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Current Demographic Models

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Handbook of Palaeodemography

Part of the book series: INED Population Studies ((INPS,volume 2))

Abstract

The purpose of palaeodemographic analysis is to understand the population as it was, within a given socio-environmental context, where the individuals it comprised formed a dynamic group marked by births, deaths and sometimes migrations, and when all we have, at best, is the bones of those who died. This is no easy task and involves the use of population models and life tables or parametric models. The hypothesis required for such models are recalled and the main life tables actually used by demographers and palaeodemographers are reviewed.

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Notes

  1. 1.

    Some authors have suggested tracking changes in the age distribution of deaths in order to measure the growth rate (Valkovics 1982); others (Longacre 1976; Cohen 1977) have based their calculations on population estimates from various periods, but their results are not robust enough to serve as a grounding for palaeodemographic hypotheses.

    At present, most studies compare the distribution of ages at death obtained for a stationary population (r = 0) with that resulting from a non-zero growth rate, either chosen arbitrarily or estimated (see examples in Chap. 11).

  2. 2.

    For example, the role of causes of death in variations in mortality.

  3. 3.

    For a detailed presentation of the various mortality models, see Bourgeois-Pichat 1994, pp. 41–86.

  4. 4.

    The East family (Austria, Czechoslovakia, Germany, Hungary, Northern and Central Italy, Poland: 31 tables) is characterised by high infant mortality and high mortality at ages 50+. The North family (Iceland, Norway, Sweden: 9 tables) has low infant mortality and low mortality at ages 50+. The South family (Southern Italy and Sicily, Spain, Portugal: 22 tables) has high under-5 mortality, low mortality at ages 40–60 and high mortality at ages 65+. The West family (130 tables from the other 22 countries, mainly in Europe, plus Australia and New Zealand, Canada and the United States, Israel, Japan, Taiwan and the white population of South Africa) comes close to the general mortality model observed in the preliminary phase.

  5. 5.

    Life expectancy at birth: a general mortality index measuring the mean number of years a newborn would live if exposed throughout its life to the mortality conditions observed in its year of birth.

  6. 6.

    In the 1983 revision, Coale, Demeny and Vaughan took account of this objection and included some tables for Africa. In 1976, Samuel H. Preston had already proposed introducing a fifth “non-Western” pattern based on life tables from Latin America.

  7. 7.

    The OECD used a corpus of 104 life tables from developing countries, divided into four groups by mortality by age (the fifth region was an aggregate of the four). For each group the OECD also published a standard table. However, the poor quality of the input data limits the validity of these model tables.

  8. 8.

    The UN only used 36 tables from 22 developing countries in Latin America, Asia and Africa, also divided into four “geographical” regimes and a general mortality pattern. But what they gained in terms of quality they perhaps lost in terms of representativeness.

  9. 9.

    To our knowledge, the life models for developing countries (UN 1982; OECD 1980) have not been used in palaeodemography, either due to unawareness of their potential utility, or because researchers consider that the causes of death among developing country populations are too remote from the health conditions of pre-industrial populations.

  10. 10.

    Mortality by age displays very similar patterns for all mammals. This observation has made it possible to apply to human populations a mathematical model originally designed for primates (Siler 1983).

  11. 11.

    I.e. the individual ages estimated from osteological remains.

  12. 12.

    The idea is that fertility affects the number of births in a year and contributes to widening or narrowing the base of the population pyramid, gradually altering its profile over time. Conversely, mortality, which affects all ages in roughly the same way from 1 year to the next (except for demographic crises), has a more moderate effect on population structure.

  13. 13.

    Only non-selective epidemics, such as plague, have little or no effect on the age-sex structure of the surviving population (Séguy et al. 2006b).

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Authors and Affiliations

  1. Unité Histoire et Population, INED, Paris, France

    Isabelle Séguy

  2. CEPAM, UMR 7264 (CNRS, UNS), Nice, France

    Luc Buchet

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Séguy, I., Buchet, L. (2013). Current Demographic Models. In: Handbook of Palaeodemography. INED Population Studies, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-319-01553-8_6

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