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:
Association with delinquent peers
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 Catastrophe
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 aged.
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 textbooks.
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
4AMERICAN EDUCATOR SPRING 2003
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.
Methodology 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 children.
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 futures.
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 below).
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 meant.
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 below).
Children’S Vocabulary Differs Greatly
‘E'” 0 3
800 ‘5 ‘” .0 0 > Ql > 600 ‘5
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 1967.
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 entry.
AMERICAN FEDERATION OF TEACHERS 7
Across Income Groups 13 higher SES children
23 middle/lowe r· SES children (working·class)
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, syntax).
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
i’! “0 “0
‘”<n 1:’ o
;;; :; E ::J
“”0 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 family.
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
AMERICAN FEDERATION OF TEACHERS 9
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
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
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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
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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).
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 (2013).
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 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.
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 is—you have not seen another car for some time.
Under these circumstances, grabbing the purse would be easy. It would be like taking candy from a baby, only easier. With this in mind, you decide to take the purse and leave the old woman to fend for herself. You do so with this convenient rationalization: Life is rough and we all face hardships, including flat tires and lost purses. In taking her purse, you will give her a chance to discover how resilient she can be in the face of adversity. What doesn’t kill her will only make her stronger—she’ll be just like those superkids of the ghetto!
But would you really do this? No, of course you wouldn’t! We genuinely believe that, and you might refrain from this crime for reasons that have little to do with self-control. If it did have to do with self-control, here is
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how the process would play out: You would see the woman’s unguarded purse as an opportunity to advance your self-interest, and this would be tempting, but your high self-control would lead you to override this temptation. Wikström (2004), however, argued that this is not how it would play out. From his perspective, when a situation like this is encountered, most individuals do not see stealing the purse as a genuine behavioral option—it runs too far afoul of their moral values, so much so that they are not consciously aware of a temptation. And if there is no temptation, there is no need for self-control.
Wikström (2004) presents this as part of his situational action theory, which sees the propensity to commit crimes as governed mainly by moral values and habits. Moral values are rules about what is right or wrong behavior in a given situation, while moral habits involve automated prosocial responses to familiar circumstances. Wikström argues that these values and habits dictate whether we see criminal behaviors as action alternatives. If our values and habits are such that crimes do not enter our minds as viable alternatives, then self-control is not needed. And in the terminology of this chapter’s discussion, this would be an interactive process that diminishes the effects of low self-control—strong moral values and habits render self-control less consequential, such that there should be few differences in crime between those with low and high self- control. Either type of person—those low and high in self-control—would avoid crime because their values and habits stopped them from even considering it.
There is a corollary of this: When moral values and habits are not so prosocial, then self-control should matter. And this could come into play in many ways. Most notably, there are many criminal and antisocial acts that are morally murky, so to speak—the difference between right and wrong is not so clear or important. Moreover, there are some individuals who have a weaker commitment to basic moral values and habits. Among these individuals, self-control should matter greatly because they encounter many situations in which antisocial behavior is seen as a morally acceptable alternative.
Few studies have examined this issue, but they often support the argument that the effects of low self-control depend on moral values. For example, Schoepfer and Piquero (2006) collected data from nearly 400 college students on their self-control, their moral beliefs, and their willingness to assault and steal. Regarding the latter, participants were asked to read scenarios and place themselves in the position of its actors who at times assaulted others or committed thefts. On a scale from 0 to 10, they rated the likelihood they would act as the person in the scenario did if they were to face the same circumstances. Consistent with the arguments above, Schoepfer and Piquero (2006) found that the most pronounced effects of self-control on willingness to assault or steal were among those with weaker moral beliefs. Wikström and Svensson (2010) reached the same conclusion in their study of 14- to 15-year-olds in the United Kingdom. They found that when there were strong moral objections to crime, self-control was of little consequence—crime was low among those both low and high in self-control. However, when moral beliefs were weak, there was a strong effect of self-control that came from a big spike in criminal involvement among those with low self-control. Simply stated, when low self-control was coupled with the absence of any moral constraints, crime was substantially more likely (see also the supporting research from Zimmerman, Botchkovar, Antonaccio, and Hughes, 2012).
