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Weiss 1973, 46f. and Bocquet-Appel and Masset 1977 (see also Herrmann et al. 1990, 306f.) have devised indices which check if the non-adult age groups are represented in proportions as can be expected from modern comparable data. Whether this is really applicable to archaeological data-sets is a matter of debate.
Quite recently, Taylor and Oxenham 2024 added a comparison of Total fertility rates (TRF) according to different formulas which depend either on subadults or adults.
Weiss chose the mortality (qx) as deciding factor and claimed that (1) the probability of death of the age group 10–15 (5q10) should be lower than that of the group 15–20 (5q15) and that (2) the latter in turn should be lower than that of age group 0–5 (5q0).
In contrast, Bocquet-Appel and Masset took the raw number of dead (Dx) and asserted that (1) the ratio of those having died between 5 and 10 (5D5) to those having died between 10 and 15 (5D15) should be equal or larger than 2 and that (2) the ratio of those having died between 5 and 15 (10D5) and all adults (>= 20) should be 0.1 or larger.
The formualas Taylor and Oxenham used either weigh all individuals aged 0–14 against all individuals or all individuals aged 15–49 against all individuals aged 15+. The formulas differ from the original ones published by McFadden and Oxenham 2018 and Taylor et al. 2023 because the data basis is slighty different. If the results of the formulas deviate by more than 0.692 (the standard error of estimate, SEE), there is a problem with the age structure.
Due to the specific nature of the indices, they only give meaningful results if 5-year-age categories have been chosen for the non-adults.

Usage

lt.representativity(life_table)

Arguments

life_table

an object of class mortaar_life_table.

Value

data.frame showing the indices and explaining their interpretation.

References

herrmann_prahistorische_1990mortAAR

masset_bocquet_1977mortAAR

mcfadden_oxenham_2018a

weiss_demography_1973mortAAR

taylor_oxenham_2024mortAAR taylor_et_al_2023

Examples

schleswig <- life.table(schleswig_ma[c("a", "Dx")])
lt.representativity(schleswig)
#>     approach            condition value1 value2 result outcome
#> 1   weiss_i1           5q0 > 5q15  20.24   4.91   4.12    TRUE
#> 2   weiss_i2          5q10 < 5q15   6.86   4.91   1.40   FALSE
#> 3    child_i    (5D5 / 5D10) >= 2  22.00  12.00   1.83   FALSE
#> 4 juvenile_i (10D5 / D20+) >= 0.1  34.00 155.00   0.22    TRUE
#> 5        TFR       TFR_SA = TFR_A   4.83   6.99   2.16   FALSE