Index

Handout for the Department Meeting 19990419


Paine, Richard R. and Henry C. Harpending (1998) Effect of sample bias on paleodemographic fertility estimates. Am. J. Phys. Anthropol., 105: 231-240.

Possible problems in demographic reconstruction from skeletal remains -> 4 causes of bias

  1. culture-based biases in mortuary behavior or in other behavioral factors such as warfare
  2. taphonomic processes
  3. archaeological recovery
  4. age estimation methods (to cause underestimation of older adults)
Object:
to what extent the biases mentioned above contribute to distorting fertility estimates?
Methodological basis:
Required condition of skeletal series: 1) actual demographic rates, 2) patter and extent of biases are known. Such samples are not available. -> computer simulated skeletal series.
Simulation:
Brass's logit system(1) -> spline interpolation -> the risk of a person age x dying before age x+1 as dx = (lx -lx+1)/(lx) -> expected number of death by age in stable population as e-rx(lx - lx+1) -> constructing cumulative risk of death by age -> generating random number in [0,1] for each individual to determine one's death age. -> 180 simulated skeletal samples of sizes 50, 100, 250 (the results were only given for the cases of 250).
Base condition:
the same life expectancy, expanding (r=0.01, CBR=31), stationary (r=0, CBR=23), rapidly declining (r=-.01, CBR=16). 60 simulations.
Bias inclusion:
1) age-at-death estimation as methodological bias; in 60 simulations, age at death of all individuals over 40 were altered as Table 1. -> Fig.2
2) infant underenumeration as archaeological bias; in 60 simulations, 50% of all children 4 years of age or younger were removed (there is no reliable basis about the value 50%)-> variation in total population -> Fig.3
Results and Discussion:
1) overestimation of fertility and birth rates due to age estimation bias increases with true CBR increasing; if the best-fitting model to the Sunwatch Village had CBR of 56 (Fig. 1), the actual CBR might be 40-45 ( 20%).
2) 50% infant underenumeration depresses both fertility and CBR by 20-25%. To utilize this result, adequate estimation about loss of infant underrecovery is necessary.
3) "Unbiased" estimates based on best-fit West model raised 4.72% lower CBR than true value, which reflects small-scale difference of mortality shape between two models.
4) Methodological bias may be controllable, but infant underenumeration is more serious problem because there is quite a few studies to quantify that, and one possible approach is integrated archaeological strategy, including estimation of population growth, population density, cultural settings, warfare, and epidemic diseases.

1. Y(a)=+Ys(a), q(a)=exp(2Y(a))/(1+exp(2Y(a)), where Ys(a) is the logit of age-specific mortality.