The conclusions therefore are fairly consistent, but we should emphasize that few studies have examined this issue and there is contrary research that finds effects of self-control that do not depend on moral values (Antonaccio & Tittle, 2008). Moreover, we also wonder whether the line between self-control and moral beliefs is as stark as Wikström has argued. In connection, an important question is this: Where do moral values come from? Moral values and habits may evolve over the life course and follow in part from prior levels of self-control—–perhaps a person’s prior willingness to override antisocial impulses and defer immediate gratification sets the stage for defining certain actions as immoral and developing habits in which such actions are avoided. In support of this possibility, the correlation between self-control and moral beliefs may be as high as .60 (Antonaccio & Tittle, 2008; Wikström & Svensson, 2010)—when someone has higher self-control, they often will have stronger moral beliefs.
Taking this all into account, we draw two main conclusions. First, at any given point in time, there is good reason to believe that effects of self-control on behavior depend on the strength of moral values and habits. Strong moral condemnation of an act often diminishes the consequences of low self-control; correspondingly, when moral beliefs are weaker, low self-control likely takes on added importance. Second, we suspect that much can be learned in future research that considers how early self-control tendencies affect the
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development of moral beliefs and habits over the life course.
In Focus 7.2
The Evolutionary Origins of Moral Values
The research in this chapter suggests that those with especially strong moral values often need not rely on self-control because temptations that are morally problematic may, for them, not even be tempting at all. And yet, there is good reason to expect that the link between self-control and moral values is more complicated than this. The two may be intertwined in fascinating ways, with each developing partly in reliance on the other. Most notably, a person’s self-control may lead him or her to have stronger moral values (if self-control is the temperamental quality that encourages diligent reflection on morality). It also is possible, however, that morality helps a person have high self-control (because those with strong morality have powerful higher-order standards to draw upon in overriding behavior).
We suspect that future research will yield more insights on the interconnections between self-control and morality, and in considering that possibility, we are drawn to the fascinating research done at Yale University’s Infant Cognition Center. It has considered the evolutionary origins of moral values, discovering that a capacity for morality likely is “hardwired” into our brains to some degree. They have reached this conclusion by asking a novel question: Do babies know something about morality, and if so, how soon do they know it? If humans can demonstrate moral behavior prior to even talking or walking, there is good reason to suspect that an inherent biological capacity for morality is at work. In short, a capacity for morality may be a key facet of human nature that we each acquire and develop from conception.
Research at Yale’s “baby lab” (as some have called it) has supported this view in laboratory studies that are almost as entertaining as Walter Mischel’s marshmallow studies with 4- and 5-year-olds nearly five decades earlier. The task for these baby morality studies was straightforward: Infants must be placed in a setting in which a moral decision could be made, and a clever way of allowing them to communicate their moral preferences had to be devised. An opening passage in Paul Bloom’s (2013, p. 7) book Just Babies: The Origins of Good and Evil perfectly captures the experiments they designed and the observations Bloom and others have made over the years:
The one-year-old decided to take justice into his own hands. He had just watched a puppet show with three characters. The puppet in the middle rolled a ball to the puppet on the right, who passed it right back to him. It then rolled the ball to the puppet on the left, who ran away with it. At the end of the show, the “nice” puppet and the “naughty” puppet were brought down from the stage and set before the boy. A treat was placed in front of each of them, and the boy was invited to take one of the treats away. As predicted, and like most toddlers in the experiment, he took it from the “naughty” one—the one who had run away with the ball. But this wasn’t enough. The boy then leaned over and smacked this puppet on the head.
Study after study reinforces this observation, indicating that babies show a natural understanding of the difference between right and wrong, often as early as the sixth month of life. And just like the boy above, they sometimes even get mad at the immoral and unfair actions of others. Bloom offers a provocative conclusion on this: “Certain moral foundations are not acquired through learning. They do not come from a mother’s knee, or from school or church; they are instead the products of biological evolution” (p. 8).
Of course, Bloom goes on to emphasize that social environments and experiences influence whether this foundation for morality is one day translated into actual moral behavior. This is an issue that merits greater attention in future research. As our comments in this chapter suggest, we see the intersection between morality and self-control over the life course as a promising issue to consider in this area of research.
Considering Self-Control as a Moderator Variable
In each of the discussions above, we have considered a causal arrangement in which self-control is the causal variable and some other factor (e.g., opportunity) plays a moderating role. In such an arrangement,
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the question is whether the effects of self-control are changed (in either an amplifying or diminishing way) when the moderating factor (e.g., high criminal opportunity) comes into play. Some studies, however, have considered a reversed arrangement in which the “other” factor is the causal variable and low self-control is the moderator. In this arrangement, the question is whether the presence of low self-control alters the effects of other variables. This type of arrangement is also instructive to consider because, regardless of whether low self-control is the causal or moderating variable, such research gives insight on the basic question of how low self-control works with other variables to affect involvement in criminal and antisocial behavior.
