Age and Crime Research Paper

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Outline

I. Introduction

II. Hirschi and Gottfredson’s “Age and the Explanation of Crime”

III. Methodological Implications of the Invariance Argument

A. Age, Crime, and Criminal Careers

B. Longitudinal Research in Criminology

IV. Theoretical Implications of the Age–Crime Relationship

V. The Age–Crime Relationship in Traditional Criminological Theory

A. Propensity Versus Developmental Theories

B. Variation in the Causes of Crime by Age

VI. Practical and Policy Implications of the Age–Crime Relationship

A. Targeting Participation Versus Frequency

B. Forecasting Crime Rates

VII. Conclusion

I. Introduction

The curvilinear relationship between age and crime is one of the most consistent findings in criminology, and it has been referred to as a “resilient empirical regularity” (Brame & Piquero, 2003, p. 107) and “one of the brute facts of criminology” (Hirschi & Gottfredson, 1983, p. 552). Social statisticians as early as Quetelet in the 1800s (Steffensmeier, Allan, Harer, & Streifel, 1989) identified a strong relationship between age and crime that has come to be known as the age–crime curve. The general form of the relationship between age and crime is not much debated. In aggregate studies, the age–crime curve is unimodal, with official crime rates rising in adolescence to a peak in the late teenage years and then declining rapidly through adulthood. It is also apparent that the age–crime curve peaks somewhat later for violent crimes as compared with property crimes. Although much research examining the age–crime relationship has relied on official data and age-specific arrest rates (Marvell &Moody, 1991),Moffitt (1993) noted that the general curvilinear pattern also holds true more generally for conduct disorder, antisocial behavior, and childhood aggression. Farrington (1986) and Hirschi and Gottfredson (1983) have commented that although scholars agree on the general form of the age– crime curve, there is less agreement on its meaning and implications.

II. Hirschi and Gottfredson’s “Age and the Explanation of Crime”

Many scholars have pointed to Hirschi and Gottfredson’s (1983) seminal article, “Age and the Explanation of Crime,” as the beginning of serious debate surrounding the relationship between age and crime. This debate centers around a number of factors, both methodological and theoretical. Specifically, Hirschi and Gottfredson set forth a number of basic perspectives on the relationship between age and crime. First, and perhaps most important, these authors argued that the age–crime curve is invariant across a wide variety of social and cultural factors, including time, place, individuals, and types of crime. Although they recognized that there may be differences in levels of offending among groups (e.g., males and females), they dismissed this variation in favor of the conclusion that the general form of the curve is the same. This invariance argument has profound implications for methodological, theoretical, and practical considerations in criminology and has provoked intense debate among criminologists that are visited in more detail later in this research paper.

Hirschi and Gottfredson (1983) also addressed theoretical attempts to contend with the age–crime curve, arguing that theories should not be obligated to try to explain this relationship and should not be rejected solely because of their inability to do so. The authors further contended that no existing criminological theories are capable of explaining the age–crime curve. In the absence of strong theoretical explanations, Hirschi and Gottfredson suggested that age has a direct effect on crime and on other social factors proposed to explain crime. An apparent relationship between marriage and reduced offending is spurious, because age causes both; in other words, this relationship appears only because individuals get married and begin to age out of crime at the same time. Finally, Hirschi and Gottfredson argued that conceptualizing the age–crime relationship in terms of a criminal career is unnecessary and potentially misleading, especially because the causes of crime are the same at all ages throughout life.

The arguments Hirschi and Gottfredson (1983) put forth in their article have spurred a great deal of debate in the field and have far-reaching implications for criminological research, theory, and policy. Tittle and Grasmick (1998), for example, argued that Hirschi and Gottfredson’s perspective presents a major challenge to many current directions in criminology, including the criminal careers perspective, longitudinal research, developmental theories, and social theories in general. Subsequent sections of this research paper explore the methodological, theoretical, and policy implications of these various arguments about the age– crime curve.

