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A central theme in Chapter 7 is that the effects of self-control are not always the same—low self-control sometimes leads to crime and deviance, but sometimes it does not. When this pattern occurs, it often is because some “other factor” has come into play to amplify or diminish the effects of low self-control.

For this discussion, pick ONE of the factors below and describe the theory and research suggesting that it leads the effects of low self-control on crime to be different than what they normally would be: 

Criminal opportunity

Association with delinquent peers

Neighborhood disadvantage

sources to use :  
Hay, Carter, and Ryan Meldrum. 2016. Self-control and crime over the life course.    

Hart and Risley (2003), “The early catastrophe,” from American

The Early

The 30 Million Word Gap by Age 3

By Betty Hart and Todd R. Risley

D uring the 1960’s War on Poverty, we were among the many researchers, psychologists, and educators who brought our knowledge of child development
to the front line in an optimistic effort to intervene early to
forestall the terrible effects that poverty was having on some
children’s academic growth. We were also among the many
who saw that our results, however promising at the start,
washed out fairly early and fairly completely as children

In one planned intervention in Kansas City, Kans., we
used our experience with clinical language in tervention to
design a half-day program for the Turner House Preschool,
located in the impoverished Juniper Gardens area of the city.
Most interventions of the time used a variety of methods
and then measured results with IQ tests, but ours focused
on building the everyday language the children were using,
then evaluating the growth of that language. In addition,
our study included not juSt poor children from Turner
House, but also a group of University of Kansas professors’
children against whom we could measure the Turner House
children’s progress.

All the children in the program eagerly engaged with the
wide variety of new materials and language-intensive activi-
ties introduced in the preschool. The spontaneous speech
data we collected showed a spurr of new vocabulary words

Betty Hart is professor ofHuman Development at the Univer-
sity of Kansas and senior scientist at the Schiefelbusch Institute
for Life Span Studies. Todd R. Risley is professor in the Depart-
ment of Psychology at the University ofAlaska Anchorage and
director ofAlaska’s Autism Intensive Early Intervention Project.
The two have collaborated on research projects for more than
35 years. This article is excerpted with permission from Mean-
ingful Differences in the Everyday Experiences of Young
American Children, © 1995, Brookes; www.brookespublish-
ing.com; 1-800-638-3775; $29.00.

added to the dictionaries of all the children and an abrupt
acceleration in their cumulative vocabulary growth curves.
But just as in other early intervention programs, the in-
creases were temporary.

We found we could easily increase the size of the chil-
dren’s vocabularies by teaching them new words. But we
could not accelerate the rate of vocabulary growth so that it
would continue beyond direcr teaching; we could not
change the developmental trajectory. However many new
words we taught the children in the preschool, it was clear
that a year later, when the children were in kindergarten, the
effects of the boost in vocabulary resources would have
washed our. The children’s developmental trajectories of vo-
cabulary growth would continue to point to vocabulary sizes
in the future that were increasingly discrepant from those of
the professors’ children. We saw increasing disparity between
the extremes-the fast vocabulary growth of the professors’
children and the slow vocabulary growth of the Turner
House children. The gap seemed to foreshadow the findings
from other studies that in high school many children from
families in poverty lack the vocabulary used in advanced

Rather than concede to the unmalleable forces of hered-
ity, we decided that we would undertake research that would
allow us to understand the disparate developmental trajecto-
ries we saw. We realized that if we were to understand how
and when differences in developmental trajectories began,
we needed to see what was happening to children at home at
the very beginning of their vocabulary growth.

W e undertook 2 1/2 years of observing 42 families for an hour each month to learn aboU[ what typi-cally went on in homes with 1- and 2-year-old
children learning to talk. The data showed us that ordinary
families differ immensely in the amount of experience with


language and interaction they regularly provide their chil-
dren and that differences in children’s experience are
strongly linked to children’s language accomplishments at
age 3. Our goal in the longitudinal study was to discover
what was happening in children’s early experience that could
account for the intractable difference in rates of vocabulary
growth we saw among 4-year-olds.

Our ambition was to record “everything” that went on in
children’s homes-everything that was done by the children,
to them, and atound them. Because we were committed to
undertaking the labor involved in observing, tape recording,
and transcribing, and because we did not know exactly
which aspects of children’s cumulative experience were con-
tributing to establishing rates of vocabulary growth, the
more information we could get each time we were in the
home the more we could potentially learn.

We decided to start when the children were 7-9 months
old so we would have time for the families to adapt to obser-
vation before the children actually began talking. We fol-
lowed the children until they turned three years old.

The first families we recruited to participate in the study
came from personal contacts: friends who had babies and
families who had had children in the Turner House
Preschool. We then used birth announcements to send de-
scriptions of the study to families with children of the de-
sired age . In recruiting from birth announcements, we had
[wo priorities . The first priority was to obtain a range in de-
mographics , and the second was stability-we needed fami-
lies likely to remain in the longitudinal study for several
years. Recruiting from birth announcements allowed us to
preselect families . We looked up each potential family in the
city directory and listed those with such signs of permanence
as owning their home and having a telephone. We listed
families by sex of child and address because demographic
status could be reliably associated with area of residence in
this city at that time. Then we sent recruiting letters selec-
tively in order to maintain the gender balance and the repre-
sentation of socioeconomic strata.

