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Cohort analysis is a technique designed to examine ‘the changes in patterns of behaviour or attitudes’ of one or more cohorts, and especially birth cohorts (Uslaner, in Glenn 1977: 5). Developed by demographers, cohort analysis has been widely used to investigate changes across the life course. Now more broadly used in the social sciences, cohort analysis has evolved to examine any form of social, cultural or political change. Such an approach has allowed for cohort analysis to adopt a period analysis perspective, whereby ‘processes of social change are related to the successive replacement of generations [or cohorts] where each generation grows older in an ever changing historical context’ (Hagenaars 1990: 315). In short, cohort analysis refers to a longitudinal method for examining the cumulative experience of a cohort. In contrast to panel studies, however, these cohorts are not regarded as ‘closed’ (i.e. lose or gain no members, or containing the self-same members as they age). Rather, cohorts are regarded as ‘open’, with cohort analysis merely showing

change in the level of the issue of interest experienced by the cohort as it has aged, irrespective of which individuals were actually involved.

The foundation of cohort analysis is to draw ‘simultaneous synchronic and diachronic’ comparisons (Glenn 1977: 9-10). Three independent variables and one dependent variable are employed in cohort analysis. The independent variables are age, period, and cohort (Glenn 1977: 11, 13; Hagenaars 1990: 317-20); it could also be argued, however, that cohort effects are a dependent variable because they are a combination of age and period effects (as shown in the age-period-cohort nexus in Chapter 3). The dependent variable is the issue of interest – in this thesis, criminal offences operationalised as apprehension ratios – which is examined for one or more cohorts at several points in time. To facilitate such observation, cross-sectional data by age are reassembled longitudinally in a table matrix (a ‘Lexis Diagram’), by 'ageing' the cohort appropriately for each observation. This involves organising data around identical intervals on three measures: the number of years making up each birth cohort, the number of years making up each age group, and the time interval between each observation of the dependent variable. For example, if a cohort is defined as having been born over a four year period, its age-specific apprehension ratios will similarly need to be considered for four year age groups and observed at four year intervals. The age-specific apprehension ratios of a cohort born between 1969 and 1972 then may be observed at age 22-25 years in 1994, 26-29 years in 1998, and 30-33 years in 2002.

The three effects associated with cohort analysis (age, period, and cohort) have already been raised in this thesis (by way of Greenberg’s variant thesis concerning the nature of the age-crime pattern in Chapter 2 for example). Most simply, age effects refer to biological, social and legal manifestations (and the like); period effects refer to historical circumstances; and cohort effects reflect (by and large) the combination of cohort size, age effects, and period effects (shown in the age-period- cohort nexus in Chapter 3).

Age effects refer to the age at which cohorts (and the individuals within them) do certain things such as enter or leave school, marry, have children, commit crime.

They may manifest as changes in the dependent variable (apprehension ratios, for example) as the cohort ages. This is a useful means of examining whether a cohort has experienced a decline in its apprehension ratios as it has aged (as the ‘traditional’ relationship between age and crime would suggest). In comparing cohort-specific observations of the dependent variable over time (or as the cohort ages), any variation in the dependent variable can be tentatively labeled as an age effect. However, the meaning of age in cohort analysis can take on numerous forms. Such interpretations of age may include the duration of time between birth of cohort members and the point in time at which they are observed, or more advanced interpretations such as sociological perspectives of age (including age-dependent social roles, status and participation) (Hagenaars 1990: 317).

Period effects refer to changes in the dependent variable that can be attributed to an influence(s) occurring at a specific point in time, either at the time of observation or at some point lying between two observations. For example, if all cohorts experience an increase in their apprehension ratio between the same points in time (say between 1998 and 2002), this may be reflecting a trend arising from the criminal justice system (a change in policing practices or reporting levels perhaps).

Cohort effects refer to changes in the dependent variable that can be attributed to the combination of age and period effects, that is, cohort-specific influences. These effects may relate to influences that cohort members were exposed to over their lives as a direct result of being born when they were. For example, the classic expression of the Easterlin hypothesis (1987a) suggests that the relative size of the cohort may impact on its life chances, such as employment and housing. If only one or two cohorts in the analysis are seen to experience an unexpected rise or fall in the dependent variable over time (for example, if only one birth cohort experiences an increase in its apprehension ratio as it has aged), then this can be tentatively regarded as a cohort effect.