One commonly studied research question in this regard involves legal deterrence variables. A long line of criminological theory—dating back to the 18th- and 19th-century writings of philosophers like Cesar Beccaria and Jeremy Bentham—argues that criminal involvement is strongly influenced by people’s perceptions of whether their criminal acts would provoke legal punishments. If punishments are fairly certain to occur and are relatively severe, then crime is avoided. The research testing this argument has a long and complicated history, one that often suggests that legal punishments do not affect behavior as directly as we would like. That said, on balance, there is evidence that a person’s perceptions of the certainty and severity of legal punishments have at least a modest deterrent effect on crime.
Some scholars have considered this issue in conjunction with self-control. One possibility is that low self- control diminishes the effects of legal punishments. Those with low self-control may be least influenced (or deterred) by the threat of punishment because they fail to consider consequences—legal or otherwise—of their behavior. Those with low self-control simply act—contemplation about the certainty and severity of legal consequences will not much matter. In an early study assessing this possibility, Nagin and Paternoster (1994) found evidence that tended to support this stance. In particular, they found that the negative influence of perceived informal sanctions on intentions to steal and engage in assaultive behavior was diminished among study participants who had the lowest levels of self-control.
While similar conclusions have been reached in other studies (e.g., Piquero & Pogarsky, 2002; Pogarsky, 2002), Wright, Caspi, Moffitt, and Paternoster (2004) point out that these studies have largely been based on college samples and examined intentions to commit crime (rather than real criminal acts). In their analyses of long-term data from a birth cohort in New Zealand, Wright and his colleagues (2004) examined the interactive effect between sanction risk and low self-control when predicting actual reports of criminal behavior. Contrary to prior work, they found that the deterrent effect of perceived legal punishments was amplified among those with the lowest self-control—when self-control was high, legal punishments did not matter. This finding points to the possibility that those with the highest self-control often build strong conventional ties in the areas of education, family, and work. These factors introduce strong inhibitions, therefore making the deterrent effect of legal sanctions irrelevant. Those with low self-control, on the other hand, are quite open to crime, and they therefore may be more responsive to perceptions of legal punishments. Pogarsky (2007) reached a similar conclusion, and together these studies indicate that those with low self-control do in fact think about the future risks of crime. Their perceptions may not always be accurate, and they have a tendency to discount the value of future (rather than present) risks and rewards (Piquero, Paternoster, Pogarsky, & Loughran, 2011). Nevertheless, they do form perceptions about the likelihood of legal punishments, and because they perceive fewer other obstacles to crime, these perceptions may be quite consequential. Thus, low self-control may amplify the effects of legal punishments on crime, although there still is much to learn on that possibility.
Robert Agnew’s (1992, 2001) influential general strain theory provides another line of research that considers low self-control as a moderating factor. This theory argues that crime and aggression often are a response to the strainful aversive experiences that people face. Such things as harsh parental discipline, criminal victimization, discrimination, and hassles at work or school lead to negative emotions like anger and frustration that prime the individual for some sort of corrective action. Sometimes this involves retaliation against those responsible for their strain; in other instances, aggression is directed toward others as the individual’s anger creates a general openness to aggressive, combative, and rebellious behavior. Either way, strainful events and relationships should increase crime and aggression, and this prediction has been supported in much research (Botchkovar & Broidy, 2013).
Agnew has emphasized, however, that not all strained individuals will resort to crime and aggression—other factors come into play to moderate the relationship between strainful experiences and involvement in crime.