Our final sample consisted of 42 families who remained
in the study from beginning ro end. From each of these fam-
ilies, we have almost 2 1/ 2 years or more of sequential
monthly hour-long observations. On the basis of occupa-
tion, 13 of the families were upper socioeconomic status
(5E5), 10 were middle 5E5 , 13 were lower 5E5 , and six were
on welfare. There were African-American families in each
5E5 category, in numbers roughly reflecting local job alloca-
tions. One African-American family was upper 5E5, three
were middle, seven were lower, and six families were on wel-
fare. Of the 42 children, 17 were African American and 23
were girls. Eleven children were the first born to the family,
18 were second children, and 13 were third or later-born

What We Found
Before children can take charge of their own experience and
begin to spend time with peers in social groups outside the
home, almost everything they learn comes from their fami-


Eighty-six percent to 98 percent
of the words recorded in each
child’s vocabulary consisted
of words also recorded
in their parents’ vocabularies.



lies, to whom soci ety has assigned the task of soc ializing
children. We were not surprised to see the 42 children turn
out to be like their parents; we had no t full y realized, how-
ever, the implications of those simi lari ties for the children’s

We observed the 42 children grow more like their par-
ents in stature and ac tivity levels, in vocabul ary resources,
and in lan guage and interaction styl es . Despite the consid-
erable range in vocabulary size among the children, 86 per-
cent to 98 percent of the words recorded in each child ‘s vo-
cabulary consisted of words also recorded in their parents’
vocabularies. By the age of 34-36 months , the children
were also talking and using numbers of differen t words
very similar to the averages of their parents (see the table

By the time the children were 3 years old, trends in
amount of talk, vocabulary growth, and style of interaction
were well established and clearly suggested widening gaps to
come. Even patterns of parenting were already observable
among the children . When we listened to the children, we
seemed to hear their parents speaking; when we watched the
children play at parenting their dolls , we seemed to see the
futures of their own children.

Families’ Language and Use
Differ Across Income Groups


12 Profession al 23 Working.class 6 Welfare
Measures and scores Paren! Child Pateur Child Paten! Child

Pretest score’ 41 31 14
Recorded vocabulary

Size 2,176 1,116 1,49 8 749 974 525
Average utteran ces

per hour” 487 310 301 223 176 168
Average d ifFerem

words per hour 382 297 25 1 216 167 149
‘Wh en we began the longitudinal study, we asked rhe parents to complete a vocab u·
lary ptetest. At the first observa ti on each paren t was asked to complete a fotm abo
stracted from the Peabod y Pictu re Vocabu lary Test (PPVT ). We gave each parent a
list of 46 vocabulary words and a seties of pictures (fou r op ti ons per vocabulary
word) and asked the pa ten t to write beside each word the number of the picture
rhar corresponded ro th e wrirren wo rd . Pa renr perform ance on [h e resr was highly
correlated with years of ed ucation (r = .5 7).

‘Parent u[(erances and different words were averaged over 13-36 months of child
age. Child utterances and different wotds we re ave raged for the four observarions
when th e chi ld ren we re 33·36 month s old .

We now had answers to our 20-year-old questions . We
had observed, recorded , and analyzed more than 1,300
hours of casual interactions berween parents and their lan-
guage-learn ing children. We had dissembled these interac-
tions into several dozen molecular features that could be reli-
ably coded and counted. We had examined the correlations
berween the quantities of each of those featur es and severa l
outcome measures relating to children’s languageaccom-
plishments. .

After all 1,318 observations had been entered into the
computer and checked for accuracy against the raw data,
after every word had been checked for speJling and coded
and checked for its part of speech, after every utterance had
been coded for syntax and discourse function and every code
checked for accuracy, after random samples had been re-



coded to check the reliability of the coding, after each file
h ad been checked one more time and the accuracy of each
aspect verified, and after th e data analys is programs had fi-
nally been run to produce frequency counts and dictionary
lists for each observation, we had an immense numeric
database that required 23 million bytes of computer file
space. We were flllally read y to begin asking what it all

It took six years of painstaking effort before we saw the
first results of the longitudinal research. And then we were
astonished at the differences the data revealed (see the graph

Children’S Vocabulary Differs Greatly




‘5 ‘” .0
0 >
> 600



10 12 14 16 18 20 22 24 26 28 30 32 34 36

Age of child in monlhs

Like the children in the Turner House Preschool, the three yea r old children from families on welfare not only had smaller vocabularies than did children of the
same age in professional families , but they were also add ing
words more slowly. Projecting the developmental trajectory
of the welfare children’s vocabulary growth curves, we could

. see an ever-widening gap similar to the on e we saw berween
the Turner House children and the professors’ children in

