributes to success (see Woolcock 1998:159). Cooperation involves the mutual recognition of the benefits of working together; yet, people vary in their willingness to
cooperate and those open to the greatest variety of relationships maximize their
working opportunities (i.e., they are willing to collaborate, to act as equals, and
super- and subordinates). Research suggests that cooperation frequently enhances
1040 / Social Forces 79:3, March 2001
success in conventional settings (Argyle 1991:126-31). Drawing on this literature,
McCarthy, Hagan and Cohen (1998) argue that criminal co-offending is facilitated
by offenders’ recognition that a desired outcome necessitates working with others;
those most open to collaboration will therefore, have the greatest number of offending opportunities. We suggest that a willingness to collaborate may help explain why
some people profit more from their associations with offenders: collaborative people
are more purposively engaged in using their connections with others to facilitate
outcomes. They are more active, and perhaps more effective in transforming their
network resources into capital, and thus should experience greater success.
Although the aforementioned factors may contribute significantly to achievement, success in most endeavors also requires competence. In some contexts, competence refers to a particular set of skills (e.g., a competent piano player); however, as
Clausen’s (1991) work on “planful competence” demonstrates, it also has a broader
form and may constitute a key dimension of personal capital. According to Clausen,
planful or non-task specific competence consists of a cluster of attributes that include intellectual capacity, dependability and self-confidence or efficacy. Noting
that adolescents demonstrate considerable variation in their rationality, Clausen
suggests that the most competent believe in their abilities and do not feel victimized
by life. They have a greater sense of their own agency and ability to influence their
future. These youth are also resilient, persisting in the face of adversity. Clausen does
not argue that competent youth are hyper-rational teenagers who assiduously plot
and strategize about their future; rather, he suggests that competent youth begin to
think about and plan their futures, helping produce “their own development”
(1991:810).
Using data on youth who lived during the Great Depression, Clausen finds that
competence is reasonably stable across the life course and has a strong effect on occupational achievement; an effect that is independent of educational attainment. This finding suggests that schooling was not the only avenue for success for these youth;
instead, the most competent youth were able to take advantage of the economic
prosperity of the 1940s (and subsequent decades) in spite of their level of education.
Thus, conventional human capital, such as years of schooling, may be less salient in
particular historical contexts.
Clausen’s analysis also reveals that competence’s effect on occupational attainment is pronounced for males, but nonsignificant for females. These interactive
effects reflect the ways in which historic circumstances and institutional structures
limit particular youth — in this case women’s — opportunities in the legal economy.
Noting social conditions’ power to compromise competence, Clausen (1991: 837)
concludes that, “some events and circumstances may be overwhelming . . . [and]
some of the highly competent will fall by the wayside.”
Clausen’s research — indeed, most work on competence — focuses on its consequences for conventional outcomes: success in work and marriage, for example.
Capital, Competence and Criminal Success / 1041
Yet, under certain conditions competence may also influence illegal success. Consider youth that experienced the economic downturn and other social problems of
the 1980s and 90s. In these years access to conventional human and social capital
diminished as school funding shrunk and the job market narrowed, limiting competent youth’s opportunities to succeed in the legal economy (Freeman 1996). However, many disadvantaged youth also encountered heightened exposure to crime as
the drug-trade flourished. Urban ethnographers have detailed the varying successes
of drug sellers, describing the economic despair of some youth and the prosperity of
others (e.g., Sullivan 1989; Williams 1989). Anderson’s (1999: 291) description of
one financially successful youth emphasizes the different paths that lead to these
different outcomes: this youth “was always intelligent and motivated — this is what
made him an upcoming leader in the drug trade.”
Anderson’s account, and the findings from other ethnographies suggest that competent youth may have been best able to take advantage of the illegal resources
available in their communities. Although competence likely contributes more to
legal than illegal behavior, it may be directed more remuneratively into illegal pursuits than legal ones in situations where conventional resources are limited and
criminal ones flourish. In these contexts, competence may give direction to, and
intensify the effects of factors that contribute to successful offending. Thus,
competence’s effect on illegal income is likely interactive, rather than independent,
and it is competent youth who may be best able to capitalize on available illegal
resources.
In sum, whereas conventional human and social capital figure prominently in
legal prosperity, illegal success is more likely to be influenced by human and social
criminal capital; by the acquisition of specialized knowledge and skills suited to
offending and by associations with offenders who can provide access to information
and training. Personal social capital is less specific in its consequences. People with
valuable personal capital — those who are competent, want to improve their material conditions, and are willing to take risks and to cooperate with others—are most
likely to be successful in their endeavors, legal or otherwise; however, limited conventional resources combined with greater criminal ones may encourage these people
to redirect their energies toward illegal activities and to be more successful at them.