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Low self-control is expected to be one such factor. Specifically, Agnew contends that low self-control should amplify the association between strain and delinquency—those with low self-control will be more likely to respond to strain in impulsive, shortsighted, and emotional ways that involve greater antisocial and aggressive behavior. Those with high self-control, on the other hand, are more likely to deliberate on their situation and pursue a prosocial, constructive response to strain. Some studies suggest that self-control plays exactly this moderating role. Agnew, Brezina, Wright, and Cullen (2002), for example, studied strainful circumstances among a sample of early adolescents, finding that strain in family, school, and neighborhood contexts increased crime. Importantly, these effects were especially high among those with low self-control and essentially zero among those with very high self-control. Hay and Meldrum (2010) reached a similar conclusion in examining the effects of being a bullying victim on thinking about suicide and engaging in self- harming behavior (cutting or burning oneself). Bullying victimization and its harmful effects have received significant attention in recent years. There is good reason to expect, however, that the harmful effects of bullying are conditional upon other factors, and self-control could easily be one such factor. Hay and Meldrum’s analyses of data from a sample of middle and high school students supported that possibility, revealing that when self-control was high, the effects of being bullied on self-harm and suicidal ideation were lessened, often by as much as 35% to 40%. This was the case for those exposed to traditional, face-to-face bullying, but also for those exposed to newer forms of cyberbullying in which the Internet or cell phones are used to ostracize others. This is consistent with the idea that self-control helps promote thoughtful, long-term adaptations to strainful circumstances.
Can Self-Control Moderate the Effects of Self-Control?
Most approaches to self-control treat it as a durable long-term trait, but recall from earlier discussions that it can also be seen as a temporary state that fluctuates from one situation to the next or across short spans of time. These different views of self-control have sometimes been seen as contradictory and opposing, but the two may both capture interesting aspects of the self-control puzzle. At any given time, a person can be thought of as having two dimensions of self-control: a trait level that involves a long-term, somewhat stable trajectory, and a state level that follows from the immediate circumstances in a given situation or short span of time. Of course, trait and state levels of self-control should often be the same—those with high trait self-control often will find themselves in high-self-control states, and vice versa. However, as the preceding chapter indicates, self-control states can diverge from trait levels of self-control in response to different experiences. And this raises an interesting possibility: Perhaps there is a statistical interaction between trait and state levels of self-control, such that the effects on behavior of one dimension depend on those of the other.
A number of studies have considered that possibility (e.g., DeWall, Baumeister, Stillman, & Gailliot, 2007; Dvorak & Simons, 2009; Gailliot, Schmeichel, & Baumeister, 2006; Muraven, Collins, Shiffman, & Paty, 2005). They often measure trait self-control with the standard measures, such as the Brief Self-Control Scale (Tangney, Baumeister, & Boone, 2004) discussed in Chapter 3. Shifts in self-control states, on the other hand, are measured in terms of exposure to circumstances expected to use up one’s short-term self-control resources (thus producing a low-self-control state). Muraven and his colleagues (2005) considered this in studying young adults who were followed for three weeks. On a daily basis, using Palm Pilots provided to them, they reported how much they felt stressed or overwhelmed by that day’s events and experiences. These feelings were conceptualized as depleting one’s state self-control, given that stressful circumstances require one to use up self-control resources. As expected, the researchers found that on days in which subjects felt greater stress, they were more likely to drink to excess. Importantly, however, the magnitude of this effect depended on trait self-control—this effect of stress on alcohol consumption was roughly twice as large for those with low trait self-control, leading Muraven and his colleagues (2005, p. 145) to conclude that “individuals high in trait self-control may have a larger pool of resources at their disposal and therefore are less affected by self-control demands than individuals lower in trait self-control.”
DeWall and his colleagues (2007) also considered this issue in a clever laboratory experiment with a sample of undergraduate students. At the start of the study, all participants were asked to read through a page from a psychology textbook and cross out all instances of the letter e. This simple task required little effort or self- control, but as the study progressed, half the participants were randomly assigned to a more difficult task. They were asked to cross out the e in all instances except when it was immediately followed by a vowel or
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was in a word that had a vowel appearing two letters before the e. Not surprisingly, this task was perceived as more tedious and burdening, and in line with that, it was conceptualized as depleting self-control states (relative to the task of crossing out every e).
The researchers then had all participants read a scenario in which they were asked to envision themselves in a bar with a romantic partner whom they were “absolutely in love with.” As the scenario continues, another person enters the situation and starts “eyeing up” and flirting with the romantic partner (who seems to be enjoying the attention). The aggrieved subject confronts the unwanted visitor, and the situation escalates to the point of potential violence, at which point the subject spots a beer bottle on the bar counter and considers smashing it over the other’s head. DeWall and his colleagues found that, on average, those subjects who faced the more difficult e-crossing task were significantly more willing to smash the beer bottle over the unwanted visitor’s head. This supports the basic view that circumstances that deplete our self-control resources (and therefore produce low-self-control states) increase the likelihood of aggression in a given situation. Interestingly, however, the researchers found that this average effect obscured differences across the sample. Specifically, among those with high trait self-control, the difficult e-crossing task had no effect on their willingness to smash the beer bottle over the interloper’s head. However, for those low in trait self- control, there was a quite pronounced effect—those with the more difficult e-crossing task were substantially more willing to resort to violence in this situation.
