Posted: December 3rd, 2013
Correlates of Behaviour
CHAPTER 5 Correlates of Criminal Behaviour 137
progressively until age 16 or 17 and then diminished, though this pattern varied
with the nature of the delinquency. Serious delinquency tended to diminish
with increasing age, while drug and status offences increased.
progressively until age 16 or 17 and then diminished, though this pattern varied
with the nature of the delinquency. Serious delinquency tended to diminish
with increasing age, while drug and status offences increased.
Since younger age categories are over-represented among criminal
offenders, it’s quite likely changes over time in the crime rate reflect, at least in
part, changes in the age composition of the total population. Several researchers
have shown that a significant amount of the rise and fall of U.S. crime rates
can be ex lainedb the chan in a e corn osition of the American 0 ulation,
offenders, it’s quite likely changes over time in the crime rate reflect, at least in
part, changes in the age composition of the total population. Several researchers
have shown that a significant amount of the rise and fall of U.S. crime rates
can be ex lainedb the chan in a e corn osition of the American 0 ulation,
P Y 8 8 8 P P P
primarily the aging of the “baby boomers” (Cohen and Land, 1987; Sagi and
Wellford, 1968; Steffensmeier and Harer, 1991; Wellford, 1973). Overall crime
rates rose as this group reached their late teens in the 196os and then fell as in
they began to reach their 3os a decade later (Carrington, 2001). Similarly in
Canada, baby boomers-those born between 1947 and 1966-reached 15 years
of age in the 196os and 197os, a time when violent and property crime rates
were rising. Figure 5.2 shows the trend in overall crime and in the number of
15-to-24-year-olds as rates per 100 000 population. The rate of 15-to-24-year-
aids began dropping in the early 198os. The general decline in crime rates since
he early 199os coincided with a decrease in the proportion of persons aged 15
o 24 during the same time period (Savoie, 2002). Ouimet (2002) examined the
irop in crime in the 199os in both the United States and Canada and attributed
I primarily to these shifts in the age composition of the population, as well
is improved employment opportunities. Carrington (2001) has forecast that 1
all types of crime in Canada should decline to the year 2041 because of the
=lGURE 5.2 Crime Rate and Population Aged 15-24, per 100 000
Population, Canada 1962-2003‘
primarily the aging of the “baby boomers” (Cohen and Land, 1987; Sagi and
Wellford, 1968; Steffensmeier and Harer, 1991; Wellford, 1973). Overall crime
rates rose as this group reached their late teens in the 196os and then fell as in
they began to reach their 3os a decade later (Carrington, 2001). Similarly in
Canada, baby boomers-those born between 1947 and 1966-reached 15 years
of age in the 196os and 197os, a time when violent and property crime rates
were rising. Figure 5.2 shows the trend in overall crime and in the number of
15-to-24-year-olds as rates per 100 000 population. The rate of 15-to-24-year-
aids began dropping in the early 198os. The general decline in crime rates since
he early 199os coincided with a decrease in the proportion of persons aged 15
o 24 during the same time period (Savoie, 2002). Ouimet (2002) examined the
irop in crime in the 199os in both the United States and Canada and attributed
I primarily to these shifts in the age composition of the population, as well
is improved employment opportunities. Carrington (2001) has forecast that 1
all types of crime in Canada should decline to the year 2041 because of the
=lGURE 5.2 Crime Rate and Population Aged 15-24, per 100 000
Population, Canada 1962-2003‘
25000
20000 ‘)1 C . . 9 _ ‘ C.) l‘
15000 – Cd ‘ V) C . »’w . » C l“ll
‘E, . g
10 000 . 1 ‘ _ 0 1 A V ,s,_ g_
5000 _ ‘ C 1 1 ,0 2
0 l . . . I. . . ’ ‘I l. 1 i
1962 1966 1970 1974 1978 1982 1986 1990 1994 1998 2002
Year
Population 15-24‘ – Crime Rate
10 000 . 1 ‘ _ 0 1 A V ,s,_ g_
5000 _ ‘ C 1 1 ,0 2
0 l . . . I. . . ’ ‘I l. 1 i
1962 1966 1970 1974 1978 1982 1986 1990 1994 1998 2002
Year
Population 15-24‘ – Crime Rate
Note that the population 15-24 refers to changes in the population for these age groups
ad not changes in crime rates.
Source: Wallace, Marnie. “Crime Statistics in Canada, 2003,” adapted from Statistics
Zanada Catalogue no. 85-002-XIE, Vol. 24, no. 6, page 3.
C] P 278 In Class Assignment: Chapter 5 – Correlates of Criminal
ad not changes in crime rates.
Source: Wallace, Marnie. “Crime Statistics in Canada, 2003,” adapted from Statistics
Zanada Catalogue no. 85-002-XIE, Vol. 24, no. 6, page 3.
C] P 278 In Class Assignment: Chapter 5 – Correlates of Criminal
Behaviour. Ten questions.
1. What type of crime(s) does not decrease with the age of the offender? Speculate on why these
types of crimes increase with age.
types of crimes increase with age.