Previous Research
Several studies of illegal earnings explicitly or implicitly consider the contributions
of human capital; a smaller number examine the consequences of social capital,
and a few consider the effects of some personal capital attributes. There are no
studies however, that explore the combined effects of the types of capital discussed
1042 / Social Forces 79:3, March 2001
above; previous studies also vary considerably in their samples, measurements,
methods and in some cases, their findings.
Research on human capital’s effects on illegal earnings vary: studies report a
negative nonsignificant effect of education (Uggen & Thompson 1999; Viscusi
1986a), a positive nonsignificant one (Matsueda et al. 1992), a significant negative
one (Fagin 1992; 1994) and a significant positive effect (Pezzin 1995) but only for
certain groups (e.g., criminal novices). Studies on the effects of legal employment
and employment income also report divergent results: some find a significant negative effect (Uggen & Thompson 1999; Viscusi 1986a;), whereas others report a positive association (Reuter et al. 1990; Tremblay & Morselli 2000).
Research on the effects of criminal human and social capital is more consistent
but often uses weaker measures; it reports that illegal income is enhanced by previous offending, prior arrests, convictions, or probation (Matsueda et al. 1992; Viscusi
1986a; however, see Uggen & Thompson 1999). There is also some evidence of the
effect of specialization. Tremblay and Morselli (2000) find that earnings increase for
those who specialized in drug dealing and Fagin (1994) reports that seller’s total
income increased with the proportion of their income that was from drug-sales. In
contrast, Reuter et al. (1990) find that the most successful sellers sold more than
others, but also committed nondrug crimes. Indirect evidence of criminal social
capital effects include higher illegal earnings for individuals who belong to gangs
(Viscusi 1986a), have a best friend who is deviant (Uggen & Thompson 1999) or have
connections with a number of offenders (Tremblay & Morselli 2000).
Two studies offer support for our interest in two personal capital attributes: willingness to take risks and to cooperate. Viscusi (1986b) reports that risk-tolerant
youth earned higher illegal earnings than risk-adverse ones and Reuter et al. (1990)
find that drug dealers who sell for others, as well as work on their own, earn the most,
whereas solo-workers make the lowest incomes.
Previous research also emphasizes the role of other factors, specifically, race,
gender, drug use, criminal opportunities and a willingness to use violence (Bourgois
1995; Fagin 1994; Matsueda et al. 1982; Pezzin 1995; Reuter et al. 1990; Uggen &
Thompson 1999; Viscusi 1986a). In sum, several studies offer useful insights about
conditions that influence illegal earnings. We build on this research, using our
conceptual model to explore systematically the combined effects of human, social
and personal capital.
Data, Variables and Analytic Strategy
The data used in this analysis were collected in a panel study of “street” or homeless
youth in Toronto and Vancouver, Canada (Hagan & McCarthy 1997). Building on
connections established in a previous study, a team of researchers contacted
respondents in service agencies (e.g., shelters and counseling agencies) and street
Capital, Competence and Criminal Success / 1043
locations (e.g., parks and street corners) in the spring of 1992. The sample was
restricted to those aged 24 or younger who did not have a permanent residence. It
included those who lived on the street, in shelters, in temporary housing with friends
or in hotels. This diverse group of runaways, throwaways, and abandoned youth
completed self-report questionnaires and were interviewed on two subsequent
occasions at approximately one-month intervals. Of the 482 youth who took part
in the first wave of the study, 78% completed a second-wave interview and 53%
reported for a third one; in this article we use data from the first two waves.7
In the second wave of the study respondents answered a series of questions about
their legal and illegal activities on each of the twelve days prior to the interview
(e.g., Did you work in a regular job/ sell marijuana yesterday? Two days ago?” etc.).
They also estimated how much money they earned in these activities. We construct
a second wave daily drug-selling income measure based on reports of after-costs
earnings for two activities (selling marijuana and other drugs) divided by the number of days worked (see Table 2). This daily measure should minimize recall error
that may be more consequential for the monthly estimates used in other studies
(e.g., Wilson & Abrahamse 1992).8
Our analysis includes three sets of independent variables that, unless noted, were
measured prior to the second wave. The first includes controls for gender, age, race,
the city in which the respondent was interviewed, street adversity, criminal opportunities and four indicators of the social and human dimensions of the respondent’s
criminal capital: drug-selling network while living at home and on the street, drugselling experiences at home, exposure to tutelage in drug selling since coming to the
street, and belief in the legitimacy of using drugs. Including a measure of prior
drug-selling has a further advantage in that it should help to control for unobserved
individual heterogeneity. We considered several other factors typically associated
with offending — age as a quadratic variable, self-control (i.e., childhood aggression
against parents); familial relationships; physical and sexual abuse; and parental
education, criminality and substance use — but none of these had significant multivariate effects and so we did not pursue them.
