Social indicators and demographic rates are often arrayed over time by age. The patterns of rates by age at one point in time may not reflect the effects associated with aging, which are more properly studied in cohorts. Cohort succession, aging, and period-specific historical events provide accounts of social and demographic change. Because cohort membership can be defined by age at a particular period, the statistical partitioning of age from period and cohort effects focuses attention on identifying restrictions. When applying statistical models to social data, identification issues are ubiquitous, so some of the debates that vexed the formative literature on age–period–cohort models can now be understood in a larger context. Four new articles on age–period–cohort modeling call attention to the multilevel nature of the problem and draw on advances in methods including nonparametric smoothing, fixed and random effects, and identification in structural or causal models.
Key Words: APC models • cohorts • identification strategies