Demographers characterize populations according to a range of traits, from the traditional standards of age and sex to a range of other variables like geography, co-residence, life expectancy, parity, and sexual orientation. Occasionally, demographers work to formalize new, creative ways of characterizing populations, such as the death cohort (Riffe et al., 2017), birth expectancy (Baudisch & Alvarez, 2021), or meaning-based measures of inequality in mortality (Wrigley-Field, 2025).
A new way of thinking about population composition has emerged in the last few years, under the banner of “demographic memory.” This term expresses the idea that populations can be characterized according to their demographic ties to the past. Coining the term, Denton & Spencer (2021) think about how being a member of a birth cohort means that individuals have lived through and been influenced by a particular set of historical events — amounting to what I call “survivorship memory.” Then, extending the concept, Alburez-Gutierrez (2022) thinks about how being a member of a family means that individuals are tied to the events of the past through kin ties — amounting to what I call “kinship memory.”
In a new paper in Population and Development Review, I build on this work to clarify, expand, and further demonstrate the usefulness of these concepts. First, I point out that survivorship and kinship memory both map onto a large number of ideas already present in the social, health, and biological sciences. For example, immunity, life-course trauma, and various forms of political and environmental lived experience can all be modeled as survivorship memory, while (epi)genetic inheritance, intergenerational trauma, and the right to citizenship can all be modeled as kinship memory. However, demographers pay careful attention to how individual characteristics aggregate and change over time and are, therefore, in a position to formalize what these concepts have in common and to provide the tools for estimating and projecting their levels over time. Getting the basics of demography right when estimating the population prevalences of these conditions is essential. They are all shaped by the fundamentals of age, period, and cohort, as well as mortality, fertility, migration, and aging. When more complex factors get involved — like social stratification with respect to who experiences what, who remembers what, and who lives to tell the tale — careful demographic modeling becomes all the more important.
This article provides a highly flexible formal framework for estimating one particular way of operationalizing survivorship memory: the average remembered exposure to past conditions of interest. Building on previous frameworks, the model allows researchers to study the memory of events, eras, and continuously varying conditions, like pollution or economic development; specify flexible population structures; account for individual-level processes of forgetting; and account for nuanced processes of social stratification. Models like this are limited in the amount of data available to specify them, and they can be a poor substitute for qualitative analysis of concepts that are intrinsically different to quantify, like the idea of collective memory, i.e. how the past is socially constructed in populations. However, this new framework can be expected to perform well at answering a range of other questions that boil down to how populations absorb and let go of aspects of the past.
The article demonstrates the usefulness of this framework with three worked examples: estimating the average experience of global warming above pre-industrial levels in different countries; estimating the proportion of the lives of the UK population over time that has been spent under different female prime ministers; and estimating the difference between the current level of liberal demographic strength across different countries versus the average level of liberal demographic strength experienced during the lives of those countries’ residents. These diverse examples show the range of empirical questions that better models of demographic memory can try to help answer.
About the Author
Hampton Gaddy, London School of Economics
Further reading
Alburez-Gutierrez, D. (2022). The Demographic Drivers of Grief and Memory After Genocide in Guatemala. Demography, 59(3), 1173–1194. https://doi.org/10.1215/00703370-9975747
Baudisch, A., & Alvarez, J.-A. (2021). Born once, die once: Life table relationships for fertility. Demographic Research, 44, 49–66. https://doi.org/10.4054/DemRes.2021.44.2
Denton, F. T., & Spencer, B. G. (2021). In Living Memory: The Demographic Dynamics of Event Recollection in a Stable Population*. Population and Development Review, 47(1), 219–235. https://doi.org/10.1111/padr.12388
Riffe, T., Schöley, J., & Villavicencio, F. (2017). A unified framework of demographic time. Genus, 73(1), 7. https://doi.org/10.1186/s41118-017-0024-4
Wrigley-Field, E. (2025). Three Ways of Looking at Black–White Mortality Differences in the United States. Annual Review of Sociology, 51(1), 311–333. https://doi.org/10.1146/annurev-soc-031021-105213