Our second set of variables introduces proxy measures of human and social
capital, as well as additional controls noted in earlier research. Research often obscures the lines between social and human capital and between social relationships
and the resources those associations offer. Some of this slippage reflects theoretical
obscurity, but it also results from the elasticity that characterizes different forms of
capital (e.g., as Caspi et al. 1998:426 note, IQ can be considered both human and
personal capital). We use three measures of the respondents’ possibilities for acquiring conventional human capital: years of education, second wave legal employment
status and a second wave estimate of the number of street friends legally employed.
We do not believe that these variables will have sizable effects on illegal earnings (see
Ehrlich 1974); we include them because of their theoretical importance and the
inconsistency of their effects in previous research.
1044 / Social Forces 79:3, March 2001
TABLE 2: Concepts, Indicators and Descriptive Statistics for Street Youth
Sample
X
S.D.
Sample Selection, Probit Equation of First Wave Drug Selling
Gender (1W)*
0 = female, 1 = male
Age (1W)
In years (sample restricted to people 24 and under) 19.802
2.509
Race (1W)
0 = white, 1 = nonwhite
.238
.426
City (1W)
0 = Toronto, 1 = Vancouver
.317
.466
Illegal opportunities
(1W)
How often do you have a chance to make money
illegally? 0 = no chance, 1= less than 1 a month,
2 = few times a month, 3 = few times a week,
4 = few times a day
2.794
1.488
2 item scale (r = .241): percent of home and street
friends who sell drugs. 0 = 0, 1 = 1 to 10 . . .10 = 90
1.739
to 100
1.625
2 items: frequency of selling marijuana and other
drugs before leaving home. 0 = 0, 1 = 1, 2 = 2,
3 = 3-4, 4 = 5-9,5 = 10-19, 6 = 20-29, 7 = 30-59,
8 = 60 or more
1.558
3.640
3 item scale (a = .678): frequency of going an entire
day without food, sleeping in buildings or cars,
spending an entire night on the street. 0 = never,
1 = once or twice, 2 = a few times, 3 = often,
5.045
4 = a lot of the time
2.981
3 items (a = .835): received offers of instruction,
assistance or protection in drug selling. (see street
adversity)
4.52
3.732
It is should be legal to take drugs. 0 = strongly
disagree, 1= disagree, 2 = uncertain, 3 = agree,
4 = strongly agree
2.250
1.368
2 item scale (r = .371): sold marijuana, other drugs.
0 = no, 1 = yes
.579
.494
Home and street drug
selling network (1W)
Home drug selling
experience (1W)
Street adversity (1W)
Tutelage in drug selling
(1W)
Believe drug use is
legitimate (1W)
Sold drugs on street
(1W)*
Tobit Regression Equation of Second Wave Daily Income
Drug use (2W)
3 item scale (a = .400): number of days used
marijuana, acid, or coke.a
Use of violence (2W)
3 item scale (a = .701): number of days used a
weapon, attacked someone, committed an
aggravated assault.a
.677
.468
3.800
5.735
.317
1.046
Capital, Competence and Criminal Success / 1045
TABLE 2: Concepts, Indicators and Descriptive Statistics for Street Youth
Sample (Continued)
Arrest (2W)
0 = not arrested, 1 = arrested at least oncea
X
S.D.
.108
.311
Conventional Human and Social Capital
Education (1W)
Highest grade achieved. 13 = college or university
9.160
2.063
Employment (2W)
0 = unemployed, 1= employeda
.206
.405
Legal employment
network (2W)
Number of street friends employed.a
0= 0, 1= 1 to 10 . . .10= 90 to 100
1.260
1.024
Criminal Human and Social Capital
Specialization (2W)
0 = no offending, or committed theft and/or sextrade crimes, 1= sold drugs exclusivelya
.098
.298
1.835
1.625
Desire for wealth (2W) I want to make lots of money. 0 = strongly disagree,
1= disagree, 2 = uncertain, 3 = agree, 4= strongly
agree
3.317
.999
Risk preferences (2W) I like to chances. (see desire for wealth)
2.798
.849
Collaboration (2W)
.142
.349
7 item scale (a = .617): Average grade. 1 = 0 to 40,
2 = 41 to 50, 3 = 51 to 60, 4 = 61 to 70, 5 = 71 to
80, 8 = 81+; Trouble with teachers, Difficulty
understanding school work. 1 = always, 2 = often,
3 = sometimes, 4 = rarely, 5 = never; I am responsible for my failures, I can do just about anything I
set my mind to, I am responsible for my own
successes, My misfortunes are the results of mistakes
I have made. 1 = strongly disagree, 2 = disagree,
3 = uncertain, 4 = agree, 5 = strongly agree
26.288
4.226
a
Drug selling network number of street friends who sell drugs.