III. Methodological Implications of the Invariance Argument

Steffensmeier and colleagues (Steffensmeier, Allan, Harer, & Streifel, 1989) noted disagreement about the strength and consistency of the relationship between age and crime. One of the main methodological points of argument stems from Hirschi and Gottfredson’s (1983) assertion that the age–crime curve is invariant across time, place, individual characteristics, offense type, and so on. Many criminologists have addressed this argument and contend that the claim of invariance is overstated. In summarizing the debate, Tittle and Grasmick (1998) found evidence of invariance only when considering the general mathematical form of the curve; in other words, the aggregate age– crime curve looks similar across different places, times, types of individuals, and offense types. All curves share the same general unimodal pattern of rising rates to a peak in late adolescence and declining rates through adulthood. However, Tittle and Grasmick (1998) commented that the claims of parametric and individualistic invariance have been conclusively rejected.

In terms of parametric invariance, studies examining the specific properties of the age–crime curve (i.e., median, mean, skewness, and kurtosis) have found variations (see Farrington, 1986, and Tittle & Grasmick, 1998). For example, using both the Uniform Crime Reports and self-report data, Steffensmeier and colleagues (1989) found that whereas the general shape of the aggregate curves are similar, specific parameters of the curve do vary. They noted in particular that, over time, the peak of the age–crime curve has shifted to younger ages and the curve has become steeper. In his study of the specific components of the curve, Farrington (1986) also concluded that the age–crime curve is not invariant. Although Gottfredson and Hirschi (1986) dismissed these variations as unimportant, others find them to be substantively interesting and worth attention (Blumstein, Cohen, & Farrington, 1988).

The claim of invariance has also been rejected when the age–crime curve is considered at the individual level. Tittle and Grasmick (1998) noted many individual deviations from the modal age–crime curve. Blumstein and colleagues (1988) argued that because the aggregate age– crime curve is capturing prevalence (e.g., the proportion of the population of a given age that engages in crime), it only appears to be invariant. When one looks at the relationship between age and crime at the individual level, however, one can see a great deal of variation. Farrington (1986) also cited individual variation in offending trajectories, which have been replicated in more recent research (Nagin & Land, 1993).

This highlights an important methodological debate in terms of whether the relationship between age and crime is due to prevalence (i.e., participation rates) or incidence (i.e., individual patterns of the frequency of offending). Differences in prevalence or participation rates of offending imply that the general shape of the age–crime curve appears because involvement in offending varies by age group. In other words, a larger proportion of the adolescent population participates in offending, while the proportion of the population involved in offending declines for older age groups. A consideration of incidence or frequency of offending, on the other hand, suggests that the age–crime curve is an aggregate representation of individual differences in the number of crimes committed at various ages. In other words, individuals commit a larger number of offenses during their adolescent years and reduce the frequency of their offending as they age. It is now generally agreed that the relationship apparent in the aggregate age– crime curve is due to prevalence (Farrington, 1986; Moffitt, 1993). Beginning with the work of Nagin and Land (1993), studies have continued to demonstrate variation in trajectories of the frequency of offending. Hirschi and Gottfredson (1983) also agreed, and they further argued that differences in prevalence (i.e., distinguishing accurately between offenders and nonoffenders) are the most important consideration in criminology.

A. Age, Crime, and Criminal Careers

Other methodological debates surrounding the relationship between age and crime largely stem from the issue of the invariance of the age–crime curve. The claim of individualistic invariance has been rejected, and some researchers, recognizing that there are individual variations in the age–crime relationship, have considered offending in the context of a criminal career. Blumstein et al. (1988) referred to the criminal career as “the longitudinal sequence of offenses committed by an offender who has a detectable rate of offending during some period” (p. 2). The criminal careers perspective looks at the relationship between age and crime at the individual level and addresses such components of a career as onset, persistence, and desistance. Onset refers to the initiation of criminal behavior, and some researchers have focused on age of onset as an important element of the criminal career. In particular, research has examined whether individuals who initiate their offending early in life are more likely to become long-term or high-rate offenders. Persistence refers to the continuation or duration of an offending career, and desistance refers to the termination of that career. Although Blumstein et al. argued that there is no reason to expect any particular pattern or tendency within criminal careers, they suggested that the inquiry as to the presence of certain patterns (e.g., escalation in seriousness, specialization in particular types of crime, etc.) is open to empirical investigation.

