Bayesian Reconstruction of Past Populations and Vital Rates by Age for Developing and Developed Countries

We show that Bayesian population reconstruction, a recent method for estimating past populations by age, works in a very wide variety of data quality contexts. To date, it has been shown to work only in a single case (i.e., Burkina Faso). Bayesian reconstruction simultaneously estimates age-specific population counts, fertility rates, mortality rates and net international migration flows from fragmentary data while formally accounting for measurement error. As inputs, Bayesian reconstruction takes initial bias-reduced estimates of age-specific population counts, fertility rates, survival proportions and net international migration. Here, we show that the method performs well when applied in a range of data quality contexts by reconstructing the female populations of Laos, a country with little vital registration data where population estimation depends largely on surveys, Sri Lanka, a country with some vital registration data, and New Zealand, a country with a highly developed statistical system and high-quality vital registration data. In addition, we extend the method to apply to countries without censuses at regular intervals. We also develop a method for using it to assess the consistency between model life tables and available census data, and hence to compare different model life table systems.