0
=
0,
1
=
1
to
10
.
.
.10
=
90
to
100
(2W)
Personal Capital
Competence (1W)
Daily drug-selling
income (2W)
(n = 480)
0 = noncollaborative, 1= open to collaborationa
Income after costs from selling marijuana, selling
other drugs/Number of days sold each drug (raw
score)†
11.736 43.848
* Notation refers to when the variable was measured, 1W = first wave, 2W = second wave
a Refers to activities for the twelve day period preceding the second interview
1046 / Social Forces 79:3, March 2001
We are more optimistic about the effects of our second wave measures of
criminal capital. Our measure of criminal human capital, specialization, is a
dichotomous variable that separates those who worked exclusively in drug selling
from those who either earned illegal earnings from an array of crimes or reported
no illegal income. Specialization in drug selling is widely assumed to be one of
the most lucrative sources of illegal profit on the street.9 We use a proxy measure
for criminal social capital, the number of second wave street friends involved in
drug selling. We also introduce second wave measures of arrests, drug use and a
willingness to use violence; although these could be construed as indicators of
human and social criminal capital, we treat them as control variables.
Our last group of variables concerns our four measures of personal capital. Our
first two items were collected during the second wave interview and asked respondents about their desire to “make lots of money” and enjoyment of “taking chances.”
Both measures have been used in other studies of crime, but in ways differing from
our own (e.g., see Farnworth & Leiber 1989; Hagan, Gillis & Simpson 1985). In this
work we assume the first question expresses the extent to which respondents value
maximizing their utility, whereas the second assesses one entrepreneurial trait, risktaking propensity.
Our third item, collaboration, measures the respondents’ willingness to work
with others, thereby utilizing their relations as social capital. This second wave,
dichotomous measure is adapted from McCarthy et al. (1998) and distinguishes
respondents who received more offers of drug-selling help (offers of instruction,
assistance or physical protection) than the average in our sample, and who were
above average in their willingness to offer help to others.10 We designate respondents
as open to collaboration if they scored higher than the sample means for both items.
We use Clausen’s work to guide the construction of our remaining independent
variable, competence. Clausen (1991:811) argues that competence, and specifically
adolescent “planful competence,” consists of three components of personality he
labels dependability, intellectual investment and self-confidence. Dependability
refers to being responsible, accomplishing tasks, and disinclination to rebellion;
intellectual investment incorporates intellectual ability and a valuing of intellectual matters; and self-confidence involves feeling satisfied with oneself and life’s
possibilities. Clausen maintains that his measure (judges’ assessment of people’s
personality based on the California Q-sort’s 100 descriptive terms) captures the key
elements of competence, however, he (1991: 837) suggests that subsequent research
should explore alternatives.
We measure competence with first wave reports of behavior and attitudes. We use
a question about problems with teachers as a measure of dependability (assuming
that such problems reflect a tendency to neglect school tasks and to rebel). To this we
add two questions about school performance (i.e., grades and ability to understand
school material) as indicators of intellectual ability. We also use four Likert-style
Capital, Competence and Criminal Success / 1047
indicators of self-confidence (i.e., feelings of responsibility for personal success and
failures, and the ability to achieve desired outcomes). Reliability analyses indicate
that, consistent with Clausen’s hypothesis, these measures are correlated (a = .617);
yet, as we expected competence is not associated with years of education for the
youth in our sample (r = -.002; b = -.003, S.E. = .025 with age controlled), most of
whom have not finished high school. There is also little collinearity among the other
independent variables in our model: the largest square root of a variance inflation
factor score is 1.5 (Fox 1991).
Our sample, measures and interests present several analytical challenges that we
address in the following ways. In the first stage of our analysis we use a sampleselection correction model because second-wave drug sellers may represent a nonrandom subsample of youth whose selling activities reflect backgrounds, opportunities and experiences (particularly crime-inducing ones and prior criminal activities) that may differentiate them from other youth (Pezzin 1995). Specifically, we
use a probit model to estimate the effects of home and first-wave characteristics on a
dichotomous measure of first-wave drug selling for the entire sample. Problems
associated with sample-selection estimators (Winship & Mare 1992) led us to explore several models. The most efficient model uses the variables described earlier.
We enter the probit results into maximum likelihood tobit regression analyses of
second-wave drug-selling income (see Matsueda et al. 1992). We use tobit models
because many youth may have had their incomes censored at zero. For example,
youth that did not report any drug-selling income at the second wave might have had
insufficient opportunities or resources to work during the short time period (twelve
days) that constitutes our second-wave income measure. Most sellers do not work
everyday — on average the sellers in our sample worked only five of twelve days —
thus some potential sellers may have had the measurement of their incomes censored. Censored data can produce a downward bias on least squares estimates and so
we use maximum likelihood estimates (Maddala 1983). We exclude three variables
used in the probit model to ensure that the identification of our tobit models is not
simply a statistical artifact: city, street adversity and criminal opportunities. We see
no reason why these variables should have an effect on second-wave income and our
data are consistent with this assumption.