The criminal careers perspective does not present any particular theoretical model; instead, it is a methodological and empirical strategy that separately considers participation in offending from the frequency of offending among active offenders and allows for the possibility that various theoretical perspectives may be important in explaining different components of the criminal career. Gottfredson and Hirschi (1986), on the other hand, contended that the appropriate comparison for criminology is between offenders and nonoffenders (i.e., prevalence or participation) and that theories that are capable of distinguishing these two groups are adequate without needing to explain different components of a career. For Gottfredson and Hirschi, questions of the incidence or frequency of offending are irrelevant to the understanding of criminal behavior, and the causes of crime are the same regardless of age or criminal career component.

B. Longitudinal Research in Criminology

The age–crime curve and the criminal career perspective both imply long-term processes at work. For a variety of reasons, longitudinal studies, which involve repeated measurement over time, have emerged as preferable to cross-sectional studies (i.e., measurement at one point in time) in the study of criminal careers. Blumstein and colleagues (1988) argued that understanding dynamic patterns of offending, whether one is looking for variation or stability, virtually requires longitudinal data, and they pointed to the inadequacies of cross-sectional data in studying criminal careers. Steffensmeier et al. (1989) likewise argued that there are many social factors that vary by age and that may provide an explanation for the shape of the age–crime curve. Greenberg (1985) pointed out that the impact of dynamic factors will be underestimated in cross-sectional analyses depending on the stability or instability of the variable over time. The distinction between longitudinal and cross-sectional data may be most important when causal ordering is unclear. For example, whereas social control theories propose that weakened social bonds will lead to criminal behavior, it is also plausible that involvement in offending may weaken social bonds (Greenberg, 1985). By measuring social factors and criminal behavior at various points in time, longitudinal studies are better able to ensure the appropriate temporal ordering necessary to demonstrate causation instead of just correlation.

Another important consideration in choosing between cross-sectional and longitudinal research was raised by Farrington (1986), who argued that cross-sectional research easily confuses period, cohort, and age effects. Period effects refers to the impact of living in a particular historical period. Regardless of age, individuals who live through particular periods (e.g., World War II) may experience the same events or social conditions. Cohort effects may be more easily confused with age effects in that individuals in the same cohort (e.g., born in the same year) may be exposed to similar life experiences (e.g., the Baby Boomer generation). In contrast, the term aging effects refers to those social conditions that may vary by age (e.g., maturational reform, changes in peer networks or social bonds, etc.) and would affect individuals regardless of cohort or period. Farrington argued for the necessity of using multicohort longitudinal studies to truly distinguish these period, cohort, and age effects.

Gottfredson and Hirschi (1986, 1987), however, have taken issue with many of these arguments. Hirschi and Gottfredson (1983) argued that if the age–crime curve is the same for everyone, then no special techniques are necessary to understand this relationship. They argued that good cross-sectional studies (e.g., true experiments) are capable of answering the same questions as longitudinal designs, especially considering that the timing of crime and social events is not ambiguous. Because temporal ordering should not be a major problem when studying criminal behavior, according to this perspective one major argument in favor of longitudinal designs is rejected. They also discounted the need to disentangle age, period, and cohort effects, because “crime cannot cause age, period, or cohort” (Gottfredson & Hirschi, 1987, p. 588), and they argued that the attention paid to these issues has distracted criminology from more substantive, policy-relevant concerns.

