Abstract
As contemporary model tables, or mathematical models based on current conditions are not the best tools for describing pre-transitional human behaviour, characterized by very high rates of fertility and child mortality, specific models must be developed for preindustrial populations. These models must be based on a large corpus of tables that are statistically representative of mortality among pre-industrial populations, to propose inputs easily accessible from historical or osteological sources and to take account of the growth rate, either positive or negative, of the population.
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Notes
- 1.
Whether mortality reduction precedes or follows fertility decline is still subject to discussion. In some cases, it appears that they have been simultaneous or even reversed (Coale and Watkins 1986).
- 2.
- 3.
Anthropologically speaking, maturity begins at around age 18 (see above). Demographically, we could have taken that threshold if we had been working with 1-year age groups. Given the unreliability of pre-statistical data, we have preferred to work with 5-year age groups, and set the transition point between immature and mature according to the age groups adopted (15–19 and 20–24 years).
- 4.
This work was aided by Stéphane Renard, for data entry, and, at various stages of the statistical study, by Magali Belaigues-Rossard, Luc di Benedetto, Paul Bermier, Bertrand Buffière, Annie Carré, Nadège Couvert, Benoît Haudidier and Carole Perraut, on short-term contracts at INED. Our thanks for all their help. We also wish to warmly acknowledge the work of Arnaud Bringé who oversaw and guided all the stages in this lengthy research project.
- 5.
The “both sexes” sample comprises 292 life tables, the “male” sample 290 and the “female” sample 286.
- 6.
Two main methods were used: random k-means clustering and automatic table classification. These discriminant analyses were based on a series of indicators specially defined to characterise the “oldest” life tables: discriminating variables chosen to measure changes in mortality structure: \( \frac{{}_1{q}_0}{{}_4{q}_1} \), \( \frac{{}_5{q}_5}{{}_5{q}_{10}} \), \( \frac{{}_{20}{q}_{20}}{{}_{20}{q}_{40}} \), \( \frac{{}_1{q}_0}{e_{20}} \) and \( \frac{e_{5-15}}{e_{20}} \); discriminating variables chosen to distinguish changes in level: e 5–15 and e 20–40; juvenility index \( \left(\frac{D_{5-14}}{D_{20-\omega }}\right) \) and the ratio \( \frac{D_{5-9}}{D_{10-14}} \) (where D = number of deceased in the table); variables chosen to distinguish differential mortality by sex: 1 q 10, e 20, e 20–40 and e 40–60.
- 7.
The sample comprises historical tables from industrialised countries (seventeenth to nineteenth centuries) and contemporary tables from developing countries (end-nineteenth to twentieth centuries), all selected according to the level of the demographic indicators cited above. No sub-group stands out: the average level of mortality in the developing countries is significantly very close to that observed in the historical tables of the industrialised countries (Student’s T-test: T = 0.023, significance level: 0.05) except for three probability values (4 q 1, 5 q 5, 5 q 65). The same holds for mortality structure: no significant difference emerges between the two samples, except for three probability values (4 q 1, 5 q 5, 5 q 65).
- 8.
Not all Coale and Demeny’s source tables have been published, so they could not be included in this comparison.
- 9.
Method for calculating this variable in the tables: [(22.5 × d 20–24) + (27.5 × d 25–29) + (32.5 × d 30–34) + (37.5 × d 35–39) + (42.5 × d 40–44) + (47.5 × d 45–49) + (52.5 × d 50–54) + (57.5 × d 60–64) + (62.5 × d 65–69) + (67.5 × d 70–74) + (72.5 × d 75–79) + (77.5 × d 80–84) + (88 × d 80–ω)]/d 20–ω
Method for calculating this variable for the osteological series: see Chap. 6.
- 10.
Method for calculating this variable in the tables: [(7.5 × d 5–9) + (12.5 × d 10–14)]/d 5–14
Method for calculating this variable for the osteological series: see Chap. 6.
- 11.
However, the growth rates of the populations for which we have life tables were not always specified in the publications. By convention, we have considered that their growth rates were very low, and have constructed our stable populations on that assumption.
- 12.
In this way we obtain a network of 24 stable populations associated with each table in our sample (namely 167 × 24 = 4,008 tables for the “both sexes” sample; 147 × 24 = 3,528 tables for our “male” sample and 139 × 4 = 3,336 tables for the “female” sample).
- 13.
Under the old demographic regime, long-term population growth was hindered not only by high mortality (particularly among infants and children) but also by frequent mortality crises (see above). In such situations, growth rates of 2.5 % or 3 %, as seen in contemporary observations (Pison 2007) are hardly plausible in the long term. For example, Bocquet-Appel and Masset (1977) proposed ten growth rates (plus and minus 2 %; 1 %; 0.5 %; 0.2 %; 0.1 %); Henneberg and Steyn (1994) proposed growth rates in increments of 0.5 from −1 % to +5 %; and Bocquet-Appel and Masset (1996) used growth rates from −2.5 % to +2.5 % by increments of 0.25.
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Séguy, I., Buchet, L. (2013). Finding the Right Models for Pre-industrial Populations. In: Handbook of Palaeodemography. INED Population Studies, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-319-01553-8_7
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