Finally, we focus on the natural logarithm of our measure of daily illegal income.
We use logged data for several reasons: analyses of income typically find earnings
follow a logarithmic functional form; residual plots and Cook-Weisberg tests indicate considerable heteroskedasticity in a model that uses our raw data; and using
logged income increases the accuracy of estimated standard errors. Nonetheless,
given concerns about the use of raw versus transformed measures of income (Portes
& Zhou 1996), we also present results for unlogged daily income. We follow recent
earnings research and place the amount of time worked on the left-hand side of the
equation (Portes & Zhou 1996); however, we present a further analysis of total in-
1048 / Social Forces 79:3, March 2001
come to examine the independent effect of the number of days worked and to
explore how days worked influences the effects of the other variables in our model.
Results
The information in Table 2 indicates that, in this sample, males outnumbered
females at a rate of about three to one, the majority of respondents were interviewed
in Toronto and most youth routinely experienced the adversity common among
the homeless (Hagan & McCarthy 1997). The average respondent was between 19
and 20 years old and just under one-quarter were from a racial minority (about
6% were black, and 15% were aboriginal). More than half (58%) of the youth
surveyed had sold drugs on at least one occasion since they left home and prior to
their first-wave interview. The home and first-wave factors suggest the youth had
considerable exposure to drug selling: on average they had twelve drug-selling
friends, had received several offers of tutelage, had opportunities to make money
illegally more than a few times a month, did not think drug use should be illegal
and had sold drugs once or twice. In short, most youth in the sample had
accumulated some criminal capital.
In the twelve days prior to their interview, youth who completed a second-wave
survey had, on average, used drugs on at least three days; few youth used violence or
were arrested. The average respondent desired financial success, was favorably disposed toward taking risks and had about seven employed friends and thirteen drugselling ones. About 10% of the sample specialized in drug selling during the second
period and 14% were open to collaboration. Neither Toronto nor Vancouver had
large-scale crack problems in the early 1990s (Cheung & Erickson 1997) and threequarters of the youth we studied sold marijuana and just over one-half sold other
drugs (LSD was the most common, followed by cocaine, speed and crack). The
average daily illegal income was about $101 for those actively involved in the drug
trade, compared with $37 a day — before taxes and deductions — for those who did
legal work (the daily means for the entire second wave sample are $12 and $7 respectively).
Estimates from the first stage of our sample-selection bias analysis are presented
in Table 3. The results indicate that neither age, gender nor race significantly
influence drug selling when more proximate measures are included in the
equation. Instead, the likelihood of drug selling increases with exposure to adverse
conditions, the size of one’s drug-selling network, tutelage in drug selling, previous
selling experience, the belief that drug use is legitimate and opportunities to make
money illegally. City also plays an important role indicating that compared to
Toronto street youth, those in Vancouver reported greater involvement in drug
selling.
Capital, Competence and Criminal Success / 1049
TABLE 3: Maximum Likelihood Probit of First Wave Drug Selling
b
S.E.
Age
Gender
Race
City
Home and street drug selling network
Home drug selling experience
Street adversity
Tutelage in drug selling
Illegal opportunities
Believe drug use is legitimate
Intercept
-.053
.168
.198
.581**
.145**
.107**
.068**
.058**
.199**
.160**
-2.080
.029
.158
.164
.160
.052
.028
.024
.015
.050
.051
x2
df
Percentage of cases correctly predicted
190.512
10**
77
(n = 480)
** p ≤ .01 (two-tailed)
We present our second stage analysis in Table 4 and include maximum likelihood tobit coefficients for three outcome variables: logged daily income (equation 4.1), raw score daily income (equation 4.2) and logged total income (equation 4.3). Our findings are rather consistent: four variables have sizable and significant effects regardless of the form of the dependent variable, one variable has significant effects in two models and the effects of the remaining variables are systematically insignificant. Equation 4.1 indicates that none of the conventional human
capital variables significantly influence logged illegal earnings. In contrast, logged
daily profits increase with the criminal human capital acquired through specialization (b = 2.441, p ≤ .01). Three other variables have significant bivariate effects (in
a tobit model with sample selection effects) but not multivariate ones: the use of
violence (b = .360, S.E. = .153, p ≤ .05), being arrested (b = 1.519, S.E. = .860,
p ≤ .10) and our second wave measure of criminal social capital on the street, the
respondent’s drug-selling network (b = .018, S.E. = .010 p ≤ .10).