Gottfredson and Hirschi (1987) also contended that the correlates of crime uncovered by longitudinal research are the same as those reported by cross-sectional research, concluding that longitudinal studies have merely confirmed results from cross-sectional studies. They cautioned that longitudinal studies are far more expensive, inefficient, and time consuming than cross-sectional studies, providing no added value to the study of crime. Other researchers have pointed to additional difficulties of longitudinal research, including the possible confusion of testing and maturation or aging effects and the high levels of attrition (i.e., dropping out) of high-rate chronic offenders from longitudinal studies over time (Brame & Piquero, 2003).

This debate is far from settled. Gottfredson and Hirschi (1987) contended that the invariance of the age–crime curve means that nothing of value has been learned from longitudinal studies and that it is more important to distinguish offenders from nonoffenders, regardless of age. On the other hand, researchers who are interested in the incidence of offending (i.e., frequency) and criminal careers argue that the curve varies a great deal at the individual level and requires longitudinal data to truly understand the patterns. Sampson and Laub (1995), for example, suggested that although differences between offenders are important, differences within individuals over time are just as important to understand. Scholars, especially those in the criminal careers or developmental/life course traditions, are increasingly turning to individual-level, longitudinal designs (Brame & Piquero, 2003). This research continues to find evidence of varying criminal career patterns and to explore whether different social factors may account for these different patterns (Nagin & Land, 1993; Steffensmeier et al., 1989).

IV. Theoretical Implications of the Age–Crime Relationship

Debate over the age–crime curve also has significant implications for criminological theory. Hirschi and Gottfredson (1983) claimed that the age–crime curve is invariant, that the causes of crime are the same at all ages, and that no existing social theory is capable of explaining the curve. Traditional criminological theories, such as differential association and social control, have tended to focus on explaining crime during the adolescent period, which represents the peak of the age–crime curve. Although this is to be expected, given that the bulk of delinquent and criminal activity occurs during these ages, Greenberg (1985) argued that crime does not just level off following the transition to adulthood; instead, it consistently declines, which suggests the need for theoretical attention to the entire life span and to the decline and desistance from offending in addition to onset.

Hirschi and Gottfredson (1983) noted that traditional theories have often been judged by their ability to explain the patterns apparent in the age–crime curve. For example, theories are criticized as being able to explain the onset of criminality, leading to the peak of offending, but not desistance. The failure to explain all aspects of the age–crime curve is often taken as a fatal flaw for theories. Hirschi and Gottfredson argued, however, that a theory that adequately distinguishes offenders from nonoffenders at a particular age (e.g., adolescence) may not necessarily account for the aging-out effect. Because aging out and desistance from crime occur consistently for all groups, the failure to explain desistance should not be used to discount a theory, especially considering that no existing theory, in their opinion, is capable of providing an adequate explanation.

Hirschi and Gottfredson (1983) also argued that, in the absence of a sufficient theoretical explanation, the remaining conclusion is that age has a direct effect on crime independent of other social factors and incapable of being explained by any existing social theories. This would seem to imply some sort of biological explanation, and they referred to a process of maturational reform, which occurs pervasively for all offenders, as an explanation of desistance. Farrington (1986) also suggested that maturational reform reflects some biological forces, noting age-related variation in physical strength and skills. Again, according to Gottfredson and Hirschi (1986), because the age–crime curve is invariant, and because aging out of crime occurs similarly for everyone, attempts to explain these patterns with social forces, which are assumed to vary, is futile. These authors ultimately concluded that the correlation between various social factors and crime is spurious, calling into question all existing criminological theories.