Taken together, these studies suggest an interesting interaction between trait self-control and one’s exposure to experiences that trigger low-self-control states. Specifically, high trait self-control seems to act as a buffer against the complications that often arise in response to the daily demands, stressors, and hassles that life throws at us. Those individuals with high self-control certainly may be aware of these demands, stressors, and hassles, but they are better able to cope with them to limit their harm. Conversely, those with low trait self-control are more likely to respond with antisocial or destructive behavior that includes drinking to excess or acting aggressively. This affirms a basic theme of this chapter: Self-control is quite important, but this importance often follows not just from its powerful independent effects on behavior, but also from how it interacts with other critical circumstances, experiences, and qualities.
Policy Implications and Possibilities
This chapter has pointed to protective factors that diminish the effects of low self-control on crime, deviance, and harmful behavior. This raises an interesting policy possibility: the harmful consequences of low self- control can be reduced without actually affecting an individual’s level of self-control. Instead, we must simply design policies that promote the protective factors that diminish its effects.
Of course, in reality, this would never be our first policy option—the most efficient and effective policy approach will involve preventing self-control deficits from ever occurring in the first place; and short of that, we would like to reverse such deficits once they do emerge. Those two mechanisms—prevention and reversal—have been prioritized in our earlier discussions of policy implications, but they will not always be possible. The justice and social service systems will always find themselves dealing with disadvantaged children whose lives have gotten off to a genuinely rough start. For many children, by the time these systems get involved, the window for accomplishing true prevention has already passed, and reversal may be difficult also—as we have noted, once a low-self-control trajectory is under way, it often persists.
Thus, although reversal efforts should be continued whenever possible (because unexpected changes can occur), these circumstances call for an alternative policy approach, one that invokes the mechanism of suppression. The goal with suppression efforts is to introduce protective factors that reduce the harmful consequences of low self-control. Even if these protective factors cannot eliminate the self-control deficit, they may strip it of its potency—the self-control deficit persists, but it no longer translates into actual acts of crime and deviance. Thus, policy efforts might focus on improvements in the key moderating factors we earlier identified: criminal opportunities, association with delinquent peers, social bonds, the neighborhood context, and moral commitments.
Of these, the most likely target for suppression efforts involves the prospect of limiting criminal opportunities. We earlier described the compelling research showing that low self-control leads to less crime and deviance
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under circumstances of low opportunity. Additionally, efforts to address the other moderating factors—including peer associations, social bonds to conventional others, and moral values—are not likely to be inspired by efforts to merely suppress the harmful consequences of low self-control. Instead, such efforts are more rehabilitative in nature, and therefore likely are focused on trying to reverse any self-control deficits that exist. Opportunity, on the other hand, is notably compatible with the idea of suppression—an emphasis on limiting criminal opportunities necessarily highlights the idea that the internal motivations and inclinations of offenders often cannot be manipulated, but their bad behavior can be reduced if we restrict their access to situations in which the inclinations are easily acted upon.
So, if we were to restrict criminal opportunities, how might this be done? With adolescents, this might be accomplished with adolescence-specific parent-training programs like Functional Family Therapy (FFT; Gordon, Graves, & Arbuthnot, 1995) that are touted for improving levels of parental supervision and monitoring. When adolescents are out of school, it is critical for parents to know where they are, who they are with, and what they are doing. However, this requires a certain amount of parenting skill, given that parental supervision and monitoring rely a great deal on adolescents’ willingness to provide information to their parents (Kerr, Stattin, & Burk, 2010). Proven programs like FFT impart the needed skills. In the process, perhaps they will reverse self-control deficits by improving family functioning; however, even if major improvements in self- control do not occur, benefits may still accrue from greater parental supervision and the resulting reduction in opportunities.