Consistent with our hypothesis, two of three measures of personal capital have
significant direct effects; illegal earnings increase with a desire for wealth (b = .333,
p ≤ .05) and are greater for those willing to collaborate (b = 1.061, p ≤ .01). These
effects affirm the importance of utility maximization and the role of agency in
transforming peer relationships into criminally productive capital. Two additional
variables, minority racial status (b = .762, p ≤ .05) and drug use (b = .063, p ≤ .01)
also have significant effects.
1050 / Social Forces 79:3, March 2001
TABLE 4: Maximum Likelihood Tobit Regression of Drug-selling Income
Equation 4.1
Logged Daily Income
Variable
B
Age 1W
-.025
Gender 1W
.002
Race 1W
.762*
Home and street drug selling
network 1W
.020
Home drug selling experience 1W .010
Tutelage in drug selling 1W
.021
Believe drug use is
legitimate 1W
.211
Education 1W
.017
Legal employment network 2W
-.053
Legal employment 2W
-.170
Specialization 2W
2.441**
Drug selling network 2W
.043
Drug use 2W
.063**
Use of violence 2W
.120
Arrested 2W
.283
Desire for wealth 2W
.333*
Preference for risks 2W
.039
Collaboration 2W
1.061**
Competence 1W
.013
Number of days sold 2W
—
Intercept
-5.110
Rho
.413
-2 x Log likelihood
781.858
S.E.
Equation 4.2
Equation 4.3
Daily Income Logged Total Income
B
S.E.
B
S.E.
.059
.359
.387
2.67
5.51
73.41*
4.64
30.02
29.09
-.047
-.059
.795†
.072
.420
.478
.108
.034
.060
-3.64
1.14
.99
8.53
3.23
5.14
.015
-.018
.012
.124
.038
.070
12.44
6.02
14.68
28.20
27.08
2.11
2.02
9.64
28.87
13.57
13.32
26.28
2.12
—
.194
-.042
.032
.106
2.033**
.033
.045
.032
.403
.452**
.075
.956**
.012
.230**
-4.379
.467
775.944
.202
.094
.195
.430
.466
.129
.029
.113
.393
.148
.217
.367
.030
.034
.156
.074
.168
.361
.498
.115
.024
.120
.366
.168
.150
.340
.024
—
12.29
1.63
-1.52
-12.84
178.84**
4.39
3.91†
11.40
5.21
25.71†
-1.58
79.44**
1.56
—
-447.672
.320
1326.726
(n = 480)
† p ≤ .10
* p ≤ .05
** p ≤ .01 (two-tailed)
Our analysis of unlogged data (see equation 4.2) provides further details of the
financial implications of our results. These results suggest that racial-minority youth
earn about $73 more than their nonminority counterparts and that profits increase
by about $4 for each day drugs are used. Each unit increase in the measure of desire
for wealth adds about $26 to earnings, implying those who have the greatest desire
(i.e., four on this variable) can earn about $100 more than youth with the least
interest (i.e., zero on this variable). Youth open to collaboration increased their
profit by about $79 and those who specialize earned $179 more than nonspecialists.
Although there is considerable heteroskedasticity in these data, the similarities be-
Capital, Competence and Criminal Success / 1051
TABLE 5: Maximum Likelihood Multiplicative Analysis of Drug-Selling
Income
Logged Daily Income
b
Competence x collaborationa
Competence x specialization
Competence x preference for risk
Education x collaboration
Education x specialization
Education x preference for risk
S.E.
.108†
.159*
.071*
.063
.073
.036
.193
.225
-.149**
.141
.159
.074
(n = 480)
a
All models correct for sample-selection bias and include the variables listed in Table 4
† p ≤ .10
* p ≤ .05
** p ≤ .01 (two-tailed)
tween the unlogged and logged data results suggest that the standard errors for equation 4.2 are not seriously distorted (Fox 1991).
Our analysis of total logged income (equation 4.3) clarifies the role of the frequency of selling on the effects described above. Controlling for the amount of time
spent working reduces the effect of specialization and collaboration by about15 and
10% respectively (i.e., from b = 2.441 to b = 2.033, and b = 1.061 to b = .956) but
increases the effect of desire for wealth by about 35% (from b = .332 to b = .452).
Thus, although the number of days worked clearly influences the consequences of
specialization, collaboration and desire for wealth, all have sizable, independent
effects on illegal earnings.