While we were immersed in collecting and processing
the dat a, our thoughts were concerned only with the next
utterance to be transcribed or coded. While we were ob-
serving in the homes, though we were aware that th e fami-
lies were very different in lifestyles, they were all similarly
engaged in the fundamental task of raising a child. All the
families nurtured their children and played and talked with
them . They all disciplined their children and taught them
good manners and how to dres s and toilet themselves.
They provided their children with much the same toys and
talked to them about much the same things. Though dif-
ferent in personality and skill level s, the children all
learned to talk and to be socially appropriate members of
the family with all the basic skills needed for preschool


Across Income Groups
13 higher
SES children

” (profeSSional)

23 middle/lowe r·
SES children

6 children from
families on welfare

, ,

Test Performance in Third Grade Follows
Accomplishments at Age 3
We wondered whether the differences we saw at age 3 would
be washed out, like the effects of a preschool intervention, as
the children’s experience broadened to a wider community
of competent speakers. Like the parents we observed, we
wondered how much difference children’s early experiences
would actually make . Could we, or parents, predict how a
child would do in school from what the parent was doing
when the child was 2 years old?

Fortune provided us with Dale Walker, who recruited 29
of the 42 families to participate in a study of their children’s
school performance in the third grade, when the children
were nine to 10 years old.

We were awestruck at how well our measures of accom-
plishments at age 3 predicted measures of language skill at
age 9-10. From our preschool data we had been confident
that the rate of vocabulary growth would predict later per-
formance in school; we saw that it did . For the 29 children
observed when they were 1-2 years old, the rate of vocabu-
lary growth at age 3 was strongly associated with scores at
age 9-10 on both the Peabody Picture Vocabulary Test-Re-
vised (PPVT-R) of receptive vocabulary (r = .58) and the
Test of Language Development-2: Intermediate (TOLD)
(r = .74) and its subtests (listening, speaking, semantics,

Vocabulary use at age 3 was equally predictive of measures
of language skill at age 9-10. Vocabulary use at age 3 was
strongly associated with scores on both the PPVT-R
(r = .57) and the TOLD (r = .72). Vocabulary use at age 3
was also strongly associated with reading comprehension
scores on the Comprehensive Test of Basic Skills (CTBS/U)
(r= .56).

The 30 Million Word Gap By Age 3
All parent-child research is based on the assumption that the
data (laboratory or field) reflect what people typically do. In
most studies, there are as many reasons that the averages
would be higher than reponed as there are that they would
be lower. But all researchers caution against extrapolating
their findings to people and circumstances they did not in-
clude. Our data provide us, however, a first approximation
to the absolute magnitude of children’s early experience, a
basis sufficient for estimating the actual size of the interven-
tion task needed to provide equal experience and, thus,
equal opportunities to children living in poverty. We depend
on future studies to refIne this estimate.

Because the goal of an intervention would be to equalize
children’s early experience, we need to estimate the amount
of experience childten of different SES groups might bring
to an intervention that began in preschool at age 4. We base
our estimate on the remarkable differences our data showed
in the relative amounts of children’s early experience: Simply
in words heard, the average child on welfare was having half
as much experience per hour (616 words per hour) as the av-
erage working-class child (1,251 words per hour) and less
than one-third that of the average child in a professional
family (2,153 words per hour). These relative differences in


amount of experience were so durable over the more than
two years of observations that they provide the best basis we
currently have for estimating children’s actual life experience.

A linear extrapolation from the averages in the observa-
tional data to a 100-hour week (given a 14-hour waking
day) shows the average child in the professional families
with 215,000 words of language experience, the average
child in a working-class family provided with 125,000
words, and the average child in a welfare family with 62,000
words of language experience . In a 5,200-hour year, the
amount would be 11 .2 million words for a child in a profes-
sional family, 6 .5 million words for a child in a working-
class family, and 3.2 million words for a child in a welfare
family. In four years of such experience, an average child in a
professional family would have accumulated experience with
almost 45 million words, an average child in a working-class
family would have accumulated experience with 26 million
words, and an average child in a welfare family would have
accumulated experience with 13 million words. By age 4,
the average child in a welfare family might have 13 million
fewer words of cumulative experience than the average child
in a working-class family. This linear extrapolation is shown
in the graph below.

The Number of Words Addressed to Children
Differs Across Income Groups

50 million Professional

40 million


1:’ o



OJ ;;;



o 12 24 36 48
Age of child in months


But the children’s language experience did not differ just
in terms of the number and quality of words heard. We can
extrapolate similarly the relative differences the data showed
in children’s hourly experience with parent affirmatives (en-
couraging words) and prohibitions. The average child in a
professional family was accumulating 32 affirmatives and
five prohibitions per hour, a ratio of 6 encouragements to 1
discouragement. The average child in a working-class fam-
ily was accumulating 12 affirmatives and seven prohibitions
per hour, a ratio of 2 encouragements to 1 discouragement.
The average child in a welfare family, though, was accumu-
lating five affirmatives and 11 prohibitions per hour, a ratio
of 1 encouragement to 2 discouragements. In a 5,200-hour
year, that would be 166,000 encouragements to 26,000 dis-
couragements in a professional family, 62 ,000 encourage-
ments to 36,000 discouragements in a working-class family,
and 26,000 encouragements to 57,000 discouragements in
a welfare family.


