Reconstructing Population Dynamics of the Recent Past, with Uncertainty, from Fragmentary Data

Mark C. Wheldon, University of Washington
Adrian Raftery, University of Washington
Samuel J. Clark, University of Washington
Patrick Gerland, United Nations Population Division

Current methods for reconstructing human population structures of the past are deterministic or do not formally account for measurement error. We propose a method for simultaneously estimating age-specific population counts, fertility rates, mortality rates and net international migration flows from fragmentary data, that incorporates measurement error. Inference is based on joint posterior probability distributions which yield fully probabilistic interval estimates. It is designed for the kind of data commonly collected in modern demographic surveys and censuses. Population dynamics over the period of reconstruction are modeled by embedding formal demographic accounting relationships in a Bayesian hierarchical model. Informative priors are specified for vital rates, migration rates, population counts at baseline, and the accuracies of their respective measurements. We investigate calibration of central posterior marginal probability intervals by simulation and demonstrate the method by reconstructing the female population of Burkina Faso from 1960 to 2000.