It is important to acknowledge that cohort analysis cannot be regarded as a definitive analytical approach. Although each of these three effects (age, period, and cohort) is more strongly associated with one particular independent variable, they are not

restricted to the influence of any single independent variable (Glenn 1977: 13-17). Rather, underlying each comparison of observations is always the confounding relationship between two of the three independent variables (which are likely to have multiple definitions and/or functions in themselves, as alluded to by the age-period- cohort studies in Chapter 4). This interception of effects was referred to with regard to period effects underlying potential cohort effects. Hagenaars (1990: 327) refers to this confounding of age, cohort and period effects as a perfect linear relationship, a relationship emerging in numerous forms in both quantitative and qualitative research. Similarly, Glenn (1977: 13) explains the relationship as:

Age [being] a perfect function of cohort membership and period of time, cohort membership [being] a perfect function of age and period, and period [being] a perfect function of age and cohort membership.

Methodologically, therefore, cohort analysis creates an ‘identification problem’ (Hagenaars 1990: 327).1 On this basis, ‘a strictly statistical solution to the age- cohort-period problem is not possible ... [whereby cohort analysis] shows effects but gives no evidence on the influences which produce those effects’ (Glenn 1977: 14- 15), the number of these potential influences in relation to crime being, according to Gottfredson and Hirschi (1990: 226), ‘unlimited’. Both Glenn (1977:19) and Hagenaars (1990: 358) concede that it is essential that the findings from cohort analysis be informed by associated theory and evidence, and complemented by further analysis (such as cross-sectional or standardisation analysis) to develop a more comprehensive portrait of the effects of ageing. To this end, this thesis draws upon the variance/invariance debate concerning the relationship between age and crime, the classic expression of the Easterlin hypothesis, and unemployment rates experienced by birth cohorts when they would first have been seeking entry to the workforce to. In addition, investigation of the age structure-crime pattern is furthered by comparative analyses (discussed in the next section) to examine the impact of age composition effects.

1

For further discussion of the ‘identification problem’, see Fienberg and Mason (1985), Heckman and Robb (1985), Jagodzinski (1984), Palmore (1978), Pullum (1977, 1980), and Rodgers (1982).

The analyses discussed in Chapter 4 indicated that ‘true’ cohort analysis has not been widely applied to examine the association between cohort density and crime. Similarly, the term ‘cohort’ has been incorrectly used in many criminological studies, which results in misleading answers (i.e. what has often been analysed is age groups rather than longitudinal birth cohorts). Furthermore, criminology has more generally applied cohort analysis to consider cohorts beyond those referring to birth, namely groups that have shared a similar experience within the criminal justice system such as persons institutionalised at a common facility, receiving a similar sentence either at or for the same period, or who have been exposed to an experimental stimulus (Maxfield and Babbie 2005: 194). Alternatively, the same individuals have been examined over time (as in panel studies). Examples of studies that have taken one of these approaches were provided in Chapter 4, and it is these approaches that have been most commonly applied in the Australian context. Hence, while this thesis adopts a more conventional approach to cohort analysis per se (by focusing on birth cohorts), it nonetheless represents a less traditional conceptual and analytical application of cohorts in the criminological sense (by not analysing age groups or cohorts linked by a common experience within the criminal justice system).

In this thesis, cohort analysis is used to investigate the cohort density expression of the age-structure crime pattern (in Chapters 8, 9, and 10). More specifically, whether the age-specific apprehension ratios of birth cohorts reflect the age-crime pattern; whether younger (larger) cohorts experienced higher ratios than older (smaller) cohorts; whether the cohort-specific apprehension trajectories reflect a decline in ratios as the cohort has aged; and, whether any transgressions from the age-crime pattern can be linked to period and cohort effects (namely, differences in birth cohort sizes and the associated effect that this has had on relative disadvantage). The process of cohort analysis is explained further in Appendix A (section A.1).