In four years, an average child in a
professional family would
accumulate experience with almost
45 million words, an average child in
a working-class family 26 million
words, and an average child in a
welfare family 13 million words,


Extrapolated [Q the first four years of life, the average
child in a professional family would have accumulated
560,000 more instances of encouraging feedback than dis-
couraging feedback, and an average child in a working-class
family would have accumulated 100,000 more encourage-
menrs than discouragemenrs. But an average child in a wel-
fare family would have accumulated 125,000 more instances
of prohibitions than encouragemenrs. By the age of 4, the
average child in a welfare family might have had 144 ,000
fewer encouragemenrs and 84,000 more discouragemenrs of
his or her behavior than the average child in a working-class

Extrapolating the relative differences in children’s hourly
experience allows us [Q estimate children’s cumulative experi-
ence in the first four years of life and so glimpse the size of
the problem facing inrervenrion. Whatever the inaccuracy of
our estimates, it is not by an order of magnitude such that
60,000 words becomes 6 ,000 or 600,000. Even if our esti-
mates of children’s experience are [00 high by half, the dif-
ferences between children by age 4 in amounrs of cumula-
tive experience are so great that even the best of intervention
programs could only hope [0 keep the children in families
on welfare from falling still further behind the children In
the working-class families.

The Importance of Early Years Experience
We learned from the longitudinal data that the problem of
skill differences among children at the time of school entry
is bigger, more inrractable, and more important than we had
thought. So much is happening to children during their first
three years at home, at a time when they are especially mal-
leable and uniquely dependent on the family for virtually all
their experience, that by age 3, an intervention must address
not just a lack of knowledge or skill, but an entire general
approach [0 experience.

Cognitively, experience is sequential: Experiences in in-
fancy establish habits of seeking, noticing, and incorporating
new and more complex experiences, as well as schemas for
categorizing and thinking about experiences. Neurologically,
infancy is a critical period because cortical developmenr is
influenced by the amounr of central nervous system activity
stimulated by experience. Behaviorally, infancy is a unique
time of helplessness when nearly all of children’s experience
is mediated by adults in one-to-one interactions permeated
with affect. Once children become independent and can
speak for themselves, they gain access to more opportunities
for experience. But the amount and diversity of children’s
past experience influences which new opportunities for ex-
perience they notice and choose.

Estimating, as we did, the magnitude of the differences in
children’s cumulative experience before the age of 3 gives an
indication of how big the problem is . Estimating the hours
of inrervenrion needed [0 equalize children’s early experience
makes clear the enormity of the effort that would be re-
quired to change children’s lives. And the longer the effort is
put off, the less possible the change becomes. We see why
our brief, intense efforts during the War on Poverty did not
succeed. But we also see the risk to our nation and its chil-
dren that makes intervenrion more urgenr than ever. 0


SAGE Books

Self-Control and Crime Over the Life Course

Do the Harmful Effects of Low Self-Control Vary
Across Different Circumstances?

Contributors: By: Carter Hay & Ryan Meldrum

Book Title: Self-Control and Crime Over the Life Course

Chapter Title: “Do the Harmful Effects of Low Self-Control Vary Across Different Circumstances?”

Pub. Date: 2016

Access Date: November 21, 2021

Publishing Company: SAGE Publications, Inc.

City: Thousand Oaks

Print ISBN: 9781483358994

Online ISBN: 9781544360058

DOI: http://dx.doi.org/10.4135/9781483397726.n7

Print pages: 179-208

© 2016 SAGE Publications, Inc. All Rights Reserved.

This PDF has been generated from SAGE Knowledge. Please note that the pagination of the online

version will vary from the pagination of the print book.

Do the Harmful Effects of Low Self-Control Vary Across Different Circumstances?

Do the Harmful Effects of Low Self-Control Vary Across Different Circumstances?

The idea of resilience is one of the more exciting behavioral science stories of recent decades (Fergus &
Zimmerman, 2005; Masten, 2001), and it illustrates a pattern central to this chapter. Resilience refers to a
pattern in which highly disadvantaged children unexpectedly overcome their adversity to achieve competence
and success over the life course. These are children born into intense poverty, perhaps to a single parent
who did not finish high school, who often have been exposed to trauma and hardships involving such things
as family violence, the death of a parent, or abuse and neglect. Prior research tells us of their expected
struggles—with such things as crime, substance use, and school dropout—in adolescence and adulthood.
And yet, somewhat miraculously, many disadvantaged children studied over long periods of time were not
plagued by these problems. Indeed, some truly thrived (Luthar, 2003)—they did well in school, got along with
peers, and then pursued conventional lines of success as adults in the areas of work and family.