As noted earlier, we expected competence would not directly influence illegal
income but would intensify the effects of other variables. In Table 5 we present the
results of adding interaction terms to the logged daily income. These results are
displayed in Figure 1. As expected, collaborators’ (b = .108, p ≤ .10) and specialists’
(b = .159, p < .05) earnings increase with competence. Furthermore, the incomes
of youth who have an average or above-average score on risk-taking propensity not
only rise with competence (b = .071, p ≤ .05), they surpass those of specialists (see
Figure 1). These risk-disposed youth that invest their competence in dealing drugs
apparently gain the most from their illegal sales. These effects contrast sharply with
those involving conventional human capital. For example, no variables
significantly interact with education to increase illegal earnings; indeed, high levels
of education and risk preferences significantly diminish drug-selling income
(b = -.149, p < .05).11
1052 / Social Forces 79:3, March 2001
FIGURE 1:
Interaction Effects on Competency, Collaboration, Specialization
and Risk
Capital, Competence and Criminal Success / 1053
FIGURE 1:
Interaction Effects on Competency, Collaboration, Specialization
and Risk
Discussion
Most academic interest in individual success focuses on achievements valued by
conventional culture, and there are many studies that explore the attainment of
prestigious jobs, successful businesses and impressive incomes. In contrast, there
has been little interest in analyzing success in activities, such as crime, that involve
less conventional work. Indeed, the notion of illegal prosperity challenges
commonly held beliefs that success requires participation in conventional activities.
Perhaps even more unsettling is the possibility that theories of legal prosperity
also explain illegal success. Our results offer some support for this hypothesis, demonstrating that some of the factors that contribute to profits in more acceptable
endeavors also influence illegal ones. Specifically, one component of human capital, specialization, and two personal capital attributes, a willingness to collaborate
and a desire for wealth, have sizable effects on success in illegal enterprises. A third
contributor to legal prosperity, competence, intensifies and gives direction to two of
these effects, increasing illegal earnings through interactions with specialization
and collaboration.
Competence also interacts with a disposition for risk-taking, another personal
capital attribute that figures in conventional success, particularly for entrepreneurs.
1054 / Social Forces 79:3, March 2001
As expected, these interactions occur in the absence of a main effect of competence,
suggesting competence itself does not lead to crime; rather, competent youth who
have an orientation favoring risk taking, specialization or collaboration may become successful offenders in particular socioeconomic conditions. Our findings
affirm the usefulness of economic theories for studying crime, and they emphasize
underdeveloped connections between individual characteristics and criminal human and social capital. It is these connections that help explain some offenders’
successful pursuit of illegal profits in settings where legal opportunities are limited.
The economic restructuring that occurred in North America in the 1980s and
early 90s undoubtedly encouraged some entrepreneurial youth to take up drug
dealing. Throughout this period declines in inner-city employment opportunities
resulted in substantial increases in unemployment and a drop in wages of 20 to
30%, particularly for males, the young, the under-educated, and racial minorities
(Fagan & Freeman 1999). Several commentators have noted how unemployment
transformed the urban landscape, disrupting inner-city life, exacerbating old sources
of strain and introducing new ones that, among other things, contributed to increased levels of crime. Urban ethnographers (e.g., Anderson 1999; Fleisher 1998;
Sullivan 1989), economists (e.g., Freeman 1996; Grogger 1998), sociologists
(e.g., Wilson 1987) and some criminologists (e.g., Baron & Hartnagel 1997; Fagin
1992) agree that a substantial number of inner-city dwellers reacted to under- and
unemployment by turning to or escalating their involvement in crime as primary
and secondary sources of income (also see Fagan & Freeman 1999).12 As Freeman
(1996:36) concludes, “a collage of evidence supports the notion that young men
respond substantially to the economic returns of crime,” as a way of circumventing
diminished opportunities for legal work. And, as noted by Anderson (1999:134),
youth that turn to drug dealing make their decision, “based in part on what they are
able to do successfully.” Yet, criminologists frequently ignore illegal income, leaving
this phenomenon under-theorized and under-studied. Our work offers the beginnings of a theory of criminal success, but more work must be done.
A century ago Veblen ([1899] 1967) reflected upon the similarities between ordinary delinquents and successful businessmen. Our research highlights some of
these similarities, demonstrating that even among street youth, those who make the
most of the economic potential of crime possess characteristics that may have brought
them success in more conventional employment. Unfortunately, the structural and
personal disadvantages experienced by some youth compromise their formal schooling in spite of their abilities, limit their conventional human capital, narrow their
opportunities for licit work and increase their opportunities for offending. Yet, drug
dealing and other illegal economic pursuits typically represent short-term solutions
that alleviate some problems while exacerbating and initiating others (Maher 1997).
The longer youth work in the illegal economy, the greater their risks of chronic
unemployment, violence, high-levels of drug use, concomitant health problems
and imprisonment. The economic losses that result from youth involvement in the
Capital, Competence and Criminal Success / 1055
illegal economy, coupled with the social and personal costs associated with drugdealing or other criminal pursuits, are a telling indictment of the problems that
currently afflict the organization of economic life in advanced market societies.