V. The Age–Crime Relationship in Traditional Criminological Theory

Despite the critique leveled by Hirschi and Gottfredson (1983), the major theoretical traditions in criminology (i.e., strain, social control, and social learning theories) have all been used to provide explanations for variation in criminal behavior over the life span. For example, strain theory argues that adolescents and young adults experience more status frustration and strain, which eases with entry into adulthood and legitimate employment. The relative deprivation experienced by youth declines with entry into the legitimate adult labor market (Greenberg, 1985). Theorists have also incorporated some elements of strain theory when considering Easterlin’s (1978) perspective on relative cohort size. Easterlin argued that larger cohorts (e.g., the Baby Boomers) face certain disadvantages, such as competition for scarce resources, that result in higher levels of economic deprivation for that cohort. Although Easterlin highlighted the negative economic conditions consistent with a strain perspective, he also suggested that large cohorts may overwhelm social institutions, subjecting cohort members to additional criminogenic social conditions, such as reduced supervision, weakened socialization, and lower levels of social control. These conditions prove to be most detrimental for adolescents and young adults and may account for increasing crime rates when these large cohorts enter the most crime-prone years (i.e., late adolescence and early adulthood).

As Easterlin (1978) and Greenberg (1985) suggested, social control theory may also provide an argument for the changing of crime rates by age. Sampson and Laub (1995) pointed out that the impact of both formal and informal social controls varies by age. This theory argues that social bonds are weakened during adolescence, freeing an individual to violate social norms. Thus, adolescence represents a time when attachments to conventional others, especially parents, and commitment to conventional institutions are reduced. Social bonds may be re-formed in adulthood as individuals accumulate conventional ties to jobs and begin to build their own families through marriage and parenthood. In addition, the consequences of crime become more serious with age and function as more of a control on behavior as individuals amass a greater stake in conformity (Steffensmeier et al., 1989).

Differential association would anticipate that increasing involvement in crime during the adolescent years is due to variation in experiences with delinquent peers (Warr, 1993). In support of this perspective, Warr (1993) used data from the National Youth Survey to demonstrate age-related changes in exposure to delinquent peers, including the percentage of friends who are delinquent, time spent with peers, and the self-reported importance of peers, that correspond to the age–crime curve. During later periods of adolescence individuals in the National Youth Survey reported a larger number of delinquent friends, more time spent with those friends, and more importance of peers in their lives. In multivariate models, the relationship between age and crime was attenuated when peer variables were included. Thus, Warr’s research suggests that the age– crime relationship may be at least partially explained by changes in peer associations.

Stolzenberg and D’Alessio (2008) more recently examined the implications of peer association in a different way, addressing whether changes in co-offending account for age-related variations in criminal involvement. Researchers have consistently noted that criminal behavior during adolescence is largely a group phenomenon. This pattern of co-offending may explain the increasing prevalence rates during adolescence that are apparent in the aggregate age– crime curve. Stolzenberg and D’Alessio put forth the following argument:

The greater prevalence of co-offending among juveniles, engendered to a large extent from the influence of criminally inclined peers, in turn explains why crime levels peak during adolescence and then begin to decline in early adulthood following graduation from high school. (p. 69)

If this perspective is true, the age–crime relationship should be most apparent when one is considering crimes involving co-offending, but it should disappear when co-offending is taken into account. In other words, the age– crime curve for solo offending should be flat, whereas the curve for co-offending should demonstrate the typical curvilinear pattern. Using National Incident Based Reporting Systemdata, however, Stolzenberg and D’Alessio (2008) found two interesting results. First, contrary to much of the discussion surrounding adolescent offending, co-offenses are not the most common pattern; instead, solo offending is more common for all age groups, including juveniles. Also, the age–crime curve emerges for both solo and co-offending, suggesting that accounting for co-offending does not attenuate the typical age–crime relationship. Thus, patterns of co-offending do not appear to account for the age–crime curve.

Marvell and Moody (1991) argued that although there is no shortage of speculation about the causes of the age– crime curve, there is little empirical support for any of these explanations, concluding that there is no firm theoretical foundation for the age–crime curve. Tittle and Grasmick (1998) examined a variety of age-varying criminogenic factors but found that including these factors did not seem to account for the age–crime relationship. They reported an inability to discount Hirschi and Gottfredson’s (1983) theoretical arguments and concluded that it is not easy to explain away the age–crime curve.