Another proven program is Big Brothers Big Sisters of America. This program has a rich history in the United States—it has been pairing at-risk youths with volunteer mentors from the community for more than 100 years. It started in New York in 1904 when Earnest Coulter, the clerk for the New York Children’s Court, solicited promises from 39 volunteers to befriend at least one at-risk youth.2 Jumping ahead to the present, the program now is active in all 50 states. Mentors are matched with children and adolescents on the basis of shared goals and interests, and the pair spends roughly three to five hours per week together for at least 12 months, but often longer. Rigorous randomized controlled studies have yielded impressive results on such things as drug and alcohol use and violence (Grossman & Tierney, 1998). At least some of this effect may be occurring as a result of suppression—the greater time spent with mentors may help suppress the harmful effects of key risk factors, including low self-control.3
Formal criminal justice efforts that physically prevent opportunities for crime represent another potential source of suppression. This approach will be more common for adults and older adolescents who are an immediate threat to the community. Even with these cases, however, there should be a key cost–benefit balancing act in play. Any formal sanction involving institutionalization is quite expensive, and the rate of recidivism for such sanctions is quite high, with some evidence suggesting that the harsh conditions of institutionalization leave some offenders worse off than they were before. Nevertheless, for serious violent offenders, incarceration may often be needed to physically prevent opportunities for crime. Incarceration clearly does not eliminate the self-control deficit (it might make it worse), but it physically prevents the individual from committing acts of crime against the general population. For less serious offenders, however, other criminal and juvenile justice options are preferred. Evaluations indicate that electronic monitoring programs that use GPS technology to supervise offenders’ whereabouts without having to incarcerate them can reduce criminal opportunities (Killias, Gilliéron, Kissling, & Villettaz, 2010; Padgett, Bales, & Blomberg, 2006). Importantly, however, policymakers must be mindful of avoiding net-widening dynamics (Blomberg, 1980) that extend criminal justice sanctions to offenders who might be better dealt with outside the justice system. Another promising approach is case management, which involves assigning a social or mental health worker to an at-risk individual to coordinate continued services and supervision (Enos & Southern, 1996; Healey, 1999). Programs of this kind are especially effective (and cost efficient) when dealing with mentally ill or developmentally disabled adults who enter the criminal justice system (Healey, 1999).
One final approach merits emphasis. In each of the policy possibilities described above, suppression efforts have focused specifically on individuals with low self-control and considered how their specific circumstances might be altered. An alternative approach involves focusing on the broader community in which the individual lives. This follows from the research indicating that the most serious problems with crime, deviance, and harmful behaviors tend to be geographically clustered in a small number of communities. If such communities can be changed for the better, there will be, among other things, fewer situations in which low-self-control
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individuals in the community can easily translate their low self-control into actual criminal and deviant acts. One successful community-wide intervention is the Communities That Care (CTC) program developed by the Social Development Research Group at the University of Washington (Hawkins, Catalano, & Arthur, 2002; Monahan, Hawkins, & Abbott, 2013). CTC provides a structure for bringing together community stakeholders to envision the community they want to build and put in place the programs needed to turn that vision into reality. This occurs in CTC training events delivered over the course of 6 to 12 months by certified CTC trainers. The communities decide on the priorities, and the CTC trainers provide the implementation expertise to target the risk and protective factors that a community prioritizes and the research expertise to evaluate progress. Randomized controlled studies find that CTC communities have a lower incidence of crime, substance use, and key risk factors; moreover, as a result of their long-term experiences with the CTC program, these communities are much more likely than non-CTC communities to take an evidence-based scientific approach to solving problems in their neighborhood.
Low self-control increases behavior problems, but the extent of that increase almost certainly varies. This is a critical complicating issue to consider. It suggests that we cannot speak of low self-control purely in terms of its average effect. Instead, we must recognize that effects vary in systematic ways, with self-control operating in conjunction with other important factors to affect the probability of crime, deviance, and harmful behavior. The research highlighted in this chapter suggests that the differences in problem behavior between low- and high-self-control individuals are especially likely to be amplified when there is a high supply of criminal opportunities, frequent association with delinquent peers, weak social bonding with conventional others, residence in a disadvantaged neighborhood, and weak moral commitment. Alternatively, the consequences of low self-control are diminished when it is coupled with the converse of these factors (i.e., low opportunity, association with prosocial peers, strong conventional bonding, an advantaged neighborhood context, and strong moral values).
