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Reconstructing Past Populations With Uncertainty From Fragmentary Data
Mark C Wheldon1, Adrian E Raftery, Samuel J Clark
1Mark C. Wheldon is Ph.D. Candidate, Department of Statistics, University of Washington, Seattle, WA 98195 (E-mail: mwheldon@uw.edu ).
Journal of the American Statistical Association
|April 13, 2013
Summary
This study introduces a novel Bayesian method to reconstruct past human populations, accounting for data errors. The new approach provides probabilistic estimates for population counts, fertility, mortality, and migration, improving demographic reconstruction accuracy.
Area of Science:
- Demography
- Statistical Modeling
- Population Studies
Background:
- Traditional demographic reconstruction methods are often deterministic and fail to account for measurement error.
- Fragmentary data from surveys and censuses present challenges for accurate population reconstruction.
Purpose of the Study:
- To develop a probabilistic method for reconstructing past human populations by age and sex.
- To simultaneously estimate age-specific population counts, fertility rates, mortality rates, and net international migration flows.
- To incorporate measurement error formally into the demographic reconstruction process.
Main Methods:
- A Bayesian hierarchical model is employed, embedding formal demographic accounting relationships.
- Joint posterior probability distributions are used for inference, yielding probabilistic interval estimates.
- The method utilizes informative priors for vital rates, migration rates, baseline population counts, and measurement error variances.
Main Results:
- The proposed method successfully reconstructs demographic parameters, including age-specific population counts, fertility, mortality, and migration.
- Probabilistic interval estimates are generated, offering a measure of uncertainty in the reconstruction.
- The method was demonstrated by reconstructing the female population of Burkina Faso from 1960 to 2005.
Conclusions:
- The developed Bayesian method offers a robust approach to human population reconstruction from fragmentary data.
- Accounting for measurement error enhances the accuracy and reliability of demographic estimates.
- The R package "popReconstruct" facilitates the application of this method in demographic research.
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