A. Propensity Versus Developmental Theories

More recent criminological theories have attempted to explain the curve itself as well as to understand changes in levels of crime over the course of the age–crime curve. Two strategies of accounting for variation across the life course are apparent in criminological theory: (a) propensity theories and (b) developmental or life course theories. Propensity theories point to a single underlying stable trait that causes crime at all ages. The most well-known and most frequently tested propensity theory in criminology is Gottfredson and Hirschi’s (1990) general theory of crime, which suggests that crime and other risky behaviors at all ages are the result of an individual’s low level of self-control. Using a theoretically stable trait to account for obvious age-related variation in criminal offending patterns may seem counterintuitive. Gottfredson and Hirschi contended that low self-control produces criminal behavior in the presence of criminogenic opportunities. Thus, opportunities for crime may vary across the life course even though levels of self-control are relatively stable. Hirschi and Gottfredson (1995) suggested that variation in the opportunity for crime by age accounts for a great deal of the variation in actual criminal activity observed.

In concert with their earlier assertion that the relationship between age-varying social factors and offending is spurious because of age (Hirschi & Gottfredson, 1983), propensity theory argues that any relationship between social factors (e.g., deviant peer associations and weak social bonds) and offending is spurious because of self-control. In other words, deviant peer associations and weakened social bonds are related to offending because they are all caused by the underlying factor of self-control. Individuals with low levels of self-control are also more likely to associate with deviant peers and have difficulty forging and maintaining the conventional connections that foster strong social bonds. Thus, they continue to argue that criminological theories attributing changes in offending over time to changes in social factors are inadequate.

In contrast to propensity theories, developmental or life course theories of offending point to age-related variations in criminogenic factors to explicitly account for the age– crime relationship. Both developmental and life course theories look to the full life course in their explanations of offending. Moffitt’s (1993) developmental theory, for example, starts with the conclusion, based on empirical research, that the age–crime curve represents differences in prevalence by age, with a larger proportion of the adolescent population engaging in delinquent or criminal activity. She also argued that the aggregate age–crime curve masks group differences in the relationship between age and crime. In other words, she noted that individual variation in the frequency of offending by age is hidden within the aggregate age–crime curve.

Moffitt (1993) proposed a typological perspective that identifies two separate groups of offenders, each with a different age–crime curve. Thus, the aggregate age–crime curve is a mix of a small group of long-term offenders (referred to as the life-course-persistent offenders), which has a relatively flat and stable age–crime curve, and a larger group of individuals with a short-term period of delinquent involvement occurring during adolescence (referred to as adolescent-limited offenders), which demonstrates the typical age–crime curve with a large peak during late adolescence. With two different offending patterns, these two groups require different etiological explanations. According to Moffitt, life-course-persistent offenders become involved in criminal behavior early in life and persist in their criminal activity because of the combination of neuropsychological deficits, inadequate parenting, and cumulative disadvantage associated with the negative consequences of early criminal involvement. Adolescent-limited offenders, on the other hand, engage in offending for a relatively short duration. Entry into offending is explained by a maturity gap, in which youth may be biologically mature but remain dependent on and under the control of their families. Minor offending occurs in an attempt to gain some independence and as a result of the imitation of antisocial models. Desistence in this group occurs in early adulthood as social bonds increase and the consequences of criminal activity become more punitive.