We caution readers, however, to keep in mind the “strong environments” dynamic that at times comes into play. Specifically, if the amplifying circumstances just noted ever produce pervading norms and values that are sufficiently powerful, they may diminish rather than amplify the effects of individual qualities like self- control. This occurs if group norms and influences—such as those from highly delinquent peer groups—are powerful to the point that they trample individual tendencies. In such instances, the “push” toward crime is strong enough to make it common among those who are low or high in self-control. The point to emphasize is that this possibility of moderating effects truly is a complicating factor—it greatly limits our ability to make simple generalizations about the average effects of low self-control.
This chapter also highlighted a novel possibility in which state and trait components of self-control statistically interact to affect the likelihood of crime and deviance. Specifically, high trait self-control (one’s long-term enduring pattern of self-control) seems to act as a buffer against the daily demands, stressors, and hassles that may lower state self-control (the level of self-control in a specific instance or situation). The specific pattern is one in which stressful circumstances or hassles are more likely to lead to criminal, deviant, and harmful behaviors among those with low trait self-control. Those with high trait self-control, on the other hand, are better able to cope with stress in ways that limit its harm.
And last, this chapter emphasized key policy implications of these findings. Most notably, efforts to reduce crime, deviance, and harmful behaviors sometimes may rely on the mechanism of suppression—when self- control deficits can neither be prevented nor reversed, the final policy alternative involves suppressing their harmful effects. Suppression efforts introduce protective factors that diminish the harmful consequences of low self-control. Our discussion focused especially on efforts to reduce the harmful effects of low self-control by decreasing criminal opportunities, something that can be done with a wide variety of social service and criminal justice programs and policies. The most notable include mentoring programs with juveniles (like the Big Brothers Big Sisters program) and formal criminal justice interventions with adults (like electronic monitoring and case management).
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• 1. What is the difference between an amplifying and a diminishing effect? Can you think of examples not discussed in the chapter in which the influence of one variable on another is amplified or diminished by some other factor?
• 2. Write out a list of all the instances where you conceivably could have engaged in criminal behavior over the past week. Based on what was discussed in the chapter, in what ways might these opportunities be more or less attractive to individuals who have more or less self-control?
• 3. Why might living in an economically disadvantaged community amplify the effect of low self-control on delinquent behavior? In other words, what physical, social, and/or cultural factors in neighborhoods could make it easier for low self-control to lead to delinquency?
• 4. Do you think people who are low in self-control are more likely or less likely to be deterred by the threat of formal (arrest and incarceration) and informal (loss of prestige, ending of relationships) sanctions for criminal behavior?
• 5. Think of an experiment on your own in which you deliberately try to deplete the state self-control of study participants to see if they will subsequently engage in antisocial behavior. What is the task that you will randomly assign to your participants, and what will be your measure of antisocial behavior? How might the impact of depleting the state self-control of your study participants on their engagement in antisocial behavior be moderated by their trait self-control?
1. For those readers especially interested in the theoretical nuances, we must elaborate on Gottfredson and Hirschi’s positions. In later writings (Gottfredson & Hirschi, 2003; Hirschi & Gottfredson, 1993), they downplayed the importance of criminal opportunity as a moderator of self-control’s effects. In doing so, they still maintained that criminal opportunities are necessary for low self-control to produce a criminal act, but they insisted that opportunities are abundant to the point that they have little causal significance. The simplest daily events—walking into a store, passing by a house, visiting a bar—provide ample opportunities for crime. The ubiquity of opportunity means that it does not matter much because those with low self-control can easily create their own opportunities or spot those that develop. Thus, what really matters is self-control, and this is Gottfredson and Hirschi’s steady position—self-control is always the factor that drives the process. Everything else—including the presence or detection of opportunities—follows from the person’s level of self-control. It bears emphasizing, however, that these arguments have not been supported. Individual differences in the frequency of criminal opportunities (measured subjectively or objectively) have been observed in various studies, and these differences have effects on behavior that are independent of the effects of self-control (Grasmick et al., 1993; Hay & Forrest, 2008; Longshore, 1998).
2. This history is described well on the program’s website (www.bbbs.org).
3. After-school programs that occupy early adolescents in the hours after school also can be helpful in limiting criminal opportunities. However, such programs have not been found to consistently reduce crime. It is useful to consider the ways in which after-school programs may differ from mentoring. A chief benefit of mentoring may be the interpersonal bonds that it offers. Thus, it enhances supervision in a context that also encourages prosocial attachments and commitments. Gottfredson, Gottfredson, and Weisman (2001) suggest that after- school programs that encourage prosocial attachments and commitments may be the most effective type of after-school program.
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