2. Describe maturational reform, and discuss how it reflects the age distribution of criminal
behaviour.
behaviour.
3. Summarize the differences in criminal behaviour based on gender and discuss whether the
concept of role convergence might apply to these trends.
concept of role convergence might apply to these trends.
4. Discuss and evaluate several possible explanations of Aboriginal over-representation in the
Canadian criminal justice system.
Canadian criminal justice system.
5. Discuss the ways in which the correlation between drug abuse and crime could be explained.
6. g Describe and evaluate the evidence concerning the relationship between social class and
criminal behaviour.
criminal behaviour.
7. Discuss how provincial variations in crime rates could be explained.
134 PART 1 Crime and Society
For example, some Criminologists have claimed that delinquent behaviour is
correlated with physique or body type. They claim that adolescent males witi:
an athletic, muscular body build commit more delinquent acts than those
whose physique is lean and fragile. Other criminologists have shown that some
types of crime occur more frequently in larger cities than in smaller towns and
rural areas. They argue that city size and crime vary together. These examples
give measurements on two variables (for example, city size and crime) for a
number of individuals or aggregates (for example, cities). The task is to deter-
mine whether and how these two sets of measurements go together-that is,
whether and how they’re correlated. When we analyze the relationship between
correlated with physique or body type. They claim that adolescent males witi:
an athletic, muscular body build commit more delinquent acts than those
whose physique is lean and fragile. Other criminologists have shown that some
types of crime occur more frequently in larger cities than in smaller towns and
rural areas. They argue that city size and crime vary together. These examples
give measurements on two variables (for example, city size and crime) for a
number of individuals or aggregates (for example, cities). The task is to deter-
mine whether and how these two sets of measurements go together-that is,
whether and how they’re correlated. When we analyze the relationship between
two variables, we may find that they are positively correlated, negatively correl-
ated, or unrelated to each other. A positive relationship means that as one vari-
able increases, the other also increases. For example, the more deviant friends
we have, the more likely we are to be deviant ourselves. A negative relationship
means that as one variable increases, the other decreases. For example, as we
get older, we are less likely to be involved in criminal behaviour. Discovering
such correlations or relationships is an important first step for any scientific
discipline such as criminology. Thus, a good deal of the early work in crimin-
ology was devoted to the task of identifying and describing the correlates of
crime.
ated, or unrelated to each other. A positive relationship means that as one vari-
able increases, the other also increases. For example, the more deviant friends
we have, the more likely we are to be deviant ourselves. A negative relationship
means that as one variable increases, the other decreases. For example, as we
get older, we are less likely to be involved in criminal behaviour. Discovering
such correlations or relationships is an important first step for any scientific
discipline such as criminology. Thus, a good deal of the early work in crimin-
ology was devoted to the task of identifying and describing the correlates of
crime.
Having identified and described a correlate of crime-a relationship-
it is natural to want to know why it exists. How might this relationship be
explained? What might have produced it?
it is natural to want to know why it exists. How might this relationship be
explained? What might have produced it?
One explanation for a correlation or relationship between two variables is
causal. The concept of causation has been debated by philosophers and social
scientists. At the very least, the idea of causation has proven to be a useful way
of thinking about the natural and social world. A causal explanation refers
to the inference that a change in one variable results from or is produced by
change in another variable. A common mistake is to confuse correlation with
causation and so to infer that one variable causes another from the fact that
they are correlated. However, correlation means only that two variables are
related. They are associated but a change in one does not necessarily produce
a change in the other. Criminologists are frequently interested in establishing
causal explanations. They are not usually satisfied, for example, with knowing
that poverty and crime are correlated; they want to know if crime results from
poverty, and if it does, how? However, it would be a mistake to conclude from
their correlation alone that poverty causes crime. So correlation between two
causal. The concept of causation has been debated by philosophers and social
scientists. At the very least, the idea of causation has proven to be a useful way
of thinking about the natural and social world. A causal explanation refers
to the inference that a change in one variable results from or is produced by
change in another variable. A common mistake is to confuse correlation with
causation and so to infer that one variable causes another from the fact that
they are correlated. However, correlation means only that two variables are
related. They are associated but a change in one does not necessarily produce
a change in the other. Criminologists are frequently interested in establishing
causal explanations. They are not usually satisfied, for example, with knowing
that poverty and crime are correlated; they want to know if crime results from
poverty, and if it does, how? However, it would be a mistake to conclude from
their correlation alone that poverty causes crime. So correlation between two
variables is a necessary first step toward causal explanation but is not in itself
sufficient for inferring such an explanation.
sufficient for inferring such an explanation.
The second element of a causal explanation is a theory linking the vari-
ables. Events may occur together, but one will not be viewed as causing the
other unless we have some explanation of how they are linked. We look for
correlates of crime to help us to explain criminal behaviour, and our explana-
tory theories must be tested against what we know about crime. For example,
we may have a theory that the movement of the planets causes criminality.