Life course theories of offending similarly point to long-term patterns of offending and social forces that operate over the full life course. Sampson and Laub’s (1993) age-graded theory of social control highlights the processes of both continuity and change in behavior over the life course, looking at both differences between individuals and differences within individuals over time. Entry into delinquent behavior is accounted for by a variety of social factors, including weak social bonds to family and school in childhood and adolescence. Desistance from delinquency occurs with the accumulation of social bonds, namely, strong marriages, stable employment, and other stabilizing influences, in the transition to adulthood. Persistence (i.e., continuity), on the other hand, occurs as a result of the cumulative disadvantage of early criminal involvement. Sampson and Laub (1995) contended that criminal behavior further attenuates already-weakened social bonds by limiting opportunities within conventional society. Some scholars have argued that the focus on social bonding is too narrow and that many life events may function to alter peer association more in line with a learning perspective instead of a social control perspective (Stolzenberg & D’Alessio, 2008). Sampson and Laub (1995) contended that this does not directly contradict their theory, and the most recent version of the theory (Laub & Sampson, 2003) has expanded to accommodate social bonding, peer, and routine activity influences that change over the life course. For example, marriage may strengthen social bonds as well as attenuate preexisting deviant peer associations and restructure routine activities and criminal opportunities. These life events, then, account for the peak of offending during late adolescence and the dramatic decline in offending that occurs shortly after the transition to adulthood.

B. Variation in the Causes of Crime by Age

One question remaining from traditional and life course/developmental theories is whether the causes of crime are the same regardless of age. As might be expected, Hirschi and Gottfredson (1983; Gottfredson & Hirschi, 1990) have argued that the causes of crime are the same at all ages; in other words, social factors do not interact with age to produce criminal behavior. Other theorists suggest that the causes of offending may vary by age. For example, Moffitt (1993) pointed to a variety of theoretical factors that may influence offending at different ages, including early neuropsychological deficits and parenting influences, negative peer associations in adolescence, and social control mechanisms in later adolescence and early adulthood. Tittle and Grasmick (1998) examined the interaction thesis and found no evidence that age interacts with criminogenic forces to produce criminal behavior. Again, they had difficulty discounting Hirschi and Gottfredson’s (1983) assertions; however, this issue remains open for debate and empirical investigation.

VI. Practical and Policy Implications of the Age–Crime Relationship

The debate surrounding the relationship between age and crime has also highlighted some practical and policy implications. Existing criminal justice policies have often been assessed in relation to the implications of the age– crime curve. For example, strategies such as the three strikes law (according to which courts are required to hand down a mandatory incarceration sentence to offenders who have been convicted of felonies three or more times) have been criticized in that, by the time the penalty for a third strike is implemented, the offender is likely at the end of his or her criminal career and would age out of criminal involvement regardless of the severity of the penalty. Other issues arise with regard to the appropriate crime reduction strategies implied by the age–crime curve and forecasting future trends in crime rates.

A. Targeting Participation Versus Frequency

The distinction between participation and frequency highlighted in the criminal careers debate proves to be an important consideration for crime policy. Hirschi and Gottfredson (1986) argued that programs targeted at reducing participation rates (i.e., reducing the proportion of the population that is engaging in criminal behavior) will have the largest effect on crime rates. They advocated for early intervention programs in particular. Farrington (1986) likewise suggested that, because the aggregate age–crime curve represents differences in participation, the best strategy to reduce crime is to prevent its onset by investing in early intervention programs. Blumstein and colleagues (1988), however, suggested that this is only one approach to decreasing crime. A second approach would be to reduce the frequency of offending among active offenders, which would involve more criminal justice strategy. These authors argued that there is a small group of offenders with a high frequency of offending and a relatively flat, stable age–crime curve (e.g., “chronic offenders” or “career criminals,” recognized as early as 1972 by Wolfgang, Figlio, & Sellin). A strategy such as selective incapacitation, which is targeted at reducing the frequency of offending among these chronic offenders, might be recommended. Selective incapacitation, however, relies on the assumption that these chronic offenders can be reliably identified before they are involved in an extensive number of offenses, something that has proven to be a difficult prospect (Gottfredson & Hirschi, 1986). Blumstein and colleagues did not dispute the difficulties in identifying these career criminals but suggested that they remain a valid topic of criminological inquiry.