Behavioral scientists naturally were drawn to these patterns and looked for explanations. Early scholarship
focused on the remarkable and extraordinary nature of these resilient individuals. Masten (2001, p. 227) noted
that they were seen as “invincible” and “invulnerable”—nothing could stand in their way. Beauvais and Oetting
(2002) similarly observed the tendency to view resilient youth as “golden children” with magical abilities to
overcome hardship. One book even dubbed them the “superkids of the ghetto” (Buggie, 1995).

As research continued, however, an interesting pattern emerged: Instances of resilience were more common
than expected (Masten, 2001)—not common, just not quite as rare as one might think. This undermined the
view that resilience followed purely from the superhero qualities of these children. After all, superheroes are
supposed to be really rare, right?

With that in mind, resilience research has over time come to emphasize a less sensational perspective—one
that acknowledges the impressive determination of resilient youth but that also sees them as examples of
a general behavioral science process in which the harmful effects of adversity vary across individuals and
situations. They often materialize as expected, but sometimes they do not, and when the latter occurs, helpful
other factors often have come into play to diminish the harmful effects of childhood adversity. Sometimes
that helpful factor is an individual quality like high IQ or strong interpersonal coping skills—these enable
smoother adaptations to hardship. Alternatively, some children benefit from social experiences with an adult
mentor or participation in an effective intervention program (see Luthar, 2003, and Masten, 2001, for reviews).
Regardless of what protective factor comes into play, the key theme of resilience research remains the same:
The causes of behavior have effects that often depend on other factors. Simply stated, x leads to y in many
instances, but not in other instances, and there may be interesting explanations for these varying effects.

This chapter considers a pattern like this for self-control research in particular. Theorists and researchers have
long speculated that the harmful effects of low self-control systematically vary (Gottfredson & Hirschi, 1990;
Grasmick, Tittle, Bursik, & Arneklev, 1993). Depending on other factors that come into play, these effects
may be greater in some instances and lesser in others. Recent research supports this speculation, and in
this chapter we identify those critical other factors that come into play—factors that work together with low
self-control to affect the likelihood of crime and deviance. Considering the joint operation of these different
causes offers more nuanced insight into how low self-control affects crime and deviance. It also generates
practical insights that are relevant to public policy—a list of factors known to lessen the harmful effects of low
self-control provides a checklist of protective factors that can be targeted in policies and programs. With that
in mind, our discussion will turn ultimately to the policy implications of our arguments.

Conditional Causation and Low Self-Control: Conceptual Issues

Much of this book has described a pattern in which low self-control has a substantial effect on behavior. For
example, those with low self-control may have a 40% greater chance of being involved in crime than those
with high self-control (Burt, Simons, & Simons, 2006, Table 2; Hay, Meldrum, & Piquero, 2013, Table 2). And

© 2016 by SAGE Publications, Inc.

SAGE Books

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SAGE Books – Do the Harmful Effects of Low Self-Control Vary Across

Different Circumstances?

for simplicity’s sake, we have often spoken of this large effect in a fairly singular sense—as if there were
one effect that operates similarly across all individuals, contexts, and circumstances. But as the discussion
of resilience just indicated, the situation likely is more complex than this—low self-control does not always
translate into the same 40% increase. Under some circumstances, it may increase the odds of crime by as
much as 50% or 60%, while under other circumstances, the effects of low self-control may be much lower
than 40%, perhaps even approaching zero.

These possibilities involve a process that behavioral scientists refer to as conditional, interactive, or
moderated causation. We offer each of those terms because they are the ones variously used in this literature;
in practice, they all mean essentially the same thing, each describing a process in which the effect of a given
cause depends on other factors. Such an effect is conditional in the sense that it depends upon the presence
of other factors. Similarly, it is interactive in the sense that the effect depends on whether it “interacts with”—or
“co-occurs with”—another variable. And such an effect is moderated in the sense that the presence of some
other factor moderates—or changes—the original effect (the effect that exists when the moderating factor is
not considered). Again, the meaning is the same with all—the effects of a given cause of behavior (like low
self-control) systematically vary according to other factors.

Importantly, those other factors may at times amplify the effects of a given variable, while at other times
they may diminish those effects. These patterns can be seen with a simple example unrelated to self-
control. Everyone at times takes medication to battle a cold or allergies. When you do, you may notice
the label that issues these warnings: “Do not consume alcohol while using this medication” and “Do not
drive, use machinery, or do any activity that requires alertness.” There is a good reason for these warnings:
Many of these medicines include an antihistamine that reduces swelling in the nose and throat, but in the
process of doing so also makes people feel drowsy. And, of course, alcohol also increases drowsiness.
When these things are consumed in conjunction with one another, the two interact such that the increase in
drowsiness is even greater than what would be expected—there is a “multiplicative” effect of using these two
substances together. Thus, through this interaction, the effect of taking cold or allergy medicine on drowsiness
is conditional upon alcohol consumption; specifically, its effects on drowsiness are significantly amplified.

However, for other drug interactions, a diminishing pattern may be in effect. This sometimes is true for
antibiotics that are taken to eliminate an infection. Many antibiotics will not have this desired effect if they are
taken in conjunction with dairy consumption. The calcium in milk or yogurt decreases the digestive system’s
absorption of the antibiotic, therefore preventing the antibiotic from accomplishing its intended task. Thus,
through this interaction, the effects of the antibiotic are diminished when dairy consumption is present.