However, when we test this explanation, we find that there is no correlation
between planetary movements and crime rates. Thus, we will reject the theory,
at least until some evidence supporting it can be found. Much of the attention
NEL
ables. Events may occur together, but one will not be viewed as causing the
other unless we have some explanation of how they are linked. We look for
correlates of crime to help us to explain criminal behaviour, and our explana-
tory theories must be tested against what we know about crime. For example,
we may have a theory that the movement of the planets causes criminality.
However, when we test this explanation, we find that there is no correlation
between planetary movements and crime rates. Thus, we will reject the theory,
at least until some evidence supporting it can be found. Much of the attention
NEL
CHAPTER 5 Correlates of Criminal Behaviour 135
of criminologists has been devoted to developing and testing theories of crime,
and these theories must explain the facts about crime. Therefore, the correlates
discussed in this chapter set the stage for the theories that follow.
of criminologists has been devoted to developing and testing theories of crime,
and these theories must explain the facts about crime. Therefore, the correlates
discussed in this chapter set the stage for the theories that follow.
Human beings have a tendency to simplify their perceptions of the
world. This tendency can lead to a distortion of the understanding of cor-
relates and causes. There is a temptation to assume that an effect can have
only one cause that is both necessary and sufficient. There is also the temp-
tation to assume that causes must be perfectly correlated with their effects
and that no other variables are necessary for interpreting how the cause
operates or for specifying the particular conditions or circumstances under
which it has its effect(s) (Hirschi and Selvin, 1966). But reality is substan-
tially more complicated than this, and it would be wise to develop the habit
of thinking of crime as the consequence of multiple causes that combine
in complicated ways to produce their effects. Nettler (1982) uses the image
of a dense web to communicate the meaning of this view of causation.
He describes this as a multiplicity of tightly packed causes that interact
strongly and non-uniformly. As selected correlates of crime are examined,
then, the urge to reach hasty conclusions concerning the causal character
and signific
ance of these correlations should be resisted.
world. This tendency can lead to a distortion of the understanding of cor-
relates and causes. There is a temptation to assume that an effect can have
only one cause that is both necessary and sufficient. There is also the temp-
tation to assume that causes must be perfectly correlated with their effects
and that no other variables are necessary for interpreting how the cause
operates or for specifying the particular conditions or circumstances under
which it has its effect(s) (Hirschi and Selvin, 1966). But reality is substan-
tially more complicated than this, and it would be wise to develop the habit
of thinking of crime as the consequence of multiple causes that combine
in complicated ways to produce their effects. Nettler (1982) uses the image
of a dense web to communicate the meaning of this view of causation.
He describes this as a multiplicity of tightly packed causes that interact
strongly and non-uniformly. As selected correlates of crime are examined,
then, the urge to reach hasty conclusions concerning the causal character
and signific
ance of these correlations should be resisted.
Peak Ages for Crime
Without much exaggeration, crime can be said to be a young man’s game. To
put this into more technical terms, age and sex are strong correlates of criminal
behaviour. Any number of criminologists has singled out these two variables
for their strong relationship with crime. For example, Sutherland and Cressey
(1978) state that sex status is of greater importance in differentiating criminals
from non-criminals than any other trait, and that statistics from a Variety of
years and jurisdictions uniformly indicate a higher prevalence of crime among
young persons compared with other age groups. ‘*./‘J<7VV:)/“xi
young persons compared with other age groups. ‘*./‘J<7VV:)/“xi
Figure 5.1 shows the distribution of persons accused of crime by age for if“)
2010. The percentage of persons accused of crime increases from early adoles- §3:l’l’tvh’*é’;I)’I’Jirs{:r-t-alltzilslttitfglé’
cence to young adulthood and then generally declines. In 2010, age-specific I-ilggitsliztgflttgtgfing
rates for those accused of crime were highest among 15-to-20-year-olds, with research papers,goto
cence to young adulthood and then generally declines. In 2010, age-specific I-ilggitsliztgflttgtgfing
rates for those accused of crime were highest among 15-to-20-year-olds, with research papers,goto
the peak age at 18 years (Brennan and Dauvergne, 2011). This pattern is stronger
for property crimes (Bunge et al., 2005).
for property crimes (Bunge et al., 2005).
Data for 2008-09 from the Adult Criminal Court Survey (Thomas, 2010)
also show that younger adults are over-represented among accused persons
when comparing the age distribution of offenders to the age distribution of the
general adult population. For example, 18-to-24-year-olds made up 12 percent
of the adult population but accounted for 31 percent of all cases in adult crim-
Lnal court in 2008-09. Similarly, persons 25 to 34 represented 17 percent of the
adult population and 28 percent of the adult criminal court cases. This age dis-
tribution is stronger for property crimes (33 percent of accused were 18 to 24)
than for violent crimes (26 percent were 18 to 24).
when comparing the age distribution of offenders to the age distribution of the
general adult population. For example, 18-to-24-year-olds made up 12 percent
of the adult population but accounted for 31 percent of all cases in adult crim-
Lnal court in 2008-09. Similarly, persons 25 to 34 represented 17 percent of the
adult population and 28 percent of the adult criminal court cases. This age dis-
tribution is stronger for property crimes (33 percent of accused were 18 to 24)
than for violent crimes (26 percent were 18 to 24).
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