B. Forecasting Crime Rates

Age has also become a major factor in explaining changes in crime rates over time and in forecasting future crime trends. For whatever theoretical reason, scholars have concluded that the age–crime curve reflects changes in the prevalence of offending among certain age groups. It is logical, then, that changing numbers of adolescents and young adults in the population should produce corresponding changes in crime rates (Phillips, 2006). This provides the potential opportunity to forecast changes in crime trends based on the age distribution of the population (Marvell & Moody, 1991).

Some research has suggested that the dramatically increasing crime rates during the 1960s and 1970s were attributable, at least in part, to demographic changes in the age structure of the population. Steffensmeier and Harer (1999) also suggested that the decline in crime during the first half of the 1980s was partly due to the declining population of teenagers. During this time, the sizable population of Baby Boomers was moving out of the most crime-prone years (i.e., aging out of crime). However, Marvell and Moody (1991) noted that although forecasts suggested a massive decline during the 1980s as the Baby Boomers aged out, the decline occurred for only the first half of the decade. Crime rates then increased again to record highs in the early 1990s even as the size of the teenage population declined (Fox, 1996; Marvell & Moody, 1991). Despite this confusion and apparent complexity in using age to predict crime rates, the declining crime rates during the 1990s were again attributed largely to the declining population of young adults (Steffensmeier & Harer, 1999).

In 1999, Steffensmeier and Harer found that changes in the age composition of the population did not appear to account for changes in crime rates as measured by both the Uniform Crime Reports and the National Crime Victimization Survey. Phillips’s (2006) cross-national research also finds no real relationship between the size of the youth population and crime rates. Most important, she found that the relationship between the percentage of young people in the population and homicide is attenuated when other criminogenic social conditions (e.g., low social control, high economic deprivation) are present. She suggested that the relationship between age and crime is complex and that the exact nature of the relationship depends on other social and cultural conditions. Levitt (1999) also concluded that, although forecasting crime rates on the basis of the number of teenagers in the population may be a logical assertion, the magnitude of the impact of age structure on crime remains unclear. His research suggests that changes in the age structure of the population account for no more than a 1% change in crime rates per year.

Marvell and Moody (1991) argued that demographic changes should not be used to forecast crime trends, because the age–crime relationship may not be strong enough to base predictions on, and other criminogenic forces may be more important. Despite the apparent complexity of this relationship, scholars and the popular media continue to forecast trends in crime rates based on the age structure of the population. In 1996, for example, Fox pointed to a “demographic time bomb” of crime and violence related to the increasing population of adolescents and young adults expected through 2010. This echoed earlier suggestions that a new crime wave would be fueled by a new, large generation of “super-predators” (Steffensmeier & Harer, 1999). By 2005, the teenage population was expected to reach its largest size in three decades (Fox, 1996). However, crime rates declined substantially throughout the decade of the 1990s and remained low in the early 2000s (Steffensmeier & Harer, 1999). Marvell and Moody (1991) summarized this difficulty by concluding from their review of 90 studies that “the age/crime relationship is far from established” (p. 251), limiting its utility in predicting future crime trends based on the age distribution of the population.

VII. Conclusion

That the age–crime curve is a well-known and consistent correlate of crime often is taught as one of the major facts of crime in criminology courses. Yet the implications of the age–crime relationship for research methods, criminological theory, and practice remain a subject of debate. Largely prompted by Hirschi and Gottfredson’s (1983) strong assertions about the age–crime curve, scholars have continued to argue about its implications. Although Hirschi and Gottfredson argued that explanations accounting for the age–crime pattern are unnecessary, other scholars find various components of the criminal career to be relevant and fruitful avenues for research. Research in this tradition has increasingly turned to longitudinal designs, and theories specifically built around explaining the age–crime curve have become popular in recent years. The practical and policy implications of the curve have proved to be more difficult. The relationship between age and crime is complex, and researchers will likely continue to explore the various issues raised in this research paper.

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