We can take this same logic and apply it back to the topic of low self-control. There are some conditioning
factors that play an amplifying role—when these factors are present, the effects of self-control on crime (and
other outcomes) become even greater. This means that the differences in crime between those with low and
high self-control are greater than they normally would be. Thus, under such circumstances, self-control takes
on added importance. Other factors, however, may diminish the effects of self-control. When this occurs, the
differences in crime between those with low and high self-control are lessened. They may even approach
zero. When this occurs, it points to a process in which the other factor is able to essentially push low self-
control to the side, rendering it largely inconsequential. Normally it would cause problems, but under these
circumstances it does not.

In Focus 7.1

Amplified and Diminished Effects

© 2016 by SAGE Publications, Inc.

SAGE Books

Page 3 of 16
SAGE Books – Do the Harmful Effects of Low Self-Control Vary Across

Different Circumstances?

This figure presents hypothetical data that nicely illustrate the manner in which the frequency of criminal
or delinquent opportunities moderates the effects of self-control on delinquency. The y-axis (the vertical
axis) shows the prevalence of delinquency—the percentage of adolescents who committed a delinquent
act during a given time period—while the x-axis identifies the groups of interest: low- and high-self-control
groups that vary across low, medium, and high levels of delinquent opportunity. Notice that in the medium
opportunity condition, there is a detectable but somewhat modest effect of self-control. Specifically, there
is a 4-percentage-point difference in the prevalence of delinquency between those with low and high self-
control—16% of those with high self-control have committed a delinquent act, but this goes up to 20% for
those with low self-control. Importantly, this difference between the low- and high-self-control groups changes
when we examine the other two values of opportunity. When opportunity levels are high, the difference jumps
to 14 percentage points (with a prevalence of 34% for the low-self-control group and 20% for the high-self-
control group). This indicates an amplifying effect of criminal opportunity—increased opportunity amplifies the
behavioral differences between those with low and high self-control. On the other hand, when the frequency of
opportunities is low, there is no meaningful difference in the prevalence of delinquency between the low-self-
control group (14% prevalence) and the high-self-control group (13% prevalence). This therefore indicates
a diminishing effect of low delinquent opportunity. Importantly, although the numbers we present here are
hypothetical, this is the basic pattern found in research considering interactive effects between self-control
and criminal/delinquent opportunities (see Grasmick et al., 1993; Hay & Forrest, 2008; Kuhn & Laird, 2013;
LaGrange & Silverman,1999; Longshore, 1998).

Criminal Opportunity

In criminology, criminal opportunity has received the most attention as a potential moderator of the effects of
low self-control. This attention followed from an argument made by Gottfredson and Hirschi (1990): Although
low self-control puts one at risk for giving in to criminal temptations, for this risk to be transformed into actual
crime, a criminal opportunity—a situation in which crime is possible and easily accomplished—must exist
as well.1 Smoking pot, for example, requires access to marijuana, an opportunity that might be especially
afforded to those whose friends smoke pot. This idea suggests that the presence of criminal opportunities
amplifies the effects of low self-control—when criminal opportunities are abundant, low self-control is more
easily translated into actual crime, thus leading the differences in crime between those with low and high self-
control to become greater.

To consider how this may often play out, imagine two 15-year-old males who both have low self-control
compared to the typical 15-year-old male. These two males should be more involved in delinquency than

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their high-self-control counterparts in the neighborhood. However, imagine that one of these males is quite
unique when it comes to opportunities for delinquency because his parents do little to monitor his behavior
and whereabouts. For example, after school, he returns to a home in which no parent is present. He largely is
free to do as he pleases, and this involves plenty of time hanging out with friends away from the supervision
of adults. Indeed, his parents may not know where he is or who he is with—they do not keep track of
who his friends are, and therefore do not know if they are bad influences. Our second male, however, is
different—although he has similarly low self-control, in these areas of supervision and monitoring, his parents
are at least doing an average job.

In comparing these two males, we would expect the unsupervised one to be at least moderately more
delinquent because of how often his low self-control will get coupled with easy opportunities for delinquency.
Moreover, when we compare him to the other males in the neighborhood—the ones with higher self-
control—we would expect him to be substantially more delinquent. That difference follows in part from his
lower self-control, but the difference is amplified by the absence of parental supervision that offers him such
a steady supply of situations in which he can translate his delinquent temptations into actual delinquency. For
him, such things as stealing small items from stores, committing acts of vandalism, and experimenting with
alcohol are all like “shooting fish in a barrel”—success is almost guaranteed.