Notes
1. Although there are limits to analogies based on legal employment, the notion that
crime shares some similarities with economic activity has established a small niche in
criminology (see Fagan & Freeman 1999) and studies reveal considerable variation in
the orientation of offenders. Some offenders’ pursuits resemble those of wage laborers,
others actions more closely parallel independent entrepreneurs (e.g., Levitt & Venkatesh
1998; Williams 1989) and the activities of a third group contrast dramatically with those
engaged in legal work (Gottfredson & Hirschi 1990).
2. Other dimensions of success include formal and informal recognition of
accomplishments (e.g., from promotions and awards to individual adulation); status
or prestige; self-satisfaction with or confidence about one’s performance or abilities; and
the avoidance of unwanted outcomes (e.g., detection, arrest, and imprisonment). See
Matsueda et al. (1992) for a discussion of prestige in the illegal economy.
3. The limits of a single paper and the data we use necessitate our setting aside important
macro and meso level concerns that operate on the “demand” side of the income equation
(Breiger 1995). Important forces operate at the international, national and community
levels and include official policies; legal and illegal employment opportunities; population
composition; policing; cultural beliefs and practices; physical and human geography; an
industry’s technology, concentration, capital intensity and market share; and the structure,
organization and practices of those involved in the local illegal economy (Adler 1993;
Calavita, Pontell & Tillman. 1997; Manning & Redlinger 1983; Sullivan 1989; Venkatesh
1997). Notwithstanding their general importance, many of these factors are less
consequential for illegal incomes generated over short periods of time in relatively
unorganized settings.
4. Research on entrepreneurial traits offers some support for the centrality of riskpropensity; however, conceptual, measurement and sampling problems trouble research
in this area (Das & Ten 1997; Shaver & Scott 1991).
5. Economists typically treat risk-taking disposition as a stable preference rather than a
type of capital. We disagree and argue that attitudes toward risk are mutable and
influenced by experience, contexts and larger social forces.
6. We are not suggesting that all successful offenders are clever strategists who choose to
specialize or to collaborate because they believe these will increase their illegal earnings.
Some offenders may be so oriented, but many acquire the benefits of collaboration and
specialization without having chosen them in a fashion suggested by the classical utilitarian
perspective; instead, their decisions are influenced by the “richer set of values and
preferences” that affect most behavior (Becker 1996:139).
1056 / Social Forces 79:3, March 2001
7. Our confidence in these data reflects several findings: second-wave respondents differ
significantly from first-wavers on only 12 of 65 variables and the demographic
characteristics of the sample are comparable to youth studied in other street settings
(see Baron & Hartnagel 1997; Hagan & McCarthy 1997).
8. Although some researchers question the validity of offenders’ reports, Maher (1997:222)
notes that “there is little evidence to suggest they [drug dealers] lie or avoid telling the
truth to a greater extent than anyone else.” Indeed, Jacobs (1999:23) reminds us that
several researchers have concluded that drug-dealers provide among the best information
about illegal activities. Tremblay and Morselli’s (2000) reanalysis of Wilson and
Abrahamse’s (1992) data suggests that success does not influence reports of illegal earnings
and Grogger (1999:769) finds that for some offenders, income-based measures may
have greater validity than criminal participation measures. Consistent with other reports
of non-wage income (e.g., self-reported legal income, see Evans & Leighton 1989) illegal
income data are typically estimates. Although they are open to some error, we have no
reason to assume these estimates are systematically distorted and we assume they
meaningfully differentiate respondents’ earnings.
9. Although specialization is less common than offense versatility, we use this variable
because of our theoretical interests; as well, an analysis that excluded specialization would
likely be misspecified.
10. Limits in the data restrict our assessment of respondents’ offers of help to the number
of types of assistance they offered, rather than the frequency of each type; this focus on
types probably makes this a conservative measure of the amount of help offered.
11. Alternative models produce similar results. Tobit results for the 376 youth who
participated in two waves of the study resemble those reported above, except the risk
and competence interaction is not significant and the competence and collaboration effect
is significant only with a one-tailed test. Sixty-five percent of the of the 106 youth who
completed only one survey sold drugs since leaving home and thus the subsample of
376 youth underrepresents drug sellers. Results from an OLS equation for the 278 youth
who sold drugs in the first wave mirror those reported in the text. Five variables, minority
status, desire for wealth, competence, specialization and collaboration have significant
effects in an OLS analysis of the 67 youth who reported drug-selling income in the second
wave (given sample size, we limited this equation to race, drug use, capital and individual
characteristics).
12. Baron and Hartnagel (1997) offer the most direct evidence of this effect, finding, for
example, that street youth who were angry about unemployment but had worked, were
more actively involved in drug selling than other youth.
Capital, Competence and Criminal Success / 1057
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