A number of studies support the existence of a pattern like this (Grasmick et al., 1993; Hay & Forrest, 2008;
Kuhn & Laird, 2013; LaGrange & Silverman,1999; Longshore, 1998). Much of this research has focused on
adolescents in particular, and across these studies, criminal opportunity has been measured in varying ways.
Some studies have used indicators of parental supervision in line with the example cited above. In other
instances, opportunity has been measured with indicators of time spent with friends. This approach builds
on the consistent finding that much delinquency is committed in the presence of peers and that peers are a
major source of delinquent opportunities. In connection, some have argued that delinquency is comparable
to a “pickup” game of basketball—if someone is there with friends, he or she has the opportunity to play in a
game that spontaneously emerges, but if not, then he or she has missed out on the opportunity (Osgood et
al., 1996). Last, some studies have taken a more direct approach to measuring criminal opportunities—they
simply have asked individuals to indicate how frequently they find themselves in situations in which they could
easily commit a criminal act without fear of getting caught.

Taken together, these studies suggest a number of conclusions. Most notably, they find substantial variation
in criminal opportunity—some individuals have or perceive an extraordinary number of criminal opportunities,
while for others, this is much less the case. For example, in Longshore’s (1998) study of 500 convicted
offenders, subjects perceived an average of 13 opportunities to commit a property crime over a six-month
period; however, while some individuals perceived nearly zero opportunities, others perceived up to 200. Also,
these variations in opportunity are consequential. Opportunity generally has significant independent effects
on crime—more opportunities are associated with greater involvement in crime and delinquency, even after
statistically controlling for varying levels of self-control.

And in reference to our specific focus in this chapter—conditional effects of self-control—this research
typically indicates that the presence of criminal opportunities significantly amplifies the effects of low self-
control (Grasmick et al., 1993; Hay & Forrest, 2008; Kuhn & Laird, 2013; LaGrange & Silverman,1999;
Longshore, 1998). For example, in one of the first tests of this thesis, Grasmick and his colleagues (1993)
found that the effects of low self-control on crimes of force and fraud were at least two to three times greater
when perceived criminal opportunity was high. Similarly, Hay and Forrest (2008) found that differences in
crime between those with low and high self-control were twice as large when adolescents returned from
school each day to a home in which no adult was present. Time spent with peers and unsupervised time away
from home played similar roles—the more there were of these things, the greater the gap in crime between
those with low and high self-control.

Association With Delinquent Peers

Some researchers have considered not just whether adolescents spend a great deal of time with peers,
but also whether those peers are highly delinquent. If they are, peer associations could amplify the effects
of low self-control on delinquency. This would follow in part from the pattern we just described in which

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peer associations increase opportunities for delinquency. However, delinquent peers offer more than just
opportunity—they also may actively encourage delinquency. In the language of social learning theorists, this
involves the reinforcement of delinquency (Akers, 1998), something captured also in the concept of peer
pressure. Such peer pressure may intensify the harmful effects of low self-control—a given level of low
self-control will be translated into even greater delinquency when coupled with strong encouragement from
delinquent peers.

A number of studies support this possibility. Desmond, Bruce, and Stacer (2012) found, for example, that
adolescents were more likely to report using alcohol, tobacco, and other drugs if they were lower in self-
control, and this effect was stronger among those who also reported having more peers who used the same
substances. Similar conclusions emerged in studies from Longshore and Turner (1998) and Kuhn and Laird

However, not all studies reach this conclusion (Ousey & Wilcox, 2007), and some reach an opposite
conclusion. Meldrum and his colleagues (2009), for example, found that self-control had strong effects among
those with few delinquent peers but quite diminished effects among those with many delinquent peers.
Why would such a result emerge? Why would differences in crime between those with low and high self-
control be more pronounced among those with fewer delinquent peers? In asking that question, one standard
caveat applies: Studies with different samples, measures, and analytical approaches can generate different
findings in ways that sometimes can appear quite random. This is why important research questions are
never conclusively answered with just one study. With the Meldrum, Young, and Weerman (2009) study,
however, there is an interesting possibility that goes beyond that standard caveat. Specifically, in some
instances, a highly delinquent peer group may represent what Mischel (1977) referred to as a “strong”
environment—one in which the pervading norms, values, and behavioral expectations are so powerful that
they diminish the effects of individual qualities like self-control. Simply stated, in strong environments, group
norms and influences dominate over individual tendencies. With delinquent peer groups in particular, the
“push” toward crime may at times be strong to the point that crime will be common among those who are low
or high in self-control. This is consistent with what Meldrum and his colleagues (2009) found—those in the
most delinquent peer groups were relatively high in delinquency regardless of their level of self-control.

Taking it all into account, what should we conclude? Based on the existing research, the most common
pattern is one in which delinquent peers amplify the harmful effects of low self-control, thereby producing
greater differences in crime between those who are low and high in self-control. Nevertheless, this will not
always be the case. Moreover, in some instances, the criminogenic push of the delinquent peer group may be
strong enough to crowd out the effects of an individual quality like low self-control. Under such circumstances,
crime and delinquency may be quite common across the entire self-control continuum.

Figure 7.1 Effect of Self-Control on Self-Reported Delinquency Across Different Levels of Peer Delinquency

Source: Meldrum, Young, and Weerman (2009).

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Social Bonds

Social bonds to conventional people, goals, and institutions represent another potentially important
moderating factor. These conventional influences often may allow those with low self-control to bypass some
of its harmful effects on behavior—their low self-control may be pushing them toward an impulsive and
antisocial pattern of behavior, but these conventional influences can divert them back in a more prosocial
direction. Wright, Caspi, Moffitt, and Silva (2001) presented this argument in their model of life course
interdependence, reasoning that the effects of stable individual traits like low self-control should be diminished
by conventional social bonds that arise from such things as employment, family attachment, and commitment
to educational goals.

Many but not all studies have supported this pattern. In contrast to it, Doherty (2006) found that while self-
control and strong social bonds in adulthood (e.g., employment, stable marriage, service in the military) each
uniquely explained whether or not an offender ended his or her criminal career in adulthood, no significant
interaction between the two was found. Other studies have often pointed to a diminishing effect of strong
social bonding. This was the case in Wright and his colleagues’ (2001) analysis—the criminogenic effect of
low self-control was attenuated among young adults who had the most stable employment, greatest education
achievement, and strongest family ties.

Also, in a national study of high school students, Li (2004) found that having stronger beliefs in conventional
behavior and greater involvement in conventional activities (e.g., working on homework) diminished the effect
of low self-control on delinquency. Similarly, Gerich (2014) found a weakened effect of low self-control on
alcohol use among Australian college students living in social environments that promoted social conformity.

Neighborhood Disadvantage

There has been a tendency in criminology to study individual behavior in ways that ignore the broader
community and neighborhood context. There is good reason to expect, however, that individual
characteristics like low self-control manifest themselves differently across communities that vary on such
things as the level of poverty, the presence of criminal subcultures, and the extent of disorder and social
disorganization. If so, rather than studying individual qualities or community environments, researchers
should study both of these causal forces and how they interact to explain crime and antisocial behavior (Tonry,

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Ohlin, & Farrington, 1991).

One distinct possibility is that the effects of low self-control on crime are amplified in an economically
disadvantaged community. The poorest communities often are plagued by a wide array of social
disadvantages that encourage the emergence of criminal and aggressive subcultures (Anderson, 1999). In
such a context, low self-control may take on added importance—if an individual lacks self-control, this will
be coupled with a community context that encourages crime, and the result will be a stronger connection
between low self-control and crime. A number of studies have considered this possibility, with most revealing
an amplification effect of this kind. Lynam and his colleagues (2000), for example, studied roughly 400
adolescents who were spread across roughly 90 neighborhoods in Pittsburgh. They were interested in effects
of impulsivity and measured it in ways that are consistent with our conception of low self-control. They found
that among adolescents who lived in the most economically disadvantaged neighborhoods, impulsivity had a
strong effect on crime. In contrast, it had almost no effect in affluent neighborhoods—in those neighborhoods,
impulsive teens were no more likely to engage in delinquency than their less impulsive peers. Similar
conclusions were reached in studies from Meier, Slutske, Arndt, and Cadoret (2008) and Jones and Lynam
(2009)—the differences in crime and delinquency between those with low and high self-control were amplified
by residence in a socially and economically disadvantaged neighborhood.

Once again, however, not all studies have reached this conclusion. Vazsonyi, Cleveland, and Wiebe (2006)
found that the effects of self-control on delinquency were largely invariant across communities that differed
in socioeconomic status. Moreover, in studying Chicago neighborhoods, Zimmerman (2010) found that there
was no effect of low self-control on crime for adolescents in poor communities but a strong effect for
those who lived in wealthier communities. This pattern may also be explained by Mischel’s notion of strong
environments that we previously discussed—the pervading norms, values, and behavioral expectations in a
highly disadvantaged community may be powerful enough to diminish the effects of individual qualities. On
the other hand, in wealthier neighborhoods—those that lack a strong push toward crime—an individual quality
like low self-control may sometimes have greater freedom to exert its effects. It bears emphasizing, however,
that the most common empirical pattern is one in which social and economic disadvantages in the community
amplify the harmful effects of low self-control, thereby producing greater differences in crime between those
who are low and high in self-control.

Weak Moral Values

Per-Olof Wikström has in recent years suggested that under some circumstances, whether or not a person
has self-control is largely inconsequential for crime. In making this argument, he suggests that the key
question that self-control provokes—“Should I or should I not give in to a criminal temptation?”—does not
come into play for many individuals, even when they encounter an opportunity for crime.

A hypothetical scenario—one that has you doing some horrible things—helps illustrate this possibility.
Imagine you are driving along an isolated stretch of road and you pass an elderly woman whose shiny new
Cadillac has suffered a flat tire. The woman looks fatigued and disoriented—she may have been stranded
for some time. It’s a hot humid day, and she looks dehydrated. In her weakened state, she has wandered
away from the car to seek the shade of a nearby tree. However, she left her expensive-looking purse sitting
on the hood of her car. By the looks of this woman and her car, two things seem obvious to you: (1) Her purse
likely contains something valuable and (2) this listless old woman is in no condition to stop you from taking
it. Moreover, as you scan the area, you once again notice how isolated this stretch of road …